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Measurement System AnalysisFor Battery Production
Using DMAIC at Northvolt AB
Phuc Nguyen
Adam Sahlberg
Industrial and Management Engineering, master's level
2020
Luleå University of Technology
Department of Business Administration, Technology and Social Sciences
Measurement System Analysis
For Battery Production
Using DMAIC at Northvolt AB
Mätsystemsanalys
För batteriproduktion genom
förbättringsmetoden DMAIC på Northvolt AB
Master Thesis project in Quality Technology and Management at
Luleå University of Technology and Northvolt Labs in Västerås.
Examensarbete utfört inom ämnesområdet kvalitetsteknik vid
Luleå tekniska universitet och Northvolt Labs i Västerås
By Av
Phuc Nguyen
Adam Sahlberg
Västerås, 2020-06-04
Supervisors Handledare
Erik Lovén, Luleå University of Technology
Sreepal Reddy, Northvolt AB
Acknowledgement This Master Thesis project has been the final work of our Quality Technology and Management
master program and our five-year educational adventure at Luleå University of Technology. The
project was performed between January 20th and June 5th in 2020 on the mission of Northvolt at
Northvolt Labs in Västerås, Sweden.
It has been a great readjustment from the regular university studies which required us as students
to be more independent and creative. Despite an external threat in terms of the covid-19 pandemic,
the progress has been overall smooth and without any major obstacles. We are both proud and
happy over the outcome and consider this as a great introduction to the upcoming challenges in
the working life.
Lastly, there are many individuals we feel a strong gratitude towards. Without their unlimited
support, this Master Thesis would not have been possible. We would like to thank the entire
Quality team and the other Northvolters for continuous guidance and support along the way,
especially Lina for always being available and for trusting us with the Master Thesis task in the
first place. We would also like to thank our fellows Cong Fei, Ding Hui and Duan Chao for
relentlessly and tirelessly providing us with samples to measure. Also, a big thank you to our
classmates and friends Magnus and Maximilian for the co-operation and support during our time
in Västerås. We would also like to express our gratitude to our families for a place to stay, food
and a car to borrow, your daily support has been indispensable. Last but not least, we would like
to thank our supervisors Erik Lovén at Luleå University of Technology and Sreepal Reddy at
Northvolt for valuable advices, comments and support along the way.
Västerås, June 2020.
Phuc Nguyen Adam Sahlberg
Abstract As battery manufacturing is enclosed with multiple quality and safety requirements, the battery
industry needs adequate Measurement Systems (MS) to provide high product quality and ensure a
safe working environment. The study purpose was to improve the performance of an MS for
battery production by utilizing MSA and Six Sigma methodology, and to make appropriate
recommendations for improvement and future control. The study included 28 measurement
instruments which were evaluated by the utilization of a framework consisting of five different
errors identified in the literature, namely bias, linearity, stability, reproducibility and repeatability.
This framework is considered as the theoretical contribution of this study.
The improvement methodology DMAIC (Define-Measure-Analyze-Improve-Control) was used to
perform the case study. The results indicate an overall improved MS and generated improvement
suggestions of three recurrent scenarios that arose in the analysis. Moreover, a company adopted
control plan with an intention to serve as a basis for future work within MSA is presented and
concerns the practical contribution of this work. The results provide helpful support as well as
establish a foundation of how to maintain a well-performing MS for Northvolt. By implementing
the suggested recommendations, the potential saving was estimated to 395 000 SEK annually.
Sammanfattning En mätsystemanalys genomfördes hos batteritillverkaren Northvolt. Då batteriproduktion
omgärdas av flera kvalitets- och säkerhetskrav behöver batteriindustrin tillförlitliga mätsystem för
att generera hög produktkvalitet samt upprätthålla en säkerhet för användare. Studien syftade till
att förbättra prestandan hos ett mätsystem inom batteriproduktion genom användandet av
mätsystemanalys och Sex Sigma-metodik, samt att ge lämpliga rekommendationer för
förbättringar och framtida kontroll. Studien inkluderade 28 mätinstrument som utvärderades
genom användningen av ett ramverk bestående av fem olika mätsystemfel identifierade i
litteraturen, nämligen bias, linearity, stability, reproducibility och repeatability. Detta ramverk
betraktas som det teoretiska bidraget från denna studie
Förbättringmetodiken DMAIC (Define-Measure-Analyze-Improve-Control) användes för att
utföra fallstudien. Resultaten visar på ett övergripande förbättrat mätsystem och genererade
förbättringsförslag på tre återkommande scenarier som uppstod i analysen. Dessutom presenteras
en företagsanpassad kontrollplan med avsikt att utgöra en grund för framtida arbete inom
mätsystemanalys och ses det praktiska bidraget från denna studie. Resultaten förblir ett användbart
stöd samt skapar en grund för hur Northvolt upprätthåller ett högpresterande mätsystem. Genom
att säkerställa prestandan av mätsystemet uppskattades den potentiella besparingen till 395 000
SEK årligen.
Table of Content 1 Introduction .................................................................................................................................. 1
1.1 Background ...................................................................................................................... 1
1.2 Case Study Background ................................................................................................... 2
1.3 Problem Discussion and Purpose ..................................................................................... 3
1.4 Delimitations .................................................................................................................... 4
1.5 The Logical Disposition ................................................................................................... 5
2 Methodology ................................................................................................................................ 6
2.1 Research Approach .......................................................................................................... 6
2.2 Research Methodology ..................................................................................................... 6
2.2.1 Define ........................................................................................................................ 7
2.2.2 Measure ..................................................................................................................... 7
2.2.3 Analyze ..................................................................................................................... 8
2.2.4 Improve ..................................................................................................................... 8
2.2.5 Control ...................................................................................................................... 8
2.3 Knowledge Establishment ................................................................................................ 9
2.3.1 Literature Review...................................................................................................... 9
2.3.2 Interviews .................................................................................................................. 9
2.4. Creditability of Research Findings ................................................................................. 10
3 Theoretical Framework .............................................................................................................. 11
3.1 The Measurement System .............................................................................................. 11
3.1.1 The Concept of Precision and Accuracy ................................................................. 11
3.1.2 Measurement System Error ..................................................................................... 12
3.2 Measurement System Analysis ...................................................................................... 13
3.2.1 Accuracy ................................................................................................................. 14
3.2.2 Precision .................................................................................................................. 17
3.3 Measurement System Analysis of Attribute Data .......................................................... 19
3.4 Cost of quality ................................................................................................................ 20
3.5 MSA and Decision-making ............................................................................................ 21
3.6 Measurement System Analysis in ISO 9001:2015 ......................................................... 22
4 Case Study ................................................................................................................................. 23
4.1 Define ............................................................................................................................. 23
4.1.1 Project charter ......................................................................................................... 23
4.1.2 Process overview .................................................................................................... 24
4.1.3 Potential savings ..................................................................................................... 30
4.2 Measure .......................................................................................................................... 32
4.2.1 Thickness Measurements ........................................................................................ 33
4.2.2 Weight Measurements ............................................................................................ 33
4.2.3 Dimension Measurements ....................................................................................... 34
4.2.4 HI Pot Test .............................................................................................................. 34
4.2.5 Angle Measurements .............................................................................................. 34
4.3 Analyze........................................................................................................................... 35
4.3.1 Analysis Strategy .................................................................................................... 35
4.3.2 Analysis of Measurement Readings........................................................................ 38
4.4 Improve .......................................................................................................................... 41
4.5 Control ............................................................................................................................ 45
5 Conclusion ................................................................................................................................. 50
6 Discussion .................................................................................................................................. 53
6.1 Validity and Reliability of Method ................................................................................ 53
6.2 Validity and Reliability of Data ..................................................................................... 54
6.3 Study Contribution ......................................................................................................... 55
6.4 Recommendations for future research............................................................................ 56
7 References .................................................................................................................................. 57
Appendix I – Complete Analysis Results ........................................................................... 33 pages
Appendix II – Analysis Using Minitab ................................................................................. 2 pages
Figures Figure 1: Northvolt Labs (Northvolt AB, 2019a) ........................................................................... 2
Figure 2: Prismatic cell (Northvolt, 2019b) .................................................................................... 4
Figure 3: Logical disposition of remaining chapters. ..................................................................... 5
Figure 4: The concept of precision and accuracy. ........................................................................ 12
Figure 5: The concept of MSA ..................................................................................................... 14
Figure 6: Visualization of Bias Error ............................................................................................ 15
Figure 7: Visualization of Linearity Error .................................................................................... 15
Figure 8: Visualization of Stability Error .................................................................................... 16
Figure 9: Visualization of Repeatability ....................................................................................... 17
Figure 10: Visualization of Reproducibility ................................................................................. 18
Figure 11: Calendering Process .................................................................................................... 25
Figure 12: Anode Jumbo Roll and Thickness of Electrode .......................................................... 25
Figure 13: Notching and Slitting process ...................................................................................... 26
Figure 14: Cathode Pancake ......................................................................................................... 26
Figure 15: Electrode Cutting and Stacking process ...................................................................... 27
Figure 16: Anode electrode dimensions ....................................................................................... 27
Figure 17: The Stacking procedure into a Jelly Roll. ................................................................... 28
Figure 18: Hot Pressing process ................................................................................................... 28
Figure 19: Jelly Roll thickness with pre-welded tab dimension ................................................... 28
Figure 20: Pre-welding process .................................................................................................... 29
Figure 21: Final welding process .................................................................................................. 29
Figure 22: Lid Angle and Film Wrapping Position ...................................................................... 29
Figure 23: Insulation film wrapping process ................................................................................ 30
Figure 24: Electrolyte Filling #1 & #2 .......................................................................................... 30
Figure 25: Type I Error rate .......................................................................................................... 31
Figure 26: Decision tree for the MSA........................................................................................... 49
Figure 27: MI improvement measured in %Error ......................................................................... 51
Figure 28: Linearity which change in variation ............................................................................ 56
Tables Table 1: Project Charter .................................................................................................................23
Table 2: Existing measurement instruments in Calendering and the Cell Assembly processes ....24
Table 3: Measurement equipment used to measure reference value. ............................................32
Table 4: Acceptance Criteria for Numerical Data .........................................................................36
Table 5: Acceptance Criteria for Attribute Data ............................................................................36
Table 6: Result of Analysis ............................................................................................................38
Table 7: Improvement Suggestions ...............................................................................................42
Table 8: Important understanding prior to MSA. ..........................................................................46
Table 9: Concrete sampling and analysis strategy for the MIs at Northvolt Labs. ........................47
List of Abbreviations
AAA Attribute Agreement Analysis
CCD Charge Coupled Device
CMM Coordinate Measuring Machine
DMAIC Define-Measure-Analyze-Improve-Control
IDMS Image Dimension Measurement System
ISO International Organization of Standardization
MI Measurement Instrument
MS Measurement System
MSA Measurement System Analysis
P/T Precision to Tolerance
SIPOC Supplier-Input-Process-Output-Customer
QC Quality Control
R&D Research & Development
R&R Repeatability & Reproducibility
SEK Swedish crowns
SNR Signal to Noise Ratio
SPC Statistical Process Control
SQ Study Question
1
1 Introduction This section introduces the subject of this Master Thesis. A description of the case, a discussion
of the problem and a purpose along with a short description of the case company are presented.
Delimitations and the logical disposition lastly illustrate the structure of this study.
1.1 Background By reducing variability in the production, the manufacturing industry has the possibility to obtain
major benefits in terms of money and time (ElMaraghy, Azab, Schuh & Pulz, 2009). At the same
time, companies need to ensure that their product meet the customer’s expectation, which highly
relies on quality of the production process (Coronado & Antony, 2002). ElMaraphu et al. (2009)
stress the importance of managing variations at all levels of the manufacturing process in order to
maintain the profit as well as the high level of quality, responsiveness and adaptability; meanwhile
offering the product variety for customers.
The well-known concept Six Sigma offers a set of statistical tools to measure variation and hence
make it able to manage the process variation (Alkunsol, Sharabati, AlSalhi & El-Tamimi, 2019).
When the process variation reduces, the number of defects also decreases, which is the reason for
the wide usage of Six Sigma in the manufacturing sector nowadays (Abhilash & Thakkar, 2019).
There are many companies that represent great examples of Six Sigma implementation, for
instance Motorola and General Electric which managed to save more than two billion dollars in
one year by reducing the cost of poor quality such as reduced defects, rework and warranty costs
(Alkundsol et al., 2019; Coronado & Antony, 2002). This highlights the importance for companies
to focus their resources on quality and improvement.
The quality of the final product is not only influenced by the production processes, but also from
the performance of the measurement system (MS) (Runje, Novak & Razumić, 2017). A MS can
briefly be described as all the components used to evaluate a certain characteristic of an object. A
critical, yet often overlooked, part of the journey towards reducing variation and continuous
improvement is developing confidence in the system that is used to measure the process
(Kazerouni, 2009). Here, an extremely important Six Sigma tool is measurement system analysis
(MSA) (Kazerouni, 2009; Zanobini, Sereni, Catelani & Ciani 2016). This study defines MSA
according to the definition by Niles (2002): a systematic procedure that identifies the components
of variations in the precision and accuracy assessments of measuring instruments used in a MS.
MSA is based on the philosophy that measurement error covers the true process capability, and
should therefore be performed prior to any other process improvement activity (Harry & Lawson,
1992).
2
It is apparent that maintaining a proper MS is crucial in most industries, however, there are some
industries that are more dependent of the MS than others. Battery technology has become
increasingly important along with the increasing demand of hybrid and electric vehicles (Liu, Bao,
Cui et al, 2019). This has brought both challenges and opportunities for battery manufacturers. To
satisfy the increasing market demand, manufacturing of high volume, high quality and high-
performance batteries are critical (Ju, Li, Xiao, Huang & Biller, 2014). Simultaneously, Ju, Li,
Xiao, Arinez & Deng (2015) claim that quality has been recognized as one of the most critical
issues in battery manufacturing due to its sensitivity and narrow safety tolerance. Additionally, it
can be problematic when the lack of quality in the earlier battery production line goes undetected
and may have substantial impact on the subsequent operations (Ju et al., 2015). A proper MS has
therefore a critical role to detect defects as quickly as possible and prevent them from traveling
further down the production line.
1.2 Case Study Background The Swedish start-up Northvolt was founded in 2016 with headquarters in Stockholm and with the
business goal to extend the boundaries of battery performance, quality and cost (Northvolt, 2020a).
This derives from a commitment to sustainability and the ambition to minimize the dependence of
the European automobile manufacturers on the Asian suppliers (Northvolt, 2020b).
In order to execute the business goal, a Gigafactory in Skellefteå, Northvolt Ett, is planned to
commence the production in large scale early 2021. Moreover, there are factories planned and
under construction in Germany (Northvolt Zwei) and Poland (Northvolt Battery Systems Jeden)
at this point (Northvolt, 2019c). In Västerås, where this Master Thesis is executed, the intended
R&D center Nortvolt Labs (see Figure 1) is starting its mission of qualifying and industrializing
battery cells and manufacturing processes together with the customers (Northvolt, 2019b).
Figure 1: Northvolt Labs (Northvolt AB, 2019a)
3
1.3 Problem Discussion and Purpose As Ju et al. (2015) mention, the lack of quality in early processes will accumulate and have a
negative impact on later stages in the production. Despite this fact, the measurement variation
interferes with the detection of the lack of quality and simultaneously covers the actual process
performance. As many decisions in the current competitive environment are made based on data,
the efficiency of the decision completely relies on the quality of the available data (Kazerouni,
2009). Along with the importance of making fact-based decisions, battery production encloses
multiple quality and safety requirements that have narrow measurement limits. Therefore, a
reliable MS in the battery industry is crucial.
The importance of a reliable MS has also been recognized by Northvolt Labs. The production line
is in a commissioning phase, where the need of process improvement and delivery of high-quality
products is highly prioritized. However, these tasks cannot be achieved without a reliable MS to
track the improvement and monitor the process output. Therefore, conducting MSA is vital for the
company at this stage. In addition, the processes in the production line are equipped with multiple
measurement instruments (MIs) to continuously monitor and measure the processes. Thus, it is
especially important that the MS perform properly at this stage, otherwise severe consequences
are unavoidable. As a start-up, Northvolt cannot afford to deliver products with poor quality since
it would have a substantial negative impact on the company image.
Furthermore, ensuring the performance of the MS is a requirement to be certificated by various
standards within the industry, such as ISO 9000:2015. The standard emphasizes the importance of
that organizations ensure the validity and reliability of monitoring and measuring results, which
inevitably are affected by the MI. Activities regarding calibration, verification or maintenance
should be regularly conducted and properly documented in order to ensure the measurement
traceability. Additionally, an ISO 9000:2015 certification is strongly required from the Northvolt
customers. Since Northvolt is striving to be certificated as early as in November 2020, MSA has
an important role to play for the company at this stage of business.
4
As earlier mentioned, MSA is an effective methodology to improve manufacturing processes by
reducing the variation in the processes and the defects in the products (Smith, McCrary, &
Callahan, 2007). Consequently, MSA can be used to reduce the variation of the MS in the battery
production at Northvolt. Thus, the purpose of this Master Thesis project is:
To improve the performance of a MS in battery production by using MSA and making
appropriate recommendations for improvement and future control.
To fulfill the purpose, following study questions (SQs) will be answered in regard to Northvolt:
SQ1 How can the measurement system be evaluated?
SQ2 How much of the variation in each process is due to the measurement system?
SQ3 How can the measurement system be monitored to ensure its performance?
1.4 Delimitations The focus of this project is to investigate the MS in two of the manufacturing processes, namely
Calendering and Cell Assembly of Prismatic Cells. These processes are mainly chosen to obtain a
moderate number of MIs to work with considering the time frame of this study, but also since the
company argues that these processes are the most eligible to investigate at this point in time.
Batteries come in a variety of shapes and designs. As the company sees the MS of the prismatic
battery line as more urgent than the cylindrical battery line, this study will only focus on the
manufacturing of the prismatic battery design at Northvolt Labs, see Figure 2.
Figure 2: Prismatic cell (Northvolt, 2019b)
5
1.5 The Logical Disposition Figure 3 below follows the structure of this report, which aims to provide better understanding of
the content of the remaining chapters. Since this Master Thesis was conducted according to the
DMAIC stage-model, the structure of the report is different from the traditional approach. The
main difference can be distinguished in the chapter Methodology, where the choice of action is
presented and motivated. However, the detailed description of how the action is accomplished is
presented in Case Study – DMAIC. The chapters Result and Analysis in the traditional report
subsequently are subchapters under Case Study – DMAIC.
Figure 3: Logical disposition of remaining chapters.
6
2 Methodology This section describes the methodology used to fulfill the purpose and answer the study
questions. The choice of research approach is also motivated and the strategies that have been
applied to achieve the aim of the Master Thesis are presented. Finally, the measures taken to
increase the creditability of the methodology are described.
2.1 Research Approach According to Åsvoll (2013), there are three main research approaches, namely deductive, inductive
and abductive. van Hoek, Aronsson, Kovács and Spens (2005) argue that the deductive approach
derives from reviewing the existing literature, from which logical conclusions are formed. These
conclusions in turns generate hypotheses, which eventually was evaluated by empirical
exploration. According to Arlbjørn and Halldórsson (2002), a deductive research approach is most
relevant for the purpose of investigating the validity of existing theories, rather than generating
new ones. An inductive approach distinguishes from a deductive approach when it comes to
generating new knowledge (van Hoek et al., 2005). An inductive approach starts with data
collection, from which analysis is conducted to obtain generalizable conclusions (van Hoek et al.,
2005; Saunders, Lewi & Thornhill, 2019). Further, an abductive approach effectively combines
both induction and deduction (Sauders et al., 2019). This approach starts with studying data to
identify any deviating phenomena. By using existing theory to explain these phenomena, new
knowledge can be obtained.
In this Master Thesis project, an abductive approach was preferably chosen. Since the production
at Northvolt Labs is in a commissioning phase and the existing internal knowledge about the MS
is limited, a deductive approach is not suitable, and a more exploratory approach therefore comes
naturally. Furthermore, there is no need to establish hypotheses since this study is aimed to
investigate the potential improvement of the assigned MIs and propose an appropriate control plan.
It is also important to emphasize that the aim of this Master Thesis was not to present any new
knowledge nor expose any gap in existing literature. However, even though the generated
knowledge from this project comes from analysis of collected data, the data has been analyzed
based on existing knowledge about MSA. This indicates a combination of deduction and induction,
and hence an abductive approach has been chosen.
2.2 Research Methodology The focus of Six Sigma is to decrease the variability in a process output till the likelihood of defects
becomes extremely low (Montgomery & Woodall, 2008). If considering the MS as an independent
process and the measurement reading as output, the Six Sigma tools can be applied on the MS to
minimize the variability of its reading.
7
The structured improvement procedure of Six Sigma is known as the DMAIC framework (define-
measure-analysis-improve-control) (De Mast & Lokkerbol, 2012). By utilizing statistical tools,
such as Design of Experiments, Process Capability Analysis and Control Charts, the DMAIC stage
model is exceptionally effective in process improvement (Montgomery & Woodall, 2008).
Due to the project purpose, the Six Sigma DMAIC framework was chosen as stage model, and to
which the case study was conducted accordingly. It was also important to mention that MSA used
to be an activity performed during the Measure phase. However, due to the scope and the
importance of MSA for the case company, MSA was chosen to be the subject of the thesis. The
DMAIC framework is further described by Montgomery and Woodall (2008) below.
2.2.1 Define The objective of the Define phase is to determine the project opportunity in order to legitimate
breakthrough potential. Initially, a project charter is established (Montgomery & Woodall, 2008),
which helps to define the project scope and clarify important tollgates. The authors also suggest
utilizing graphical aids such as process maps, flow charts or SIPOC (supplier-input-process-
output-customer) to illustrate and help with a better understanding of the processes.
For this Master Thesis project, the Define step was initiated with studying about the batteries and
battery production processes to understand the operation better. Further, a literature study was
conducted in order to obtain relevant knowledge about MSA, see 2.3.1 Literature Review. To
clarify the purpose of the project, its scope, responsibility of team members and different tollgates,
a project charter was formulated with support from the supervisor at the Northvolt. To obtain a
deeper understanding of the process, a process overview was created. Consequently, an estimation
of potential impact in terms of potential cost savings, increased revenue or customer satisfaction
were calculated.
2.2.2 Measure The purpose of the Measurement phase is to evaluate and understand the process by collecting
data on different criteria related to quality, cost and cycle time. The authors mean that data
collection can be conducted by studying historical records as well as collecting process data during
a certain period. This is, however, depending on the completeness of the existing data. If there are
many human factors involved in the process, data samples would be more useful. The collected
data is used to study the current state of the process.
Regarding the Master Thesis project, more MS related data were collected in this phase. Since the
production includes different types of MIs, different approaches were conducted to estimate the
capability of each MI. Furthermore, since the production is in its commissioning phase, actual
process data was not available. Therefore, Standard Weights and Standard Gauge Blocks with
known specifications were used to justify the capability of the MS. In addition, sample collections
for each MI were required where the project group was assisted by the technician responsible for
8
each process to gather samples. As this was the first time MSA is conducted at Northvolt, this
phase was also aimed to discover the appropriate data collection procedure for each type of MI.
Such knowledge would likely be implemented in the large-scale production at Skellefteå Ett in
order to save time and ensure data completeness.
2.2.3 Analyze The Analyze step is aimed to determine the cause-and-effect relationship in the process by using
data from the previous phase. Here, the main purpose is to identify the root cause as well as any
quality issue or problem that initiated the project. Montgomery and Woodall (2008) provides a
large range of statistical tools that are relevant in the Analyzing phase. Some of these tools are
control charts, regression analysis and Design of Experiment. Sometimes the analysis of data
exposes useful evidence concerning potential problem causes, which may lead to specific
improvement actions. However, the aim of this step is to study the correlation between different
variables in the process to ultimately address specific causes that need to be listed prior to the
Improve phase.
In this Master Thesis, the Analyze step was conducted to justify whether each measurement
instrument is capable and acceptable for the intended purpose. The criteria of acceptance were
formulated based on the results of the literature review, where important characteristics of a MS
was identified and classified. The data analysis was performed using the computer software,
Minitab and Microsoft Excel.
2.2.4 Improve This step concerns the development of specific adjustments that can be performed to improve the
process and solve its related problem. Such changes can include redesigning the process or Design
of Experiments. Once the solution to a problem has been developed, a pilot test should be
conducted to evaluate and document the solution to ensure the alignment between the solution and
the project purpose.
The Improve phase in this study was intended to address the issue identified in the Analyze phase.
Once the suggested improvement was conducted, another sample from the MS was taken and
analyzed in order to follow up the improvement results as well as other issues, which were not
earlier addressed. This loop was performed between every sampling until the performance of the
MS reached the acceptance criteria.
2.2.5 Control The final step in the DMAIC stage model is referring to the Control step. This step aims to ensure
that the achievement from the project is utilized (Montgomery & Woodall, 2008). This step
includes the handover of the improved process to the project owner. Furthermore, a control plan
with control charts on critical process metrics should also be provided.
9
For this Master Thesis project, one of the key results is the improved performance of the MIs,
which has been progressively implemented during the project and hence did not require an
implementation plan. Instead, the Control phase of this Master Thesis included a control plan with
information regarding what process, which MI, what measurement, how to perform the sampling
procedure and QC verification was provided and handed over to the case company. Furthermore,
the control plan also stated the frequency to conduct MSA, concrete instruction to the technicians
or the person in charge. In addition, a decision tree for MSA with further recommendations and
analysis instructions was also shared with the case company.
2.3 Knowledge Establishment An important part of the project has been to identify existing knowledge regarding MSA and
structure a theoretical framework to shape the entire project. Also, knowledge regarding different
processes and MIs was acquired through experts within the field.
2.3.1 Literature Review In the initial phase of this study a theoretical framework was established to position the study and
further explain key concepts and definitions. Appropriate literature has been studied to highlight
the existing knowledge and identify the type of new knowledge required to answer the purpose
and study questions (Thorgren & Frishammar, 2019). The aim was to retrieve information from
peer-reviewed, multiple cited, scientific articles by targeting relevant journals. Through the library
of Luleå University of Technology, the databases used for obtaining relevant literature were
Google Scholar and Scopus. Keywords used in the literature review were Measurement System
Analysis, Measurement System, Six Sigma and Gauge R&R. To further extend the knowledge,
course literature and books such as Introduction to Statistical Process Control (Montgomery,
2012) and Measurement system analysis (MSA) (AIAG, 2010) were used.
2.3.2 Interviews To explore a general area in depth, a semi-structured interview is appropriate. This type of
interview enables the respondent to explain or build on the previous answer and is therefore
beneficial for more specific data collection (Saunders et al., 2019).
Multiple semi-structured interviews were held to better understand how the MI and the calibration
procedure functions. The interviewees were chosen to be technicians, process engineers and
suppliers because they have great experience in the MI and the associated processes. The obtained
information contributed with knowledge in how to handle this type of instrument in upcoming
factories within the studied company.
10
2.4. Creditability of Research Findings To ensure the creditability of the research result as well as minimize the risk of getting an
inadequate answer, Sauders et al. (2019) claim that the focus should lay on the research design.
According to Golafshani (2003), the reliability of the research finding concern how the result is
consistent over a certain duration. Concurrently, the researched population should be
representative for the total population while using the same methodology. In other terms, the
author means that reliability refers to the consistence of the research finding when using the same
data collection procedure and analysis approach. Furthermore, Golafshani (2003) refers the
validity of the research to the degree that the research truly measures what it was intended to. Thus,
it refers to the accuracy of the research. Especially in quantitative research, researchers affect the
validity of the research by formulating concepts, hypothesis or questions to obtain the data they
prefer. In addition, the choice of analysis method or test also affects the interpretation of data.
To ensure the reliability of the study, the framework for analysis was derived from existing
knowledge during literature study conducted. Therefore, the framework was expected to include
all the aspects when it came to evaluate a MS. Since all the aspects of a MS were studied, it was
reasonable to expect that similar result regarding the performance of the MS was achievable when
evaluating with other frameworks than the one using in this study. The project group also received
standardized trainings when measured reference value, which ensure the quality of the result and
the traceability of the measurement.
The characteristics of the MS which were evaluated in this study were general and can be found
in all types of MS. Furthermore, the characteristics were derived from literatures and research
conducted in the other industries than the battery industry. Many MI in the case company such as
electronical scale, camera or thickness sensors can also be found in other industries. It was
therefore reasonable to believe that the framework used in this study can be adopted in other
industries. The statistical tools used in this study are useful to describe the data collected in this
study. To ensure the validity of this study, conclusions and decisions were made based on many
criteria rather than a single metrics. Such criteria as sample collection approach, quality of data,
sample size as well as external factors were also taken into consideration when decisions or
conclusion were made.
11
3 Theoretical Framework Firstly, the theoretical framework captures the MS in general terms and specific ways in how to
analyze it. It also highlights how the decision-making is influenced by the MS and emphasizes
the importance of the cost of quality. Lastly, an overlook of MSA in ISO 9001 is presented.
3.1 The Measurement System A measurement system (MS) is defined as “the collection of instruments or gauges, standards,
operations, methods, fixtures, software, personnel, environment and assumptions used to quantify
a unit of measure or fix assessment to the feature characteristic being measured; the complete
process used to obtain measurements” (AIAG, 2010). Further, AIAG (2010) argues that there are
certain fundamental properties that distinguish a well-functioning MS:
1) Adequate discrimination and sensitivity – The variation of measurement should be small
relative to the process variation or specification limits for the purpose of measurement.
2) The MS ought to be in statistical control - This means that under repeatable conditions, the
variation in the MS is due to common causes only and not due to special causes.
3) For product control, variability of the MS must be small compared to the specification limits.
This means an assessment of the MS to the feature tolerance is necessary.
4) For process control, the variability of the MS ought to demonstrate effective resolution and be
small compared to manufacturing process variation. An assessment of the MS to the 6-sigma
process variation and/or total variation from the MSA study is necessary.
Apart from this, a MS is also required to have appropriate statistical properties in order to measure
what it is intended to in a proper way (AIAG, 2010), which is an important aspect within process
improvement activities (Montgomery, 2012).
3.1.1 The Concept of Precision and Accuracy Montgomery (2012) and Grubbs (1973) argue that each measurement made by MI generally
consists of an inherent measurement error, which can be divided into precision and accuracy.
Precision is in many organizations often interchanged with repeatability, which describes the
expected variation of repeated measurements over the operating range (size, range and time) of the
MS. However, the American Society of Testing and Materials (ASTM) defines precision in a
broader sense to include the variation (reproducibility) from different readings, gauges, people,
labs or conditions (AIAG, 2010). This study aligns with ASTM and will define precision to include
both repeatability and reproducibility.
12
Accuracy can be described as the extent of agreement between the average value of one or
more measured results and a reference value (AIAG, 2010). In other words, accuracy is the
measurement of the systematic error of the MS. As AIAG (2010), this study defines accuracy
as the difference between the true value (reference value) and observed average of
measurements on the same characteristic on the same part. Precision and accuracy are
illustrated in Figure 4.
Figure 4: The concept of precision and accuracy.
3.1.2 Measurement System Error To briefly introduce the studies of measurement system errors, consider this formula describing
MS performance.
𝜎𝑇𝑜𝑡𝑎𝑙2 = 𝜎𝐺𝑎𝑢𝑔𝑒
2 + 𝜎𝑃𝑟𝑜𝑐𝑒𝑠𝑠2
where 𝜎𝐺𝑎𝑢𝑔𝑒2
is the measurement error, 𝜎𝑇𝑜𝑡𝑎𝑙2 is the total observed variation and 𝜎𝑃𝑟𝑜𝑑𝑢𝑐𝑡
2 is the
actual process performance. Further, Montgomery (2012) defines 𝜎𝐺𝑎𝑢𝑔𝑒2 as reproducibility and
repeatability, which are included in the concept of precision (Kazerouni, 2009; Hajipour, Kazemi
& Mousavi, 2013; Cagnazzo et al., 2010). While mentioning accuracy as an important aspect of
the measurement system capability, Montgomery (2012) never includes this in his definition of
𝜎𝐺𝑎𝑢𝑔𝑒2 . This is supported by Kooshan (2012), and they both seem to set aside the importance of
the measurement accuracy.
However, Grubbs (1973), Kazerouni (2009), Hajipour et al. (2013) and Runje et al. (2017)
highlight the concept of accuracy and include this in the measurement of error. Runje et al. (2017)
13
emphasize the importance to consider all the elements of a MS when performing MSA. Grubbs
(1973) argues how both precision and accuracy are important to calculate the true product
variability and true variances in errors of measurements. The equation can therefore be considered
as inadequate to evaluate a MS since improvement in precision does not necessarily mean that the
MS produces a true value. Evaluating the difference between the observed value and the true value
of the samples is therefore an approach to analyze the aspect of accuracy in the MSA framework.
3.2 Measurement System Analysis To ensure that the process delivers products within the specification, the MS must perform
properly to minimize the measurement error (Pai, Yeh & Hung, 2015). Data collected by MIs
generally consists of an inherent measurement error (Chen, Wu & Chen, 2008; Pai et al., 2015),
and risks to be inaccurate and hence considered inappropriate to use for monitoring the process
(Chen et al., 2008). In the attempt to improve the MS performance, companies have established
various techniques, including MSA.
External factors such as human, material, machine or methodology can affect a MS to an extent
that systematic and random errors may occur during the measurements (Wu, Pan, Cai & Zhang,
2014). The purpose of MSA is to ensure the reliability of the measured data (Pai et al., 2015) and
to capture and quantify the measurement error in the MS (Wu et al., 2014). By performing
statistical analysis and graphical methods on the MS error, the MSA estimates the variability
associated with the MS (Pai et al., 2015), which includes variation from the MI, the appraiser, the
measured part as well as the surrounding environment (Chen et al., 2008).
There are five types of classified measurement errors: bias, linearity, repeatability, reproducibility,
and stability (Wu et al., 2014). These errors can be categorized according to the concept of
precision and accuracy, see 3.1.1 The Concept of Precision and Accuracy. According to this
categorization, accuracy includes error associated with bias, linearity and stability while
repeatability and reproducibility constitute the precision error (Kazerouni, 2009; AIAG, 2008).
The concept of MSA have been summarized in Figure 5 below.
14
Figure 5: The concept of MSA
If a MS is not capable to detect process variation, decisions cannot be made based on its produced
data. This is where MSA comes into play, since it evaluates if the MS is suitable for the intended
purpose (AIAG, 2010). As accuracy reflects the quality of the measurement data, precision reveals
the predictability of the MS (AIAG, 2010). It is therefore important to analyze and monitor these
characteristics when conducting MSA. However, accuracy and precision are often considered
interchangeable which can result in inadequate decision-making concerning the product and
process, since controlling one of the error types does not necessarily ensure control of the other
(AIAG, 2010). It is therefore important to understand the difference between these aspects.
3.2.1 Accuracy As previously mentioned, the accuracy error can be divided into bias, linearity and stability.
Bias
Bias, see Figure 6, refers to the difference between the averages of the measured data and the
reference value of the measured part (Pai et al., 2015; Kazerouni, 2009; AIAG, 2008; Wu et al.,
2014). Pai et al. (2015) state that the bias can be obtained when the same appraiser repeatedly
measures the same characteristic of the same part using the same measuring equipment. By
determining the difference between the gauge reading and the reference value, the bias can be
calculated. The gauge reading is constituted by a single or by multiple measurement(s), meanwhile
the reference value comes from readings of certified measurement equipment (Pai et al., 2015) or
a known value of a reference sample (AIAG, 2010). An example for bias is that when using a scale
to measure a sample with the true weight of 5 kg. The reading from the scale is instead 6 kg, which
indicates a bias of 1 kg.
15
Figure 6: Visualization of Bias Error
Linearity
Another aspect affecting MS accuracy is linearity, which according to Wu et al. (2014) is the
difference of the bias value within the operating range of the measurement instrument, see
Figure 7. In other words, it is the difference between the observation and the reference value for
different ranges. When linearity exists in the MS, the size of bias varies as the size of reference
varies. The existence of linearity indicates a systematic error in the MS (AIAG, 2010). Generally,
the existence of error related to bias and linearity is unacceptable (AIAG, 2010). If present,
recalibration to minimize the error is required. An example for linearity is that when using a scale
to measure multiple samples. The bias observed when measuring the lightest sample is
significantly differed the weight of the heaviest one.
Figure 7: Visualization of Linearity Error
16
It is of interest to evaluate whether the linearity is acceptable at a certain confidence interval.
According to AIAG (2010) and Wu et al. (2014), such evaluation can be conducted by performing
a linear regression over the average of the observation for each size of parts. Consequently, a Test
of Hypothesis is performed to determine if the slope of the fitted regression line and the intercept
is significantly differed from zero. If the slope of the regression can assume the value of zero, the
bias for all reference values in the MS must be the same.
Stability
The last aspect of error in precision is stability. It refers to the variation of the measured
observations obtained using the same MS to measure the same characteristic of the same part over
a certain duration (Wu et al., 2014; AIAG, 2010). In other words, stability reflects the variation of
bias over time, see Figure 8. The system has acceptable stability if the measurement produces
similar readings every time (AIAG, 2010). According to Montgomery and Woodall (2008), the
drift in bias over time can be a result of machinery warm-up effects, shift in environment or change
in operating procedures. Such shifts can easily be detected by monitoring the bias in a �̅� & 𝑅 or
𝑋 ̅& 𝑠 control chart over time (AIAG, 2010). If the control chart does not show any out-of-control
or noticeable pattern, the error associated with stability is not significant.
Figure 8: Visualization of Stability Error
17
3.2.2 Precision As earlier mentioned, variation concerning precision can be distinguished by repeatability and
reproducibility.
Repeatability
The repeatability refers to the variation that occurs when repeatedly conducting measurements of
the same part using the same gauge under the same circumstances, while measuring the same
characteristic of the part (Kazerouni, 2009). In other words, repeatability displays the capability of
the MS to produce similar readings from repetitive measurements, see Figure 9. Since repeatability
is the variation from repetitious trials under a defined measurement condition, it includes all the
within-system variation (AIAG, 2010). A scale with high repeatability is able to produce consistent
results with multiples readings.
Figure 9: Visualization of Repeatability
Reproducibility
The reproducibility is the variation in the measurement when different appraisers measure the same
part with the same MI (Kazerouni, 2009; Wu et al., 2014). However, AIAG (2010) argues that this
is only true when the measurement is performed manually, because an appraiser is not a significant
source of variation in autonomous systems. Hence, it is more accurate to define the reproducibility
as the between-system or between-condition variation, see Figure 10. The difference in conditions
can be referred to different working instructions, different people who conduct measurements or
different environment.
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Figure 10: Visualization of Reproducibility
Gauge R&R
To evaluate the repeatability and reproducibility of the MS, a Gauge R&R study can be conducted
(Runje et al., 2017). There are several methods for a Gauge R&R study to be conducted
accordingly (AIAG, 2010; Burdick, Borror & Montgomery (2003). However, according to
Burdick et al. (2003), a Gauge R&R study using an analysis of variance is preferable. As the
method can be adapted to handle more complex experiments, it is both simple and widely used by
practitioners.
When evaluating the precision of the MS, the precision-to-tolerance (P/T) ratio is a common
indicator in the production industry (Dalalah & Hani, 2016). The metric can be calculated as
following:
𝑃 𝑇⁄ =𝑘�̂�𝑔𝑎𝑢𝑔𝑒
𝑈𝑆𝐿 − 𝐿𝑆𝐿
In the equation, �̂�𝑔𝑎𝑢𝑔𝑒 refers to the estimated measurement error, 𝑈𝑆𝐿 − 𝐿𝑆𝐿 is the tolerance
band. The constant k is often chosen to be either 5,15 or 6,00 depending on the desired level of
confidence. According to the guideline of AIAG (2010), the variation from the MI should not take
more than 10 % of the output tolerance. In this study, the abbreviation used by Montgomery (2012)
is applied.
19
The signal-to-noise ratio (SNR) is another measure of MS adequacy (Montgomery, 2012). AIAG
(2010) defines SNR as “the number of distinct levels of categories that can be reliably obtained
from the data”. Further, AIAG (2010) states that this measure demonstrates the ratio between the
signal power and the noise power. The metric can be calculated as following:
𝑆𝑁𝑅 = √2𝑃𝑝
1 − 𝑃𝑝
Where 𝑃𝑝 is the ratio between the process variability 𝜎𝑃𝑟𝑜𝑐𝑒𝑠𝑠2 and the total variability 𝜎𝑇𝑜𝑡𝑎𝑙
2 . A
value of five or more is usually recommended, and a value of two or less indicates that the MS is
of no value when monitoring the process (Burdick et al., 2003).
3.3 Measurement System Analysis of Attribute Data Apart from measuring variable data, the concept of MSA also includes attribute data (Furterer,
Hernandez & Doral, 2019). For this type of analysis, the study assesses how well the appraisers
agree with each other, with themselves as well as the standard, which is why an MSA of attribute
data is usually called Attribute Agreement Analysis (AAA). Similarly, as variable data, attribute
data can be studied based on accuracy and precision. The accuracy refers to the variation between
measurement and reference values meanwhile the precision aspect concerns the variation from
repetitive measurement of the same part in the same condition (repeatability) and the variation
when the same part is measured in different conditions (reproducibility) (Furterer et al., 2019).
According to Marques, Lopes, Santos, Delgado and Delgado (2018), there are two ways of
evaluating performance of an attribute MI. The first is referring to the percentage of agreement
between the appraisers and the reference value, or among appraisers. The second concerns the
kappa statistic. As the first one indicates the observed agreement that can be calculated based on
what value the MI generates, the latter considers the possibility that the agreement occurs by
chance (Viera & Garrett, 2005). The metric can be calculated as following:
𝐾𝑎𝑝𝑝𝑎 𝑠𝑡𝑎𝑡𝑖𝑠𝑡𝑖𝑐 = 𝑂𝑏𝑠𝑒𝑟𝑣𝑒𝑑 𝑎𝑔𝑟𝑒𝑒𝑚𝑒𝑛𝑡 𝑟𝑎𝑡𝑒 − 𝐴𝑔𝑟𝑒𝑒𝑚𝑒𝑛𝑡 𝑏𝑦 𝑐ℎ𝑎𝑛𝑐𝑒
According to AIAG (2010), a Kappa value higher than 0,75 indicates good agreement, but a value
of 0,9 or higher is preferred. In this project, the Kappa statistic value will be used to assess the
attribute MS at the case company.
20
3.4 Cost of quality Cagnazzo et al. (2010) argue how the accuracy of a MS will have a direct influence on the judgment
of a product and process quality. Consequently, this has a substantial impact on the cost of quality
as well. Cost of quality is an important factor within the quality concept and is a suitable tool to
direct companies into a more promising future (Desai, 2008; Surange; 2015). There is no general
agreement on a single definition of cost of quality in the literature (Schiffauerova, Thomson, 2006).
However, Surange (2015) divides the quality costs in two categories: cost of good quality and cost
of poor quality. Surange argues how cost of quality is not the price of creating a quality product or
service. Instead, it is the cost of not creating a quality product or service. Desai (2008) views cost
of quality as a double-edged sword, which ensures quality improvement along with cost reduction.
Further, the cost of quality can be divided according to the most established PAF (Prevention,
Appraisal, Failure) model (Porter & Rayner, 1992; Schiffauerova & Thomson, 2006; Desai 2008),
namely
Prevention – The cost of the actions taken to investigate, prevent or reduce the risk of non-
conformity or defects (Porter & Rayner, 1992).
Appraisal – The cost of evaluating the achievement of quality requirements (Porter & Rayner,
1992).
Failure – The cost of nonconformities, both internal failure (discovered before customer delivery,
such as scrap, rework, re-inspection), and external failure (discovered after customer delivery, such
as warranty costs and service calls) (Porter & Rayner, 1992).
Nevertheless, the literature regards the cost of quality as the sum of conformance and non-
conformance cost, where cost of conformance is the price paid for prevention of poor quality (for
example, inspection and quality appraisal) and cost of non‐conformance is the cost of poor quality
caused by product and service failure (such as rework and returns) (Schiffauerova, Thomson,
2006). Schiffauerova & Thomson (2006) states that even if many larger companies claim to assess
quality costs, multiple industry surveys have confirmed that cost of quality is not a widely applied
area, even regarding larger companies. Companies rarely have a realistic idea of how much profit
they are losing through poor quality (Schiffauerova & Thomson, 2006). However, companies that
have implemented cost of quality to drive quality costs down seem to be successful. Hesford &
Dale (1991) studied how British Aerospace Dynamics significantly managed to lower the
manufacturing costs with cost of quality, including improvements of the MS. Moreover, Knock
(1992) investigated how the company York International managed to reduce their failure
manufacturing costs with 96 % by applying cost of quality to their organization.
21
It is important to find the balance between the cost of conformance and non-conformance. In this
case study, the studied MS must undergo substantial improvement to achieve better performance.
However, when applying the principle of cost of quality on the MS, if the costs of improving the
MS are too extensive, it is more beneficial to, for example, establish regular maintenance.
3.5 MSA and Decision-making MSA is necessary to evaluate the precision and accuracy and facilitates to understand the
implications of measurement error for decision-making about a product or process (Cagnazzo,
Sibalija & Majstorovic, 2010; Diering, Hamrol & Kujawińska, 2015). Critical decision-making is
often based on data from the manufacturing processes (Kazerouni, 2009). The outcome of these
decisions is strictly related to the quality of the data, which is what ties MSA and decision-making
strongly together (Cagnazzo et al., 2010).
A MS incapable of detecting process deviation can never be trusted to decide on process
adjustment, or any decision at all (Cagnazzo et al., 2010). A wrong decision can be made whenever
any part is measured to be outside the specification limits and is therefore considered good (type I
error). Or oppositely, a bad part can sometimes be considered good (type II error) and subsequently
is sent to the customer. When the decision-making is based on inaccurate data, it has the potential
to cost companies substantial amounts of money and time (AIAG, 2010).
Cagnazzo et al. (2010) investigated the MSA action implemented in a manufacturing company
and evaluated the measurement system capability. When analyzing based on the concept of bias,
linearity and Gauge R&R, it reveals how proper MSA actions could influence the general business
performance. The business achieved a significant financial improvement in a short time. Further,
Cagnazzo et al. (2010) explain that the business performance of a company is related to the
decision-making process, which also is seen as an underlying, contributing factor in the success.
Furthermore, Diering et al. (2015) mention that it is possible to ensure the efficiency of decision
regarding for example process and product control. By monitoring the MS in terms of bias,
stability, reproducibility and repeatability, the quality of measurement data is assured, which
positively influences the overall business. The same conclusion can therefore be applied for
Northvolt and a proper implementation of MSA is thus advantageous for the company business.
22
3.6 Measurement System Analysis in ISO 9001:2015 Ensuring the MS performance is also required to be certificated by standards in the automotive
industry, which is often demanded by the customers. ISO 9000:2015 is an international standard
that guides companies in establishing a Quality Management System and simultaneously provides
accredited certification (Hadidi, Assaf, Aluwfi & Akrawi, 2017). According to the authors, there
are many studies that present evidence how achieving the certificate effects the customer
satisfaction. Even though the requirements of the standard are generic, some requirements can be
specific, depending on the industry (Pop & Elod, 2015). One requirement refers to documentation
of how companies properly use different Automotive Quality Core Tools such as Statistical
Process Control, Potential Failure Modes and Effect Analysis. The utilization of MSA is also one
of the tools mentioned by Pop and Elod (2015).
ISO 9001:2015 stresses the importance of monitoring and measuring in an adequate manner. (SIS,
2015). The requirement also highlights that organizations must ensure the creditability of the
monitoring or measuring results. Activities referring to calibration, verification or maintenance
should be regularly conducted and properly documented to ensure the measurement traceability
(SIS, 2015). Since Northvolt is striving to be certificated with ISO 9001:2015 as early as
November 2020, MSA has an important role to play for the company in this stage of business.
23
4 Case Study In this section, the case study conducted at Northvolt is performed. By following the DMAIC
model, the case study begins with the define phase where project charter and production
processes are reviewed. In measure and analyze phase, the data was collected and analyzed,
respectively. Improvement suggestions for the MS is presented in the Improve phase. Lastly, in
the control phase, a plan for how Northvolt in the future can monitor and conduct MSA is found.
4.1 Define As different MIs included in this study have their own attributes and functionality, they need to be
analyzed differently. Therefore, the proper approach is to consider each of the included MIs as an
own mini-DMAIC. In this way it is easier to keep track of the MI improvements.
4.1.1 Project charter The project charter, see Table 1, presents the overview of the project that was formulated by the
team. The purpose of the project charter was to structure the project approach, highlight the
importance of the project outcome and to align all involved in the project.
Table 1: Project Charter
Problem statement
As the production at Northvolt Labs is newly
implemented, the need of improvement is apparent.
However, improvement cannot be properly
conducted without reliable measurement systems to
track the results. The lack of reliable data can lead to
wrong decision-making which inevitably has
negative effects on company image as well as profit.
Business case
To achieve the goal of delivering sustainable batteries with high
quality to a competitive price, quality must be incorporated in
every step of the production. Defect products must be detected
and removed. As Northvolt is a start-up it is important with a
respectable reputation by managing to deliver products with
high quality. A functioning and reliable measurement system is
crucial for achieving this. Thus, conducting MSA aligns with
the business targets and is a fundamental need for future
successful business results.
Goal Statement
To improve the measurement systems in the battery
production by using Six Sigma methodology and
making appropriate recommendations for improve-
ment and control.
Project scope
All measurement instruments included in the manufacturing
processes Calendering and Cell Assembly of the prismatic
battery design.
Project Plan
Phase Start End
Define
Measure
Analyze
Improve
Control
w. 4
w. 7
w. 11
w. 15
w. 20
w. 7
w. 12
w. 16
w. 19
w. 23
Team
Phuc Nguyen, Adam Sahlberg
Erik Lovén, Sreepal Reddy
24
4.1.2 Process overview In Table 2, a mapping of each MI type existing in Calendering and Cell Assembly processes is
presented. This is followed by a brief description of a battery cell. The Calendering and Cell
Assembly processes of the prismatic cell design are then described in a consequential order. All
processes take place in a dry and clean room due to the need of a controlled environment and to
minimize the level of particle contamination. The visualization of the process could have been
done using tools such as SIPOC-chart or process flow chart. However, such tools require some
information that, to a certain extent, is confidential. Therefore, the processes are visualized in a
simpler structure excluding such confidential information without disrupting the comprehension.
Table 2: Existing measurement instruments in Calendering and the Cell Assembly processes
Measurement instrument Amount Function
TF Laser Gauge 2 A laser that screens the electrode back and forth to monitor the
thickness.
Charge Coupled Device (CCD) 17
A camera system that measures dimension and detects shape or
surface defects. The initial pixel size is 50 µm and the highest
accuracy is 3 pixels, i.e. 150 µm. Because of its versatility, the
CCD is the most common measurement instrument within the
investigated processes.
Electronic Scale/Load Cell 4 Monitors weight.
HI Pot Test 2
An insulation test that investigate if there is contamination with
metal particles in the battery cell which risk short-circuit. Since
the cell is not filled with electrolyte, the electron movement is
limited, and electric current should not occur. By applying testers
(current collectors) to both the anode and cathode the current
going through the cell can be detected.
Contact Sensor 1 Monitors thickness.
Battery cell
A battery consists of several battery cells arranged together in a serial or parallel combination,
which helps to create the desired capacity. The cell is the most essential part of the battery as it
holds the electrochemical reaction and releases energy. The battery cell is built by electrodes
(anode and cathode) which allow the exchange of electrons and lithium ions and produce
electronical energy. The cathode and anode electrodes consist of aluminum and copper foil
respectively, coated with a slurry mix of carbon and other active materials. The anode and cathode
operations are similar and run parallelly until they are assembled in the Stacking process, see
Electrode Cutting & Stacking. In a battery cell, the cathode and anode electrodes are arranged
alternatively and held apart by a separator. This entirety is kept in a steel can, to not be affected by
external forces. Once the can is sealed it is filled with electrolyte, which allows the movement of
electrons and lithium ions.
25
Calendering
In Calendering, see Figure 11, the coated electrode is compressed through two oil-heated, massive
cylindrical pressing rolls. Simultaneously, the electrode thickness is reduced to a controlled value
as improvement in adhesion and the active material density is achieved. The thickness, see Figure
12, is monitored by the TF Laser Gauge. Lastly, the electrode is rewound into a roll which at this
stage is called Jumbo Roll.
Figure 11: Calendering Process
Figure 12: Anode Jumbo Roll and Thickness of Electrode
Notching & Slitting
The Cell Assembly process starts with Notching & Slitting, see Figure 13, where the Jumbo Roll
is loaded and unwounded at the loading station. After being straightened, it is notched by a
mechanical notching unit that continuously moves up and down to cut the electrode with its sharp
edges, see Figure 14. The electrode is then cut in half and the width of each half is monitored by a
CCD. Lastly, they are rolled up on two different rolls, called Pancakes, before unloading.
26
Figure 13: Notching and Slitting process
Figure 14: Cathode Pancake
Electrode Cutting & Stacking
In the Electrode Cutting and Stacking process, see Figure 15, the electrodes are cut into dimensions
according to the current design specification. There are two CCDs that controls the dimension of
each cut electrode, see Figure 16. The cut electrodes then enter the stacking procedure, where the
corresponding electrodes are stacked alternatively and separated by a zig-zag folded separator foil,
see Figure 17. When the stacking sequence is finished the output is called a Jelly Roll, see Figure
19.
27
Figure 15: Electrode Cutting and Stacking process
Figure 16: Anode electrode dimensions
28
Figure 17: The Stacking procedure into a Jelly Roll.
Hot Pressing
The Jelly Roll is transported to Hot Pressing, see Figure 18, where it is pressed with heat to prevent
movement of electrodes. The thickness, see Figure 19, and the weight of the Jelly Roll are
monitored by a thickness sensor and an electronical scale.
Figure 18: Hot Pressing process
Figure 19: Jelly Roll thickness with pre-welded tab dimension
Tab Pre-Welding
This procedure contains an ultrasonic welding machine to weld the multi-layer electrode foil tabs
together, see Figure 20. The dimension of the pre-welded anode and cathode tab, see Figure 19, is
monitored by one CCD respectively. Lastly, a HI Pot test is conducted to examine contamination
in the Jelly Roll.
29
Figure 20: Pre-welding process
Tab Final Welding
A certain number of Jelly Rolls are taped together to obtain the desired battery capacity. A CCD
is used to monitor the taping position to ensure the stability of the Jelly Rolls. The pre-welded tabs
of the Jelly Rolls are welded together with a current collector (anode to anode and cathode to
cathode), see Figure 21. The angle of the current collector, the lid angle, is controlled prior to
welding procedure by another CCD, see Figure 22. Lastly, a HI Pot test is conducted to ensure
non-contamination.
Figure 21: Final welding process
Figure 22: Lid Angle and Film Wrapping Position
Insulation Film Wrapping
To ensure stability and prevention of short-circuit, the welded battery cell is wrapped with an
insulation film, see Figure 23. The wrapping position, according to Figure 22: Lid Angle, is
monitored by two CCDs at three different positions on each side of the cell.
30
Figure 23: Insulation film wrapping process
Electrolyte Filling #1 and #2
Before the battery is injected with a certain amount of electrolyte through repeated pressurization
and vacuum pumping, see Figure 24, the weight of the incoming battery is controlled by an
electronical scale. Once the battery is filled the first time, its weight is controlled again by another
scale before it is transported to age at a high temperature, which enable the electrolyte can be fully
infiltrated. After aging, the can is filled with electrolyte for the second time to ensure that the
battery is properly filled.
Figure 24: Electrolyte Filling #1 & #2
4.1.3 Potential savings Pai et al. (2015) argue how a well-functioning MS can incur great savings by detecting Type I and
Type II errors. As the fundamental purpose of the measurement is to determine whether the
products meet the quality specifications and safety requirements, great expenses occur when
sending defective products to the customers or rejecting an acceptable product (Pai et al., 2015).
In addition, Cagnazzo et al. (2010) argue how MSA strongly influences the general business
performance, where significant financial benefits can be achieved in a relatively short period of
time. As Northvolt is a start-up, and as their batteries are not rigorously tested at this point, it would
not be beneficial for the company image and business to utilize a dysfunctional MS.
31
However, there are no historical data to base this calculation on. Thereby, the potential savings
must be based on how many Type I or Type II errors that occur annually. In any hypothesis test, α
refers to the probability of Type I error and β to the probability of Type II error (Pollard &
Richardson, 1987). As the calculation of β involves data such as effect size and sample size
(Pollard & Richardson, 1987) which differs largely between each MI, the Type II error cannot be
calculated properly and is therefore overlooked in these calculations. Furthermore, since the Type
II error refers to the rejection good products, the type I error is considered to entail higher costs,
as Northvolt’s image and reputation also may be affected. Since the production at Northvolt Labs
cannot be considered stable, it assumes a 5 % defect rate as in a four-sigma process instead, see
Figure 25: Type I Error rate.
According to personal communication (March 23rd, 2020 and 31st March 2020), information about
the total cost of a standard battery cell and the annual production volume in watt-hours were
obtained. With an assumption that 5 % of the production is defect, and that 5 % of the defects
experiences a Type I error, an unreliable MS would cost Northvolt 395 000 SEK annually. To
clarify, further expenses in terms of brand image, inferior customer relationships or Type II errors
is not considered in these calculations.
Approved
95%
Defect
5%
Annual production volume (Wh)
Detected +
Type 2
95%
Type 1
5%
5% Defected
Figure 25: Type I Error rate
32
4.2 Measure This chapter starts with a mapping of the instruments used to determine the reference value for all
gathered samples in this project, see Table 3. A description of the data collection then follows.
Since there are more than 40 MIs in the assigned processes, the data collection will be summarized
based on the characteristic that each MI is intended to measure, namely thickness, weight, width,
angle and HI Pot test. Observe that once data from a certain MI is collected, the analysis was
conducted, see 4.3 Analyze, which resulted in improvement suggestions as in Table 7. When
improvement was performed, further data was collected to evaluate the improvement result which
followed by further analysis and improvement suggestions. This workflow proceeded until the
performance of each MI reached an acceptable level. The sample size chosen when studying each
measurement depends on the availability of staff, material, machine and verifying instruments.
Table 3: Measurement equipment used to measure reference value.
Measurement
Instrument Function
Image Dimension
Measurement System
(IDMS)
The IDMS resembles a microscope, but with a screen and an auto-calibration
function. By taking multiple high resolution 2D-pictures on the measured part, it can
be used to measure different type of geometrical dimensions such as length, angle,
or area.
Coordinate-Measuring
Machine (CMM)
The CMM is a device with extremely high precision used to measure the physical
geometrical characteristics. Equipped with a probe, it creates surfaces along the x, y-
and z-axis where the distance between the surfaces can be determined.
Analytical Scale A calibrated scale that has a precision of four decimals. Maximum weight is 230 g.
Calibrated Scale A calibrated scale that has a precision of four decimals. Maximum weight is 6000 g.
Standard Weight Blocks Cylindrical Standard Weights of 50, 100 and 500 g.
Standard Gauge Blocks Blocks with a known thickness of 130, 180 and 230 µm and 60, 70, 80, 90 and 100
mm
33
4.2.1 Thickness Measurements The thickness measurements in the production consists of MIs with different measuring
mechanism. Therefore, it required different approaches when studying the performance of these
gauges.
TF Laser Gauge – Calendering
Standard Gauge Blocks were used when assessing the reading of the thickness from Calendering.
Three Gauge Blocks with the known thickness of 130, 180 and 230 µm was placed under the TF
Laser Gauge. The reading was conducted on 30 positions on each Gauge Block and the result was
then summarized in Excel. This procedure was conducted for Calendering of both anode and
cathode.
Contact Sensor – Hot Press
Standard Gauge Blocks and a Master Sample were used to evaluate the reading. Since the
individual thickness of each Standard Gauge Block fell below the minimum allowed limit of the
Contact Sensor, a compromise was made by putting two Standard Gauge Blocks with different
thickness together, which resulted in eight different combinations of Standard Gauge Block pairs.
The CMM was used to measure the thickness of the Master Sample and the Standard Gauge Block
pairs at ten different positions. The average reading from the CMM was used as reference value.
Consequently, the Hot Press Contact Sensor did measure the thickness of the samples at the same
ten positions.
4.2.2 Weight Measurements In the weight assessment, certain scales did not manage to measure the intended Standard Weights
due to the scale design. Therefore, Standard Gauge Blocks were used for certain scales. Prior to
measurement, the weight of both Standard Weights and Standard Gauge Blocks was controlled
with an Analytical Scale. Each Standard Weight and Standard Gauge Block was measured ten
times and their average value was used as reference value.
Electronic scale – Hot Pressing
Two Standard Weights and three Standard Gauge Blocks were used to assess the reading from the
Hot Press scale. Each part was weighted ten times.
Electronic scale – Electrolyte Filling 1 and 2
The three remaining scales in Electrolyte Filling were studied by using five different Standard
Gauge Blocks, since the Standard Weights did not fit due to the scale design. Each part was
weighted ten times.
34
4.2.3 Dimension Measurements There are several steps in the production where the dimension of different objects is measured. For
this type of measurement, CCDs were used.
CCD 3 & 4 – Notching & Slitting
To obtain the reading from the CCDs in the Notching & Slitting Machine, a sample with ten
reading positions was prepared by the responsible operator. To obtain the reference value, an
IDMS was used to measure these ten positions. By comparing the CCD with the IDMS
measurement, Appropriate adjustment suggestions could be made. The second sample with 20
reading positions was prepared to the adjustment and followed by a third sample with 20 other
reading positions. This procedure was conducted for both cathode and anode.
CCD 1 & 2 – Electrode Cutting & Stacking
Ten different samples were collected from anode and cathode respectively. On each of these
samples, six different measures with the IDMS were taken. Data from the concerned CCD were
provided by the responsible technician which enabled comparison.
CCD 1 & 2 – Tab Pre-welding
The welded ear height was measured with an IDMS on ten different samples for both anode and
cathode. A result was achieved by comparing with data from the two CCDs in the manufacturing
by measuring anode and cathode respectively.
CCD Upper & Lower – Insulation Film Wrapping
To assess the reading from the CCD in the Insulation Film Wrapping Machine, ten battery cells
wrapped with film were prepared. Together with responsible operators, six measuring positions
for upper and lower taping position respectively were decided. The taping position was measured
with an IDMS. Production data was provided by operator. Consequently, the IDMS measured the
same six positions, where the result was used as reference value.
4.2.4 HI Pot Test To assess the capability of the four HI Pot Test in the production, the study was conducted by
using inhouse made samples. The sample consisted of five G- and five NG-cells, where each cell
went through the HI Pot Test three times.
4.2.5 Angle Measurements After the Tab Welding Machine, the lid angle of the samples was measured by using the IDMS.
Ten samples were collected for both cathode and anode.
35
4.3 Analyze The analyze phase began with establishing acceptance criteria when assessing the MIs. The criteria
are determined mainly on existing knowledge from 0. 3 Theoretical Framework. In addition, the
analyze criteria used for each type of MI were also chosen based on the availability and
characteristic of data. For instance, there are some processes where it is impossible to obtain
multiple readings on the same sample, which limited the MS evaluation concerning repeatability.
Furthermore, as historical data is not available, the standard deviation of each process is calculated
using the reference values, which should reflect the standard deviation, at least at the time of
measurement. Once the acceptance criteria are determined, analysis of the measurement reading
was conducted. The MI results is presented individually in Table 6.
4.3.1 Analysis Strategy To detect the error related to stability, the bias must be monitored using control charts during a
longer period, including gathering of multiple samples. Due to the number of MIs that are needed
to be studied, the time frame does not allow to investigate the variation in stability. According to
Montgomery and Woodall (2008), the drift in bias over time can be a result of machinery warm-
up effects, shift in environment or change in operating procedures. However, since the studied MS
is in a controlled environment with constant climate conditions and limited human access, it is
reasonable to believe that these factors will not have a significant effect on the MS. The stability
error can therefore be assumed to be zero and hence, the analysis will not consider the error of
stability. Consequently, the analyze criteria will only be based on the existence of bias and linearity
and the error of repeatability and reproducibility.
Since bias refers to the differentiation of averages between the measured data and the reference
value of the measured part, it is logical to assume that bias does not exist in a measurement when
the difference is not statistically significantly differed from zero. Likewise, since linearity indicates
the difference between the observation and the reference value for different ranges, a conclusion
about the non-existence of linearity can be made if the slope of the fitted regression line on the
bias is not significantly differed from zero. If a linearity problem is not statistically proven, the
average bias can be assessed since the individual bias is assumed to be equal across the range of
study. However, if the linearity problem is significantly present, the size of bias is therefore
assumed to vary across the range of study, hence the individually bias should be assessed instead.
By using Minitab, the study of bias and linearity can be conducted. If the slope and the average
bias is less than 0,05, a conclusion can be made with 95 % confidence that the size of the slope
and the differentiation between the averages of the measured data and the reference value of the
measured part are not differed from zero. AIAG (2010) and the Northvolt argue how a 95 %
confidence interval is a sufficient coverage factor and is established as an industry standard.
Consequently, a 95 % confidence interval is used.
36
As the MS environment remains controlled and constant over time and the measurement of a
sample is conducted automatically, it is appropriate to argue that the condition in which the
measurement is performed is constant. An assumption that the condition is not accountable for the
variation in the measuring reading conducted by the MS is thereby made. The reproducibility can
therefore be considered insignificant. Consequently, the entire variation that has acquired from
measuring the same part repetitively therefore relates to the repeatability. The software Minitab
compares this repeatability variation to the tolerance of the part, which indicates the MS precision-
to-tolerance, P/T, which according to AIAG (2010) should not be greater than 10 %. Furthermore,
Minitab can also calculate the number of distinct levels of categories, SNR, which should be
greater than five (Burdick et al., 2003; AIAG, 2010). The result from Minitab is compared with
the Acceptance Criteria in Table 4 to determine the MS performance.
Table 4: Acceptance Criteria for Numerical Data
NUMERICAL DATA Criteria Acceptance
Linearity p-value ≥ 0,05
%Linearity %Linearity ≤ %Bias
(Average/Linearity) Bias p-value ≥ 0,05
(Average/Linearity) %Bias %Bias ≤ %Linearity
Precision-to-tolerance P/T ≤ 10 %
Signal-to-noise SNR ≥ 5
When conducting the AAA, the Kappa statistic value is used. Since each attribute assessment in
Northvolt is conducted automatically by one single MI, there is no need to study the agreement
among the appraisers, i.e. reproducibility. However, it is of interest to study how well the appraiser
agrees with itself, i.e. repeatability, as well as with the reference value, i.e. bias. Based on the
AIAG (2010) recommendation, the acceptance criteria can be formulated according to Table 5.
Table 5: Acceptance Criteria for Attribute Data
ATTRIBUTE DATA
Criteria Acceptance
With Appraiser Kappa ≥ 75 %
Appraiser vs Reference Kappa ≥ 75 %
37
Since the processes are in the commissioning phase, they are not stable and the variation from each
process varies. In order to keep an overview over the MI improvement as well as to answer SQ2,
the contribution of the measurement error in each process variation will be calculated using the
tolerance of each process parameters, which can be considered as the allowed variation determined
by the case company. This contribution will be presented as the ratio %Error, which will be
calculated according to the formula below. Due to the confidentiality, neither the process tolerance
nor the measurement error will be presented in this study.
%𝐸𝑟𝑟𝑜𝑟 =𝑆𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝐷𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛 𝑜𝑓 𝑀𝑒𝑠𝑢𝑟𝑒𝑚𝑒𝑛𝑡 𝐸𝑟𝑟𝑜𝑟
𝑈𝑝𝑝𝑒𝑟 𝑇𝑜𝑙𝑒𝑟𝑎𝑛𝑐𝑒 − 𝐿𝑜𝑤𝑒𝑟 𝑇𝑜𝑙𝑒𝑟𝑐𝑎𝑛𝑐𝑒
Additionally, AIAG (2010) agrees that it is necessary to assess the MS based on the feature
tolerance, which the ratio %Error does. According to AIAG (2010), the variation from a well-
functioning MS used for product control, must be relatively small compared to the specification
limits. This implies that the smaller the ratio, the better the MI has improved. Observe that this
ratio is similar to the P/T ratio which instead is used as acceptance criteria. However, the %Error
ratio includes the total variation in measurement error, and hence does not seem to provide
information about source from which the variation is coming from. Therefore, this ratio appears
to be more useful to track the overall improvement in a certain MI, and to provide an answer to
SQ2.
38
4.3.2 Analysis of Measurement Readings The analysis result is presented in Table 6 below. However, the complete analysis can be found in
Appendix I – Complete Analysis Results. To distinguish between different data, the data taken from
the MS is called “Measurement” and the reference value is called “Reference”. In between every
sampling, the results are analyzed, and necessary calibrations are performed for further
improvement. This loop proceeds until an adequate result is obtained.
Table 6: Result of Analysis
Process Measurement
Instrument Characteristic Measurement Sampling Result %Error Remark
Calendering
Anode TF Laser Gauge Thickness 1 1st Bias and linearity 15,4 % Bad precision and bad accuracy
Calendering
Cathode TF Laser Gauge Thickness 2 1st Bias 2,2 %
Good precision and bad accuracy.
Bias size 0,43
Notching & Slitting
Anode
CCD #3
Pancake Coated
Width
3
1st Bias 9,1 % Bias size -0,245
2nd Linearity 9,1 %
3rd Bias 9,4 % Bias size 0,0185
CCD #4 4
1st Linearity 22,9 %
2nd Bias and linearity 12,2 %
3rd Bias 40,6 % Bias size -0,026
Notching & Slitting
Cathode
CCD #3
Pancake Coated
Width
5
1st Bias 8,1 % Bias size -0,187
2nd Bias and linearity 11,2 %
3rd Bias and linearity 10,5 %
CCD #4 6
1st Linearity 9,7 %
2nd Linearity 9,2 %
3rd Linearity 12,5 %
Electrode Cutting
Anode CCD #1
Upper Width 7
1st Linearity 32,4 %
2nd Linearity 23,5 %
3rd Linearity 30,4 %
Coating Depth 8 1st Bias and linearity 15,1 %
2nd Bias and linearity 10,9 %
Tap Position 9
1st Bias 39,1 % Bias size 0,039
2nd Bias 30,0 % Bias size -0,247
3rd Bias 20,6 % Bias size -0,143
39
Process Measurement
Instrument Characteristic Measurement Sampling Result %Error Remark
CCD #2 Lower Width 10
1st Bias and linearity 31,6 %
2nd Bias and linearity 16,3 %
3rd Bias and linearity 9,7 %
CCD #1 & #2 Average Length 11
1st Bias and linearity 18,0 %
2nd Linearity 75,0 %
3rd Bias 10,0 % Bias size 0,222
Electrode Cutting
Cathode
CCD #1
Upper Width 12
1st Bias 9,6 % Bias size -0,154
2nd Bias 12,3 % Bias size 0,017
3rd Bias 12,9 % Bias size 0,190
4th Bias 16,8 % Bias size -0,07
Coating Depth 13 1st Bias and linearity 11,1 %
2nd Bias and linearity 8,8 %
Tap Position 14
1st Bias 10,4 % Bias size -0,03
2nd OK 10,8 %
3rd Linearity 22,3 %
4th OK 8,4 %
CCD #2 Lower Width 15
1st Bias 8,2 % Bias size -0,076
2nd Bias 7,4 % Bias size -0,019
3rd Bias 8,8 % Bias size -0,043
4th Bias 9,7 % Bias size -0,032
CCD #1 & #2 Average Length 16
1st Bias 3,5 % Bias size -0,075
2nd OK 5,7 %
3rd Linearity 14,6 %
4th Bias 9,4 % Bias size 0,204
Hot Pressing Electronic Scale Weight 17 1st Bias 0,12 % Bias size 0,0076
Contact Sensor Thickness 18 1st Bias and linearity 64,6 %
US Pre-Welding
CCD Anode Ear
Dimension 19
1st Linearity 135,8 %
2nd Linearity 48,5 %
CCD Cathode Ear
Dimension 20
1st Bias and linearity 110,0 %
2nd Linearity 76,2 %
HiPot Test Insulation 21 1st OK
Tab Final Welding CCD Lid Angle 22
1st Linearity 237,6 %
2nd Linearity 67,0 %
HiPot Test Insulation 23 1st OK
Insulation Film
Wrapping Upper CCD
Upper Taping
Position 24 1st Bias and linearity 28,4 %
40
Process Measurement
Instrument Characteristic Measurement Sampling Result %Error Remark
2nd Bias and linearity 22,5 %
3rd Bias and linearity 5,9 %
4th Bias 6,4 % Bias size 0,004
Lower CCD Lower Taping
Position 25
1st Linearity 25,3 %
2nd Linearity 26,9 %
3rd Linearity 7,5 %
Electrolyte Filling 1
Electronic Scale Incoming Cell
Weight 26 1st OK 0,0016 %
Load Cell Outgoing Cell
Weight 27
1st Bias and linearity 0,21 %
2nd OK 0,003 %
Electrolyte Filling 2 Electronic Scale Final Cell Weight 28 1st OK 0,0011 %
41
4.4 Improve Apart from bias, certain MIs initially showed a high degree of linearity. This turned out to be
manageable with appropriate calibrations. Subsequently, no recommendations concerning
investments in MI upgrades are presented. The improvement was divided in three scenarios based
on the encountered conditions of each MI. Specific proposals connected to each scenario are
presented and motivated. The proposals lead to specific actions, which can be found in Table 7.
Scenario 1: Bias and linearity issues
There are analysis results that indicate linearity problem is dominant and responsible for the larger
part of the measurement error. The size of bias varies as the size of the measured part varies, for
example in measurement 49 where the bias increased as the weight of Standard Gauge Blocks
increases. This scenario entails more thorough work and deep understanding in order to
successfully calibrate. The most effective approach would be to study each deviant data point in
depth to find the root cause and be able to exclude this data. Another method would be to use
Master Samples in different sizes to calibrate the MI to ensure it behaves similarly for all sizes.
Once linearity issues are solved, the bias issues can be assessed.
Scenario 2: Bias issues
Existence of bias does not necessarily mean a linearity problem. There are analyzes that prove bias
issues are dominant and account for the larger part of the measurement error, where the data
illustrates gaps between the measurement and the reference values, although both curves share the
similar pattern. The bias issues can be solved by considering the difference and compensate the
MI reading. This can be performed differently depending on which MI that is addressed. For other
MIs, the solution could be reprogrammed by adding or subtracting an offset with the same size as
the bias. Regarding the CCDs, such adjustments can be done by multiply the bias ratio with the
current pixel size resolution of the CCD. The ratio is calculated as:
𝐵𝑖𝑎𝑠 𝑟𝑎𝑡𝑖𝑜 =∑
𝑅𝑒𝑓𝑒𝑟𝑒𝑛𝑐𝑒𝑖
𝑀𝑒𝑎𝑠𝑢𝑟𝑒𝑚𝑒𝑛𝑡𝑖
𝑛𝑖=1
𝑛
Once the difference is compensated, the gap between the two values should disappear and the
measurement data will align with the reference value. As the analysis reveals, many instruments
are initially simply not sufficiently calibrated to perform an adequate measurement, which in turn
creates the gap that this proposal manages to reduce.
Scenario 3: “All good”
As previous scenarios flow like a loop of analysis and improvement, the loop ends in this scenario
where further improvement is considered unnecessary. Firstly, this depends on the highest
accuracy that the addressed MI can provide. Secondly, the resources used for improvement exceed
the benefits that the improvement generates. This is highlighted in the theory of cost of quality,
see 3.4 Cost of quality.
42
Table 7: Improvement Suggestions
MEASUREMENT SAMPLING ISSUE ACTION
1 1st Bias and linearity Change mechanical measuring part, waiting for arrival.
2 1st Bias Change mechanical measuring part, waiting for arrival.
3
1st Bias Multiply pixel size resolution with bias ratio (1,001492)
2nd Linearity Calibration using master sample
3rd Bias Reach the limit of accuracy. Need to be replace with other type.
4
1st Bias pixel size resolution with bias ratio (0,9971)
2nd Linearity Calibration using master sample Multiply
3rd Bias Reach the limit of accuracy. Need to be replace with other type.
5
1st Linearity Double checked the measurement positions of the width
2nd Bias and linearity Calibration using master sample Multiply
3rd Bias Reach the limit of accuracy. Need to be replace with other type.
6
1st Linearity Calibration using master sample
2nd Linearity Calibration using master sample
3rd Linearity Reach the limit of accuracy. Need to be replace with other type.
7
1st Linearity Check root cause. Calibration using master sample*
2nd Linearity Calibration using master sample*
3rd Linearity **
8 1st Bias and linearity Check root cause. Calibration using master sample*
2nd Bias and linearity **
9
1st Bias Check root cause. Multiply pixel size resolution with bias ratio (1,0066)*
2nd Bias Multiply pixel size resolution with bias ratio (1,0404)
3rd Bias Multiply pixel size resolution with bias ratio (1,0245)
10 1st
Bias and linearity Double checked the measurement positions. Calibration using master
sample*
2nd Bias and linearity Calibration using master sample*
3rd Bias and linearity **
11
1st Bias and linearity Calibration using master sample*
2nd Linearity Double checked the measurement positions
3rd Bias Multiply pixel size resolution with bias ratio (0,9987)
12
1st Bias Multiply pixel size resolution with bias ratio (1,00243)*
2nd Bias Multiply pixel size resolution with bias ratio (0,9997)
3rd Bias Multiply pixel size resolution with bias ratio (1,0030)
4th Bias Multiply pixel size resolution with bias ratio (1,0011)
43
MEASUREMENT SAMPLING ISSUE ACTION
13 1st Bias and linearity Calibration using master sample*
2nd Bias and linearity **
14
1st Bias Multiply pixel size resolution with bias ratio (0,9957)
2nd OK
3rd Linearity Calibration using master sample
4th OK *
15
1st Bias Multiply pixel size resolution with bias ratio (1,0012)
2nd Bias Multiply pixel size resolution with bias ratio (1,0009)
3rd Bias Multiply pixel size resolution with bias ratio (1,0007)
4th Bias **
16
1st Bias Multiply pixel size resolution with bias ratio (1,0005)*
2nd OK
3rd Linearity Calibration using master sample
4th Bias Multiply pixel size resolution with bias ratio (0,9993)**
17 1st Bias Bias size 0,0076. Relatively small since only contribute to 0,12% of the
product specification. Action determined to accept the performance.
18 1st Bias and linearity Change mechanical measuring part, waiting for arrival.
19 1st Linearity Changing measurement mechanism***
2nd Linearity Not critical. Accept the linearity error.
20 1st Bias and linearity Changing measurement mechanism***
2nd Linearity Not critical. Accept the linearity error.
21 1st OK
22 1st Linearity
Check the root cause. CCD mechanism only checks the acute angles which
causes unalignment between reference and measurement data. CCD was
reprogrammed to align with reference data despite whether the angle is
acute or obtuse.
2nd Linearity Not critical. Accept the linearity error.
23 1st OK
24
1st Bias and linearity Double checked the measurement positions
2nd Bias and linearity
In consultation with operators, a decision that one CCD measure the upper
taping position at three points on one side, and another CCD doing the
same for the other side was made.
3rd Bias and linearity Calibration using master sample
44
MEASUREMENT SAMPLING ISSUE ACTION
4th
Bias Bias size 0,004. Error relatively small comparing to the product tolerance.
The CCD is accepted
25
1st Linearity Double checked the measurement positions
2nd Linearity
In consultation with operators, a decision that one CCD measure the lower
taping position at three points on one side, and another CCD doing the
same for the other side was made.
3rd Linearity Accept the linearity problem.
26 1st OK
27 1st Linearity and bias Reprogramming the load cell.
2nd OK
28 1st OK
* The CCD could not distinguish between the electrodes and conveyer because both were black.
Consequently, the conveyers were changed from black color to transparent.
** CCD used for many different dimensions. Calibration based on one dimension affect result of
other dimension measurement. Action was taken to reduce the amount of dimension measurement
and focus on dimensions critical for quality.
*** As all tabs are partially bent after the pre-welding, and as it was measured by being fixed
between two glass plates which straightened it in the production, the tab height naturally differed
when measuring the reference values. Since this setting was difficult to recreate, one of the plates
was removed to give similar measurement conditions with the reference value.
45
4.5 Control For this Master Thesis project, one key result is the improved MIs, which has been progressively
implemented during the project and hence did not require an implementation plan. Therefore, the
Control phase of this Master Thesis instead is designed as a control plan for how Northvolt in the
future should perform MSA to ensure and maintain the performance of the MS.
The control plan can be used to evaluate and improve the MS in the commissioning phase of
Northvolt Ett. In addition, it can also be used to monitor and evaluate the performance of the
existing MS in Northvolt Labs, Northvolt Battery Systems Jeden and Northvolt Zwei. For the
monitoring purpose, the frequency of MSA must be agreed upon with the customers. As mentioned
in 3.6 Measurement System Analysis in ISO 9001:2015, activities referring to calibration,
verification or maintenance should be regularly conducted and properly documented in order to
ensure the product quality and the measurement traceability.
It is recommended that the MSA activities are performed by a cross-functional team due to the
extent of the necessary competencies listed below:
- Process competence – an understanding of the critical process control parameters that are
monitored in the MS.
- Machine competence – an understanding of how the machine functions and how to extract
samples for control.
- MS competence – an understanding of how each MI perform the measurement of a certain
control parameter, as well as how to adjust or calibrate the MI.
- Quality control competence – an understanding of how to measure the sample and training in
how to handle specific lab equipment
- Statistical competence – to performance statistical analysis such as Gauge R&R or Bias &
Linearity analysis.
By utilizing a cross-functional team, it is expected that the knowledge listed in Table 8: Important
understanding prior to MSA. is identified prior to the MSA in order to ensure the effectiveness of
the following activities. Consequently, the sampling procedure for each MI is recommended to
follow Table 9 to ensure the effectiveness of sampling and completeness of data. The analysis
strategy and decision-making approach is recommended to follow the decision tree as presented
in Figure 26, which was structured as the analysis criteria that was used in this project. In this study
the statistical software Minitab was utilized. The functions of the software include Gauge Linearity
and Bias study, Gauge R&R study (crossed) and Attribute Agreement Analysis were used
throughout the project. A more detailed description of the procedure will be presented in Appendix
II – Analysis Using Minitab.
46
Table 8: Important understanding prior to MSA.
Process Which process is concerned?
Process variation Historical process variation
Gauge type What type of MI?
Measurement What does the MI measure?
Measurement
mechanism How does the MI perform the measurement?
Data type Variable or attribute data?
Possibility to repetitive reading?
Tolerance The tolerance for the process control parameter
Analysis strategy Gauge R&R, Bias, Linearity, Stability, Attribute Assessment Agreement
Analysis acceptance
level To be determined by the cross-functional team
Data sampling Depends on how the MI perform the measurement
47
Table 9: Concrete sampling and analysis strategy for the MIs at Northvolt Labs.
Process Scope Gauge Measurement Sampling procedure QC verification Data Type Analysis
Strategy
Calendering Anode/Cathode TF Laser
Gauge
Jumbo Roll
thickness
Using at least 5 Standard Gauge Blocks
with significantly different thickness. Let
machine read at least 10 times in random
order.
Verify thickness of the Gauge
Blocks by using CMM at ten
different positions. Calculate
average.
Variable data
with
replication
Gauge R&R
Bias & Linearity
Notching &
Slitting Anode/Cathode
CCD Top Jumbo Roll
coated width 20 samples marked with the detected
position. Stored pictures from CCD to
ensure the same positions measured in
Quality Control
Verify by using an IDMS at the
same position as the CCD. Refer to
pictures if uncertain.
Variable data
without
replication
Bias & Linearity CCD Bottom Coating depth
CCD #3 & #4 Pancake coated
width
Electrode
cutting Anode/Cathode CCD #1 & #2
Electrode
dimension
At least 10 samples, repeating 3 readings
on each sample.
Verify by using an IDMS at the
same position as the CCD. Refer to
pictures if uncertain.
Variable data
with
replication
Gauge R&R
Bias & Linearity
Stacking JR CCD Electrode
alignment
3 samples from each stacking station.
Carefully mark each JR after stacking
and follow it till pre-welding. Remove
and hand over to Quality Control.
CT scan
Hot
pressing JR
Electronic
scale JR weight
Using at least 5 different Standard
Weight Block, alternatively Standard
Gauge Block or Master Sample with
known weights. Let the scale reads at
least 10 times in randomly order.
Verify the weight of the object by
using a calibrated scale. Read ten
times in random order. Calculate
average.
Variable data
with
replication
Gauge R&R
Bias & Linearity
Contact
sensor JR thickness
Use at least 5 Standard Gauge Blocks
with significantly different thickness. Let
machine read at least 10 times in random
order.
Control thickness of the Gauge
Blocks using CMM at ten different
positions. Calculate average.
Variable data
with
replication
Gauge R&R
Bias & Linearity
US Pre-
welding JR
CCD Anode/Cathode
Tap height
At least 10 samples, repeat 3 readings on
each sample.
Control by using an IDMS and
measure the same position as CCD.
Refer to pictures if uncertain.
Variable data
with
replication
Gauge R&R
Bias & Linearity
HI Pot Test Insulation 10 G and 10 NG samples. Read 3 times
each in random order. N/A Attribute data
Attribute
Assessment
Agreement
Tap Final
Welding JR
CCD Taping Position 10 G and 10 NG samples. Read 3 times
each in random order. N/A Attribute data
Attribute
Assessment
Agreement
CCD Lid Angle
Make sure that the CCD reads the angle
from the inside angle, despite ocute or
acute angles. At least 10 samples,
repeating 3 readings on each sample.
Control by using an IDMS and
measure the same position as CCD.
Refer to pictures if uncertain.
Variable data
with
replication
Gauge R&R
Bias & Linearity
48
Process Scope Gauge Measurement Sampling procedure QC verification Data Type Analysis
Strategy Insulation
Film
Wrapping
JR Upper/Lower
CCD Tapping Position
At least 10 samples (30 reading position
on each side), repeating 3 reading on
each sample.
Control by using an IDMS and
measure the same position as CCD.
Refer to pictures if uncertain.
Variable data
with
replication
Gauge R&R
Bias & Linearity
Can
Insertion Cell HI Pot Test Insulation
10 G and 10 NG samples. Read 3 times
each in random order. N/A Attribute data
Attribute
Assessment
Agreement
Lid Laser
Welding Cell
HI Pot Test Insulation 10 G and 10 NG samples. Read 3 times
each in random order. N/A Attribute data
Attribute
Assessment
Agreement
Laser
Displacement
Sensor
Cell Height 3 Samples, 10 positions. 3 readings on
each position. Randomly if possible
Variable data
with
replication
Gauge R&R
Bias & Linearity
CCD Weld seam
inspection Attribute data
Attribute
Assessment
Agreement
Electrolyte
filling #1 Cell
Electronic
Scale
Incoming Cell
Weight
Using at least 5 different Standard
Weight Blocks, alternatively Standard
Gauge Blocks or Master Samples with
known weights. Let the scale read at least
10 times in random order.
Control the weight of the object by
using a calibrated scale. Read ten
times in random order. Calculate
average.
Variable data
with
replication
Gauge R&R
Bias & Linearity Load Scale
Outgoing Cell
Weight
Electrolyte
filling #2 Cell
Electronic
Scale Final Cell Weight
Using at least 5 different Standard
Weight Blocks, alternatively Standard
Gauge Blocks or Master Samples with
known weights. Let the scale read at least
10 times in random order
Control the weight of the object by
using a calibrated scale. Read ten
times in random order. Calculate
average.
Variable data
with
replication
Gauge R&R
Bias & Linearity
49
Figure 26: Decision tree for the MSA
50
5 Conclusion In this section, the study results are concisely described by the answering of each SQ. An
evaluation of the fulfillment of the study purpose is also presented.
This study has aimed to improve the performance of a MS in the battery production at Northvolt
Labs by utilizing MSA, and to make appropriate recommendations for improvement and future
control. This has been achieved by the creation of an MSA framework consisting of five different
measurement errors used to analyze. The analysis resulted in three scenarios, where specific
proposals and actions were suggested and motivated to each scenario. Also, a control plan to serve
as a basis for future work within MSA was presented. This plan could be used to evaluate and
improve the MS in the commissioning phase of Northvolt Ett and other factories. All this was
encapsulated in the stage-model and improvement methodology DMAIC. By following the
suggested recommendations and implementing the control plan, estimated savings is calculated to
395 000 SEK annually.
To achieve the aim of the study, the purpose was divided in three SQs which now have been
answered in regard to Northvolt.
SQ1 How can the measurement system be evaluated?
By identifying five measurement errors from the literature, namely bias, linearity, stability,
repeatability, and reproducibility, which were later categorized into precision or accuracy, a
framework was created. As the investigated MIs differed due to their different measuring
mechanisms, they also had to be evaluated with different approaches. This means that the basis of
analyze occasionally differed. Nevertheless, every single MI was studied using the concept of the
five mentioned characteristics. As the five characteristics captured the entire measurement error,
the framework enabled a proper analyze of every MI involved, which proved how diverse and
adaptive the framework can be. On the other hand, it has been difficult to perform a repeatability
study on certain MIs as the measured samples were not replicable. This was due to the fact that
samples are cut or notched directly after a measurement with no possibility to cancel the process.
A conclusion could be made that the framework of precision and accuracy presented in this study
can be used to evaluate the MS.
SQ2 How much of the variation in each process is due to the measurement system at?
Since the studied processes were not stable, the data regarding process variation cannot be used to
determine how much of the process variation measured by the MI derives from the MI itself.
However, this is possible to calculate based on the process tolerance, which can be interpreted as
the ideal process variation that is determined by the case company. This ratio is abbreviated as
%Error and the result can be found in Table 6. In Figure 29 a selection of certain MIs and the
improvement the performed calibrations have generated is presented. It is apparent that the %Error
has decreased after calibrations.
51
Figure 27: MI improvement measured in %Error
SQ3 How can the measurement system be monitored to ensure its performance?
As many experiences were obtained during the project, valuable knowledge regarding MSA at
Northvolt lays a foundation for a control plan presented in Table 9. Since an MSA is as important
now as it would be in a future perspective, the control plan undertakes the future responsibility of
how an MSA should be performed.
One important finding is the multiple factors the control plan highlights and must take into
consideration in order to establish and maintain a well-performing MS. This is emphasized by
Table 8, which clarifies the significance of diverse and profound understanding regarding
processes, measurements and statistical knowledge. Subsequently, skills in how to extract and
measure samples, in utilization of analytical software, in conducting comprehensible compilations
of the results and how to calibrate the MS in an appropriate way are essential. Apart from deep
knowledge and understanding of MSA, a project like this also requires cooperation and
customization to progress and be successfully performed. In fact, often certain people have deep
knowledge of only one or a few certain MIs or utilization of specific lab equipment to obtain a
reference value. This demonstrates exactly how complex and multifaceted an MSA is or can be.
To master these challenges, an establishment of a cross-functional team with the proposed
competencies as in 4.5 Control would be a suitable approach. This set up enables communication
and structure meanwhile it is easy to include all competencies necessary.
MI7 MI8 MI9 MI10 MI11 MI13 MI14 MI18 MI19 MI20 MI21 MI22
Before 32,4 15,1 39,1 31,6 18 11,1 10,4 135,8 110 237,6 28,4 25,3
After 30,4 10,9 20,6 9,7 10 8,8 8,4 48,5 76,2 67 6,4 7,5
0
50
100
150
200
250
%E
rro
r [%
]Selection of MI improvement
Before After
52
Fulfillment of Purpose
By conducting MSA on the MS in the battery production at Northvolt Labs, the obtained data of
the calibrations performed on each involved MI has shown a significant improvement. The MSA,
as a Six Sigma tool, has proven the potential to identify and quantify the existing measurement
errors. This made it possible to provide improvement recommendations as well as track the
improvement result of each MI. Knowledge concerning MSA activities were obtained and
summarized in a plan for future control. Apart from answering each SQ properly, this study also
manages to deliver solutions that is not only adjusted to the case company but also can be applied
more generally. Thereby, the presented answering of the SQs along with the fulfillment of the
study purpose may be considered successfully achieved.
53
6 Discussion In this section, the validity and reliability of both methodology and data gathering are discussed.
The study contribution is also described from a theoretical and practical aspect. Lastly,
recommendations of further studies within this area of research is presented.
6.1 Validity and Reliability of Method In agreement with 2.4 Creditability of Research Findings, the choice of DMAIC as research
methodology has been successful since it has provided structure and basis to this study. Firstly, as
DMAIC includes certain predetermined elements, the way forward has always been rather clear.
Secondly, these elements have highlighted several important results that otherwise not likely had
been presented. Altogether, the DMAIC methodology has created a complete contribution to the
stated purpose in a standardized way, which increases the validity and reliability of the research.
The presented concept of MSA composed a framework for evaluation, which has strong support
from academic researchers. By utilizing the framework, the results capture the improvement in
performance of the MS in terms of precision and accuracy. Although, one problem has been to
know where to set the limit of a well-performing MI. There are MIs that were directly approved
according to the framework. On the other hand, this scenario does not mean perfection. As there
is no certain approval criteria regarding MSA, specifically for battery industry, this limitation was
determined primarily with respect to the standard of the manufacturing industry, but also the cost
of quality see 3.4 Cost of quality. This limit has been difficult to estimate and needs to be discussed
among concerned people in order to gain multiple perspectives and lead to an adequate decision.
Moreover, there have been several cases where it required multiple samples to reach an adequate
level of precision and accuracy of each MI. Subsequently, a substantial amount of time has been
spent. Although a satisfying result, the journey prior to this has been rather protracted. The key
factor is the calibration stage, where more accurate calibration actions would have streamlined the
whole improvement process in a timely sense. As the calibrations are objectively based on the
result of data analysis, there are no further improvement proposals to minimize the number of
calibrations. However, even though time might be a crucial factor, several stages of calibration
contrariwise enabled a deeper understanding of each MI in sense of how it works and how it reacts
to different settings and conditions.
Nevertheless, all MIs included in this study indicates improvement. In fact, the analysis shows that
the majority of the MIs are improved substantially. This study, however, does not enclose the
actual reason for this. Have the investigated MIs been in unfavorable initial settings, considering
the commissioning phase of the case company? Or have the MIs facilitated the MSA work by
simply being easy to work with? As far as this study goes, the chosen approach and methodology
for the MSA have enhanced the creditability of this study.
54
The risk for the MIs to deteriorate in performance after we finish the project must also be
considered. As Northvolt is in a commissioning phase, no historical experiences regarding the MIs
are available at this point. If the time frame of this study had enabled to also evaluate stability, the
performance over time would have been monitored, and subsequently the recommendations would
have been adjusted to this. Instead, the presented control plan takes time into consideration as it
focuses on MSA in a futuristic sense. In fact, we believe the control plan, along with the whole
control phase of this project, has the potential to continuously keep the MS performance at a
satisfactory level.
6.2 Validity and Reliability of Data The gathering of data has been a continuous and time-consuming task through this work. A
contributing factor is that each of the samplings that has been performed is with regard to the
availability of supplier support, that the machinery is fully functioning, a mutual understanding
between the lab and the production of how the investigated MI is measuring, and beyond this a lot
of time and workload spent in order to provide the actual samples. Subsequently, much time and
high costs are included in the production and enhanced the preciousness of the samples. In turn, it
was difficult and often impossible to provide any sort of repeatability of the measurements. This
has highlighted only a fraction of the framework’s potential, where Gauge R&R only could be
utilized in certain MIs.
The sample size chosen for studying each MS was determined by the availability of multiple
factors. Firstly, the responsible technicians must be available. Secondly, the machine in question
needs to be isolated and the process to be stopped to extract samples. Thirdly, the equipment used
to measure the reference values need to be available. Since there were several on-going projects at
the case company simultaneously, the access was limited. When considering this from a cost of
quality perspective, based on the PAF model, the appraisal cost can be estimated as higher than
the benefit of taking larger sample size. This is because it interferes and causes delay for other
projects as well as consuming time to measure the reference value.
Referring to 3.2.1 Accuracy and the narrow time frame of this study, the measurement error
stability was never included in the analysis. With the possibility to monitor the variation of bias
over time, the outcome of this project may have differed substantially, as time is not a factor that
this study has been allowed to encapsulate. In other words, time possibly could have had major
effects on the improvement suggestions and the control plan. All MIs in general, and certain CCDs
in particular, are performing a high number of measurements as the production has a fast flow. In
a longer perspective, friction occurrence is likely which in turn possibly may affect the
measurements. However, as the production environment is considered controlled and stable due
to cleanrooms and strict environmental policies, at least the time disadvantage can be estimated to
have less influence on the study contributions.
55
Moreover, a higher level of conformance between certain MIs and the equipment used for
measuring the reference value would have been beneficial. This refers to the production scales and
the reference scale, which have had a different number of decimals. This means that there exists
an underlying bias in the analysis of the scales, even if there is a possibility that no bias actually
exists.
Since the measurement of reference data was conducted by the project group, proper training of
measurement equipment was conducted and evaluated by employees that has proper equipment
utilization. However, flaws in reference data is unavoidable. The equipment was delivered with a
calibration certificate by the supplier to justify their performance. However, the performance of
such measurement equipment should also be assessed, perhaps by using MSA.
6.3 Study Contribution While mentioning accuracy as an important aspect of the measurement system capability, neither
Montgomery (2012) or Kooshan (2012) include accuracy in their definition of measurement error,
but instead the variation regarding repeatability and reproducibility. However, there is a strong
academic support in also investigate the concept of accuracy when evaluating the MS. The results
arisen from this Master Thesis provide a conceptual framework for MSA involving both precision
and accuracy and presents differences of these two concepts, namely repeatability, reproducibility
and bias, linearity and stability, respectively. This framework therefore contributes to increase the
completeness of the existing MSA framework in literature.
Regarding Northvolt, this study encapsulates great experience and know-how of the multiple MIs
analyzed and investigated. Consequently, several practical contributions are presented. Firstly, this
project resulted in MS improvement of the battery production of the company, which was
estimated to have significant impact in terms of profit and image. Secondly, an adapted control
plan for how Northvolt can perform MSA activities in a future perspective including sampling
strategy as well as analysis and acceptance criteria, which otherwise would consume resources in
terms of time, money and human effort. Lastly, even though the project is conducted on only the
delimited processes at Northvolt Labs, the same procedures can be applied for the other production
processes at Northvolt Labs as well, as production at the other and upcoming factories.
It is also important to emphasize that the measurements, such as length, weight and insulation
capacity, are generic and can be found in other industries, and not solely battery manufacturing.
The main purpose of the framework, the control plan and the decision tree are to ensure the
completeness and effectiveness of data sampling, analysis and decision making. In other words,
the results of this study can therefore be transformed to other industries when performing MSA
without any comprehensive adjustments. Regarding the control plan, it is presented with specific
recommendations to solely battery manufacturing and must be overlooked. However, the structure
of the control plan is still applicable to other industries.
56
6.4 Recommendations for future research The MSA framework used in this study consists of five different MS characteristics, including
linearity. In this study, linearity has been defined as the change in bias across the measured range
when moving from one part to another part. However, it is logical to assume that when moving
across the measured range, the reading assumes another variation as illustrated in Figure 28. When
comparing Figure 28 with
Figure 7, the standard deviation varies across the range of measurement, rather than the size of
bias, which this study has chosen to exclude. It is therefore of interest to investigate how this type
of linearity can affect the entire measurement error.
Figure 28: Linearity which change in variation
The MI that was investigated in this study have been assumed to be capable of its intended
measurement. In other words, this study does not investigate how suitable each MI is for its
intended purpose. For example, the CCDs used in the Notching & Slitting processes seem to not
be able to deliver the precision and accuracy required by the company. A suitable area for future
research would therefore be to investigate which type of MI is suitable for respective type of
measurement.
Lastly, the choice of confidence level when conducting MSA was determined to 95 %, which
required the p-value to exceed 0,05 to not reject the hypothesis that, for example, linearity or bias
issues exist. This choice derives from the fact that a 95 % confidence level is a standard in the
manufacturing industry. However, one can argue that since battery production has more strict
requirements regarding quality and safety, it would be wiser to choose the confidence level of 99
% instead. Thus, this need to be further investigated.
57
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1 (33)
Appendix I – Complete Analysis Results Measurement 1 – Calendering - Anode
Process Calendering - ANODE
Gauge Type TF Laser Gauge
Measurement Thickness
%Error 15,4 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Individual Bias p-value
Individual %Bias
0,000
0,6 %
[0,000]
[113,6; 1810,6] %
≥ 0,05
%Linearity < %Bias
≥ 0,05
%Bias < %Linearity
Linearity problem. Assess individual Bias
Bias problem
Bias problem >> Linearity problem
Precision-to-tolerance 0,24 % OK
Signal-to-noise SNR = 4842 OK
Comment Bad precision and bad accuracy
229,5
230
230,5
231
231,5
232
232,5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Measurement using 230 µm Gage Block
Measurement Reference
179,5
180
180,5
181
181,5
182
182,5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Measurement using 180 µm Gage Block
Measurement Reference
129,5
130
130,5
131
131,5
132
132,5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Measurement using 130 µm Gage Block
Measurement Reference
2 (33)
Measurement 2 – Calendering - Cathode
Process Calendaring - CATHODE
Gauge Type TF Laser Gauge
Measurement Thickness
%Error 2,2 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Average Bias p-value
Individual %Bias
0,466
0,0 %
0,000
83,3 %
≥ 0,05
%Linearity < %Bias
≥ 0,05
%Bias < %Linearity
No linearity problem. Assess average Bias
Bias problem. Bias size 0,43
Bias problem
Precision-to-tolerance 2,25 % OK
Signal-to-noise SNR = 819 OK
Comment Good precision and bad accuracy
229,5
230
230,5
231
231,5
232
232,5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Measurement using 230 µm Gage Block
Measurement Reference
179,5
180
180,5
181
181,5
182
182,5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Measurement using 180 µm Gage Block
Measurement Reference
129,5
130
130,5
131
131,5
132
132,5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Measurement using 130 µm Gage Block
Measurement Reference
3 (33)
Measurement 3 – Notching & Slitting - Anode
Process Notching & Slitting - ANODE
Gauge Type CCD #3
Measurement Pancake coated width
1st sample
%Error 9,1 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Average %Bias
0,849
4,3 %
141,0 %
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
No linearity problem. Assess average Bias
Bias problem
2nd sample
%Error 9,1 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Average %Bias
0,232
9,7 %
3,3 %
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
No linearity problem. Assess average Bias
Linearity problem > Bias problem
3rd sample
%Error 9,4 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Average %Bias
0,514
11,4%
12,1%
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
No linearity problem. Assess average Bias
Bias problem > Linearity problem
164,30
164,40
164,50
164,60
164,70
164,80
164,90
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
Measurement Reference
2nd sample 3rd sample1st sample
4 (33)
Measurement 4 – Notching & Slitting - Anode
Process Notching & Slitting - ANODE
Gauge Type CCD #4
Measurement Pancake coated width
1st sample
%Error 22,9 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Average %Bias
0,124
51,9 %
12,6 %
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
No linearity problem. Assess average Bias
Linearity problem > Bias problem
2nd sample
%Error 12,2 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Individual %Bias
0,027
16,9 %
[10,9; 25,6]
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
Linearity problem. Assess individual Bias
Bias problem > Linearity problem
3rd sample
%Error 40,6 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Average %Bias
0,212
81,9 %
18,1 %
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
No linearity problem. Assess average Bias
Linearity problem > Bias problem
164
164,1
164,2
164,3
164,4
164,5
164,6
1 2 3 4 5 6 7 8 9 1011121314151617181920212223242526272829303132333435363738394041424344454647484950
QC data Machine data
1st sample 2nd sample 3rd sample
5 (33)
Measurement 5 – Notching & Slitting - Cathode
Process Notching & Slitting - CATHODE
Gauge Type CCD #3
Measurement Pancake coated width
1st sample
%Error 8,1 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Average %Bias
0,144
76,9 %
299,9 %
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
No linearity problem. Assess average Bias
Bias problem > Linearity problem
2nd sample
%Error 11,2 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Individual %Bias
0,004
56,1 %
[7,7; 58,4] %
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
Linearity problem. Assess individual Bias
Bias problem > Linearity problem
3rd sample
%Error 10,5 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Average %Bias
0,138
44,8 %
15,9 %
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
No linearity problem. Assess average Bias
Linearity problem > Bias problem
161,30
161,40
161,50
161,60
161,70
161,80
161,90
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
Measurement Reference
2nd sample 3rd sample1st sample
6 (33)
Measurement 6 – Notching & Slitting - Cathode
Process Notching & Slitting - CATHODE
Gauge Type CCD #4
Measurement Pancake coated width
1st sample
%Error 9,7 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Average %Bias
0,328
62,6%
7,6
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
No linearity problem. Assess average Bias
Linearity problem > Bias problem
2nd sample
%Error 9,2 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Individual %Bias
0,520
10,6%
8,5%
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
No linearity problem. Assess average Bias
Linearity problem > Bias problem
3rd sample
%Error 12,5 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Average %Bias
0,417
26,2%
15,9%
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
No linearity problem. Assess average Bias
Linearity problem > Bias problem
161,30
161,40
161,50
161,60
161,70
161,80
161,90
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
Measurement Reference
2nd sample 3rd sample1st sample
7 (33)
Measurement 7 – Electrode Cutting – Anode
Process Electrode cutting
Gauge Type CCD #1
Measurement Upper width
1st sample
%Error 32,4 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Average %Bias
0,077
103,4%
29,6%
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
No linearity problem. Assess average Bias
Linearity problem > Bias problem
2nd sample
%Error 23,5 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Individual %Bias
0,271
47,8%
1,6%
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
No linearity problem. Assess average Bias
Linearity problem > Bias problem
3rd sample
%Error 30,4 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Average %Bias
0,680
10%
8,5%
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
No linearity problem. Assess average Bias
Linearity problem > Bias problem
65,4
65,45
65,5
65,55
65,6
65,65
65,7
65,75
65,8
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
Refernce Measurement
2nd sample 3rd sample1st sample
8 (33)
Measurement 8 – Electrode Cutting – Anode
Process Electrode cutting
Gauge Type CCD #1
Measurement Coating depth
1st sample
%Error 15,1 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Average %Bias
0,033
66,9%
[8,4; 42,8]%
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
Linearity problem. Assess individual Bias
Linearity problem > Bias problem
2nd sample
%Error 10,9 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Individual %Bias
0,003
43,7%
[1,5; 25,2]%
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
Linearity problem. Assess individual Bias
Linearity problem > Bias problem
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
Refernce Measurement
1st sample 2nd sample
9 (33)
Measurement 9 – Electrode Cutting – Anode
Process Electrode cutting
Gauge Type CCD #1
Measurement Tab position
1st sample
%Error 39,1 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Average %Bias
0,980
1,0%
29,6%
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
No linearity problem. Assess average Bias
Bias problem > Linearity problem
2nd sample
%Error 30,0 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Individual %Bias
0,154
37,7%
351,8%
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
No linearity problem. Assess average Bias
Bias problem > Linearity problem
3rd sample
Standard deviation 20,6 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Average %Bias
0,431
16,6%
232,1%
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
No linearity problem. Assess average Bias
Bias problem > Linearity problem
5,7
5,8
5,9
6
6,1
6,2
6,3
6,4
6,5
6,6
6,7
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
Refernce Measurement
2nd sample 3rd sample1st sample
10 (33)
Measurement 10 – Electrode Cutting – Anode
Process Electrode cutting
Gauge Type CCD #2
Measurement Lower width
1st sample
%Error 31,6 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Average %Bias
0,087
66,8%
26,3%
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
No linearity problem. Assess average Bias
Linearity problem > Bias problem
2nd sample
%Error 16,3 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Individual %Bias
0,000
86,5%
[6,7; 26,3]%
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
Linearity problem. Assess individual Bias
Linearity problem > Bias problem
3rd sample
%Error 9,7 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Average %Bias
0,063
16,3%
12,4%
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
No linearity problem. Assess average Bias
Linearity problem > Bias problem
65,400
65,450
65,500
65,550
65,600
65,650
65,700
65,750
65,800
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
Refernce Measurement
2nd sample 3rd sample1st sample
11 (33)
Measurement 11 – Electrode Cutting – Anode Process Electrode cutting
Gauge Type CCD #1 & #2 Measurement Average lenght
1st sample
%Error 18,0 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity Average %Bias
0,022
165,6% [90,8; 188,6]%
≥ 0,05
%Linearity < %Bias %Bias < %Linearity
Linearity problem. Assess indiv idual Bias
Bias proble m > Linearity prob lem
2nd sam ple
%Error 75,0 %
Parameter Result Acceptance criteria Remark
Linearity p-value %Linearity
Individual % Bias
0,000 128,4%
[14,3; 77,8]%
≥ 0,05 %Linearity < %Bias
%Bias < %Linearity
Linearity problem. Assess indiv iual Bias
Linearity problem > Bias prob lem
3rd sam ple
%Error 10,0 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity Average %Bias
0,127%
35,2% 180,9%
≥ 0,05
%Linearity < %Bias %Bias < %Linearity
No linearity prob lem. Assess average Bias
Bias proble m > Linearity prob lem
164,2
164,3
164,4
164,5
164,6
164,7
164,8
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
Refernce Measurement
2nd sample 3rd sample1st sample
12 (33)
Measurement 12 – Electrode Cutting – Cathode
Process Electrode cutting
Gauge Type CCD #1
Measurement Upper width
1st sample
%Error 9,6 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Average %Bias
0,173
27,6%
79,1%
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
No linearity problem. Assess average Bias
Bias problem > Linearity problem
2nd sample
%Error 12,3 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Individual %Bias
0,734
11,8%
34,8%
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
No linearity problem. Assess average Bias
Bias problem > Linearity problem
3rd sample
%Error 12,9 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Average %Bias
0,300
19,3%
433,6%
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
No linearity problem. Assess average Bias
Bias problem > Linearity problem
4th sample
%Error 16,8 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Average %Bias
0,467
10,4%
94,4%
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
No linearity problem. Assess average Bias
Bias problem > Linearity problem
63,4
63,45
63,5
63,55
63,6
63,65
63,7
63,75
63,8
63,85
63,9
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Refernce Measurement
2nd sample 3rd sample 4th sample1st sample
13 (33)
Measurement 13 – Electrode Cutting – Cathode
Process Electrode Cutting - CATHODE
Gauge Type CCD #1
Measurement Coating depth
1st sample
%Error 11,1 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Average %Bias
0,000
211,9%
[105,0; 231,7]%
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
Linearity problem. Assess individual Bias
Linearity problem > Bias problem
2nd sample
%Error 8,8 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Individual %Bias
0,006
23,1%
[0,4; 4,9]%
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
Linearity problem. Assess individual Bias
Linearity problem > Bias problem
0,4
0,5
0,6
0,7
0,8
0,9
1
1,1
1,2
1,3
1,4
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
Refernce Measurement
1st sample 2nd sample
14 (33)
Measurement 14 – Electrode Cutting – Cathode
Process Electrode Cutting - CATHODE
Gauge Type CCD #1
Measurement Tab Position (Tap to Edge)
1st sample
%Error 10,4 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Average %Bias
0,513
18,3%
19,1%
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
No linearity problem. Assess average Bias
Bias problem > Linearity problem
2nd sample
%Error 10,8 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Individual %Bias
0,555
6,6%
1,9%
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
No linearity problem. Assess average Bias
Linearity problem > Bias problem
3rd sample
%Error 22,3 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Average %Bias
0,088
35,4%
15,6%
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
No linearity problem. Assess average Bias
Linearity problem > Bias problem
4th sample
%Error 8,4 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Average %Bias
0,158
11,1%
3,7%
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
No linearity problem. Assess average Bias
Linearity problem > Bias problem
6,500
6,600
6,700
6,800
6,900
7,000
7,100
7,200
7,300
7,400
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Refernce Measurement
2nd sample 3rd sample 4th sample1st sample
15 (33)
Measurement 15 – Electrode Cutting – Cathode
Process Electrode Cutting - CATHODE
Gauge Type CCD #2
Measurement Lower width
1st sample
%Error 8,2 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Average %Bias
0,619
9,9%
41,9%
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
No linearity problem. Assess average Bias
Bias problem > Linearity problem
2nd sample
%Error 7,4 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Individual %Bias
0,518
11,3%
10,0%
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
No linearity problem. Assess average Bias
Linearity problem > Bias problem
3rd sample
%Error 8,8 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Average %Bias
0,167
16,8%
21,5%
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
No linearity problem. Assess average Bias
Bias problem > Linearity problem
4th sample
%Error 9,7 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Average %Bias
0,630
6,6%
15,6%
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
No linearity problem. Assess average Bias
Bias problem > Linearity problem
63,500
63,550
63,600
63,650
63,700
63,750
63,800
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Refernce Measurement
2nd sample 3rd sample 4th sample1st sample
16 (33)
Measurement 16 – Electrode Cutting – Cathode
Process Electrode Cutting - CATHODE
Gauge Type CCD #1 & #2
Measurement Average length
1st sample
%Error 3,5 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Average %Bias
0,951
2,4%
19,5%
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
No linearity problem. Assess average Bias
Bias problem > Linearity problem
2nd sample
%Error 5,7 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Individual %Bias
0,509
10,0%
7,3%
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
No linearity problem. Assess average Bias
Linearity problem > Bias problem
3rd sample
%Error 14,6 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Average %Bias
0,165
69,5%
7,4%
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
No linearity problem. Assess average Bias
Linearity problem > Bias problem
4th sample
%Error 9,4 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Average %Bias
0,204
16,7%
55,1%
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
No linearity problem. Assess average Bias
Bias problem > Linearity problem
161,300
161,350
161,400
161,450
161,500
161,550
161,600
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Refernce Measurement
2nd sample 3rd sample 4th sample1st sample
17 (33)
Measurement 17 – Hot Pressing
Process Hot Pressing
Gauge Type Electronic Scale
Measurement Weight
500,00
500,01
500,02
500,03
500,04
1 2 3 4 5 6 7 8 9 10
Measurement using 500g Standard Weight Block
Measurement Reference
99,98
99,99
100,00
100,01
100,02
1 2 3 4 5 6 7 8 9 10
Measurement using 100g Standard Weight Block
Measurement Reference
235,97
235,98
235,99
236,00
236,01
1 2 3 4 5 6 7 8 9 10
Measurement using 100mm Gauge Block
Measurement Reference
213,24
213,25
213,26
213,27
213,28
1 2 3 4 5 6 7 8 9 10
Measurement using 90mm Gauge Block
Measurement Reference
188,09
188,10
188,11
188,12
188,13
1 2 3 4 5 6 7 8 9 10
Measurement using 80mm Gauge Block
Measurement Reference
18 (33)
%Error 0,12 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Bias p-value
Avarage %Bias
0,329
0,0 %
0,000
31,3 %
≥ 0,05
%Linearity < %Bias
≥ 0,05
%Bias < %Linearity
No linearity problem. Assess average Bias
Bias problem
Bias problem > Linearity problem
Precision-to-tolerance N/A
Signal-to-noise SNR = 24308 OK
Comment The scale has bad precision and bad accuracy
19 (33)
Measurement 18 – Hot Pressing
Process Hot Pressing
Gauge Type Contact Sensor
Measurement Thickness
14,1
14,15
14,2
14,25
14,3
14,35
14,4
1 2 3 4 5 6 7 8 9 10
Master JIG
Measurement Reference
17,69
17,7
17,71
17,72
17,73
17,74
17,75
1 2 3 4 5 6 7 8 9 10
Gauge Block Combination 18
Measurement Reference
15,81
15,825
15,84
15,855
15,87
15,885
15,9
1 2 3 4 5 6 7 8 9 10
Gauge Block Combination 16
Measurement Reference
14,860
14,865
14,870
14,875
14,880
14,885
14,890
1 2 3 4 5 6 7 8 9 10
Gauge Block Combination 15
Measurement Reference
14,3
14,35
14,4
14,45
14,5
14,55
14,6
1 2 3 4 5 6 7 8 9 10
Gauge Block Combination 14,5
Measurement Reference
13,8
13,85
13,9
13,95
14
14,05
14,1
1 2 3 4 5 6 7 8 9 10
Gauge Block Combination 14
Measurement Reference
20 (33)
%Error 64,6 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Bias p-value
Avarage %Bias
0,000
5,3 %
0,000
[5,5; 187,6] %
≥ 0,05
%Linearity < %Bias
≥ 0,05
%Bias < %Linearity
Linearity problem. Assess individual Bias
Bias problem
Bias problem > Linearity problem
Precision-to-tolerance 14,38 % NOT OK
Signal-to-noise 671 OK
Comment The scale has bad precision and bad accuracy
13,3
13,35
13,4
13,45
13,5
13,55
13,6
1 2 3 4 5 6 7 8 9 10
Gauge Block Combination 13,5
Measurement Reference
12,8
12,85
12,9
12,95
13
13,05
13,1
1 2 3 4 5 6 7 8 9 10
Gauge Block Combination 13
Measurement Reference
8,8
8,9
9
9,1
9,2
9,3
9,4
1 2 3 4 5 6 7 8 9 10
Gauge Block Combination 9
Measurement Reference
21 (33)
Measurement 19 – US Pre-welding
Process Ultra-Sonic Pre-Welding - ANODE
Gauge Type CCD
Measurement Anode Ear Dimension (Height) after trimming
1st sample
%Error 135,8 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Average %Bias
0,092
50,8 %
0,4 %
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
No linearity problem. Assess average Bias
Linearity problem > Bias problem
2nd sample
%Error 48,5 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Average %Bias
0,011
92,9%
[3,6; 54,0]%
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
Linearity problem. Assess individual Bias
Linearity problem > Bias problem
2,5
3
3,5
4
4,5
5
5,5
6
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Refernce Measurement
1st sample 2nd sample
22 (33)
Measurement 20 – US Pre-welding
Process Ultra-Sonic Pre-Welding - CATHODE
Gauge Type CCD
Measurement Cathode Ear Dimension (Height) after trimming
1st sample
%Error 110,0 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Average %Bias
0,000
91,5 %
[29,4; 61,7] %
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
Linearity problem. Assess individual Bias
Linearity problem > Bias problem
2nd sample
%Error 76,2 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Average %Bias
0,011
78,1%
[4,8; 25,7]%
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
Linearity problem. Assess individual Bias
Linearity problem > Bias problem
2,5
3
3,5
4
4,5
5
5,5
6
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Refernce Measurement
1st sample 2nd sample
23 (33)
Measurement 21 – US Pre-welding
Process Ultra-Sonic Pre-Welding
Gauge Type HI POT test
Measurement Insulation Test
Sample nr Reference Measurement 1 Measurement 2 Measurement 3
1 F F F F
2 P P P P
3 F F F F
4 P F F F
5 F F F F
6 P P P P
7 F F F F
8 P P P P
9 F F F F
10 P P P P
Parameter Result Acceptance criteria Remark
Within appraisers
Kappa value
100 %
≥ 0,75
Good consistent, Good
repeatability
Appraiser vs Standard
Kappa value
0,797980
≥ 0,75
Acceptable accuracy
24 (33)
Measurement 22 – Tab Final Welding
Process Tab Final Welding
Gauge Type CCD
Measurement Lid Angle
1st sample
%Error 237,6 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Individual %Bias
0,000
81,8 %
[1,2; 17,5] %
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
Linearity problem. Assess individual Bias
Linearity problem > Bias problem
2nd sample
%Error 67 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Individual %Bias
0,000
61,4 %
[0,7; 16,5] %
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
Linearity problem. Assess individual Bias
Linearity problem > Bias problem
88
88,5
89
89,5
90
90,5
91
91,5
92
92,5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
Measurement Reference
1st sample 2nd sample
25 (33)
Measurement 23 – Tab Final Welding
Process Tab Final Welding
Gauge Type HI POT test
Measurement Insulation Test
Tolerance Pass/Fail
Sample nr Reference Measurement 1 Measurement 2 Measurement 3
1 F F F F
2 P P P P
3 F F F F
4 P P P P
5 F F F F
6 P P P P
7 F F F F
8 P P P P
9 F F F F
10 P P P P
Parameter Result Acceptance criteria Remark
Within appraisers
Kappa value
100 %
≥ 0,75
Good consistent, Good
repeatability
Appraiser vs Standard
Kappa value
1
≥ 0,75
Good accuracy
26 (33)
Measurement 24 – Insulation Film Wrapping
Process Insulation Film Wrapping
Gauge Type CCD
Measurement Upper Taping Position
1st sample
%Error 28,4 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Individual %Bias
0,000
67,1 %
[1,1; 11,1] %
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
Linearity problem. Assess individual Bias
Linearity problem > Bias problem
2nd sample
%Error 22,48
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Individual %Bias
0,000
80,5 %
[0,8; 45,7] %
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
Linearity problem. Assess individual Bias
Linearity problem > Bias problem
3rd sample
%Error 5,9 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Individual %Bias
0,000
42,4%
[14,5; 33,8]%
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
Linearity problem. Assess individual Bias
Linearity problem > Bias problem
4th sample
%Error 6,4 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Individual %Bias
0,565
8,1%
0,6%
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
No linearity problem. Assess average Bias
Linearity problem > Bias problem
2,90
3,10
3,30
3,50
3,70
3,90
4,10
4,30
4,50
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99 101 103 105 107 109 111 113 115 117
QC Machine
1st sample 4th sample3rd sample2nd sample
27 (33)
Measurement 25 – Insulation Film Wrapping
Process Insulation Film Wrapping
Gauge Type CCD
Measurement Lower Taping Position
1st sample
%Error 25,3 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Individual %Bias
0,002
57,9 %
[2,0; 51,7]
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
Linearity problem. Assess individual Bias
Linearity problem > Bias problem
2nd sample
%Error 26,9 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Individual %Bias
0,000
54,2 %
[0,7; 30,7] %
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
Linearity problem. Assess individual Bias
Linearity problem > Bias problem
3rd sample
%Error 7,5 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Individual %Bias
0,046
12,5%
[0,2; 12,3]%
≥ 0,05
%Linearity < %Bias
%Bias < %Linearity
Linearity problem. Assess individual Bias
Linearity problem > Bias problem
2,75
3,00
3,25
3,50
3,75
4,00
4,25
4,50
4,75
5,00
5,25
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87
Refernce Measurement
2nd sample 3rd sample1st sample
28 (33)
Measurement 26 – Electrolyte Filling 1
Process Electrolyte Filling #1
Gauge Type Electronic Scale
Measurement Cell Weight - Incoming
235,95
235,955
235,96
235,965
235,97
235,975
235,98
1 2 3 4 5 6 7 8 9 10
Gauge Block 100 mm
Measurement Reference
213,23
213,235
213,24
213,245
213,25
213,255
213,26
1 2 3 4 5 6 7 8 9 10
Gauge Block 90 mm
Measurement Reference
188,08
188,085
188,09
188,095
188,1
188,105
188,11
1 2 3 4 5 6 7 8 9 10
Gauge Block 80 mm
Measurement Reference
165,97
165,975
165,98
165,985
165,99
165,995
166
1 2 3 4 5 6 7 8 9 10
Gauge Block 70 mm
Measurement Reference
142,13
142,135
142,14
142,145
142,15
142,155
142,16
1 2 3 4 5 6 7 8 9 10
Gauge Block 60 mm
Measurement Reference
29 (33)
%Error 0,0016 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Bias p-value
Avarage %Bias
0,298
0,0 %
0,000
28,3 %
≥ 0,05
%Linearity < %Bias
≥ 0,05
%Bias < %Linearity
No linearity problem. Assess average Bias
Bias problem
Bias problem > Linearity problem
Precision-to-tolerance N/A
Signal-to-noise SNR = 24308 OK
Comment The scale has bad precision and bad accuracy
30 (33)
Measurement 27 – Electrolyte Filling 1
Process Electrolyte Filling #1
Gauge Type Electronic Scale
Measurement Cell Weight - Outgoing
235,90
236,00
236,10
236,20
236,30
236,40
236,50
1 2 3 4 5 6 7 8 9 10
Gauge Block 100 mm
Measurement Reference
213,10
213,20
213,30
213,40
213,50
213,60
213,70
1 2 3 4 5 6 7 8 9 10
Gauge Block 90 mm
Measurement Reference
187,90
188,00
188,10
188,20
188,30
188,40
188,50
1 2 3 4 5 6 7 8 9 10
Gauge Block 80 mm
Measurement Reference
165,80
165,90
166,00
166,10
166,20
166,30
166,40
1 2 3 4 5 6 7 8 9 10
Gauge Block 70 mm
Measurement Reference
141,90
142,00
142,10
142,20
142,30
142,40
142,50
142,60
1 2 3 4 5 6 7 8 9 10
Gauge Block 60 mm
Measurement Reference
31 (33)
1st sample
%Error 0,21 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Individual Bias p-value
Avarage %Bias
0,000
0,2 %
[0,000]
[879,3; 1465,5] %
≥ 0,05
%Linearity < %Bias
≥ 0,05
%Bias < %Linearity
Linearity problem. Assess individual Bias
Bias problem
Bias problem > Linearity problem
Precision-to-tolerance N/A
Signal-to-noise SNR = 10864 OK
Comment The scale has bad precision and bad accuracy
2nd sample
%Error 0,003 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Individual Bias p-value
Avarage %Bias
0,06
0,0%
0,000
24,2%
≥ 0,05
%Linearity < %Bias
≥ 0,05
%Bias < %Linearity
No linearity problem. Assess average Bias
Bias problem
Bias problem
Precision-to-tolerance N/A
Signal-to-noise SNR = 19818 OK
Comment The scale has bad precision and bad accuracy
32 (33)
Measurement 28 – Electrolyte Filling 2
Process Electrolyte Filling #2
Gauge Type Electronic Scale
Measurement Cell Weight
235,960
235,965
235,970
235,975
235,980
235,985
235,990
1 2 3 4 5 6 7 8 9 10
Gauge Block 100 mm
Measurement Reference
213,240
213,245
213,250
213,255
213,260
213,265
213,270
1 2 3 4 5 6 7 8 9 10
Gauge Block 90 mm
Measurement Reference
188,080
188,085
188,090
188,095
188,100
188,105
188,110
1 2 3 4 5 6 7 8 9 10
Gauge Block 80 mm
Measurement Reference
165,970
165,975
165,980
165,985
165,990
165,995
166,000
1 2 3 4 5 6 7 8 9 10
Gauge Block 70 mm
Measurement Reference
142,130
142,135
142,140
142,145
142,150
142,155
142,160
1 2 3 4 5 6 7 8 9 10
Gauge Block 60 mm
Measurement Reference
33 (33)
%Error 0,00011 %
Parameter Result Acceptance criteria Remark
Linearity p-value
%Linearity
Individual Bias p-value
Avarage %Bias
0,005
0,0 %
[0,000]
[1,0; 8,0] %
≥ 0,05
%Linearity < %Bias
≥ 0,05
%Bias < %Linearity
Linearity problem. Assess individual Bias
Bias problem
Bias problem > Linearity problem
Precision-to-tolerance N/A
Signal-to-noise SNR = 92116216 OK
Comment The scale has bad precision and bad accuracy
1 (2)
Appendix II – Analysis Using Minitab
Calculating Standard Deviation Prior to analysis, it is important to have as much information regarding the specific process as
possible, particularly the process standard deviation. However, since many processes at the case
company are in the commissioning phase and hence not stable, it is logical to assume that the
Reference data reflects the standard deviation of the process at the time of sample collection. The
Reference data is therefore used to calculate the standard deviation as follows:
1. Enter the Reference data in Minitab worksheet, see Image 1:
Minitab worksheet.
2. Choose Stat → Basic Statistics → Display Descriptive Statistics.
3. In Variables, enter Measurement data.
4. Open Statistic option and ensure that Standard deviation is
chosen to be displayed.
5. From the result, the standard deviation of the process at the
sampling time can be obtained.
MSA in Minitab For data with repetitive readings, the analysis using Minitab begins
with a Gage R&R analysis followed by a Gage Linearity and Bias
Study. However, if it is possible to only obtain one sampling, the
analysis only includes Gage Linearity and Bias Study. For attribute
data, the analysis will be conducted as Attribute Agreement Analysis.
Gage R&R analysis
1. Measurement data and Reference data as well as the Part are inserted in Minitab in columns,
see Image 1: Minitab worksheet.
2. Chose Stat → Quality Tools → Gage Study → Gage R&R study (Crossed)…
3. In Part numbers, enter Part.
4. In Measurement data, enter Measurement.
5. The Method of Analysis is chosen to ANOVA
6. In Option, choose and enter the Lower and Upper Specification
7. From the result, the value of Precision-to-tolerance (P/T) and Signal-to-noise (SNR) can be
obtained as Minitab display these metrics as %Tolerance (SV/Toler) for Total Gage R&R and
Number of Distinct Categories, respectively.
Image 1: Minitab worksheet
2 (2)
Gage Linearity and Bias Study
1. Calculate the standard deviation of the process at
the sampling time as Calculating Standard
Deviation.
2. Measurement data and Reference data as well as
the Part are inserted in columns in Minitab, see
Image 1: Minitab worksheet.
3. Choose Stat → Quality Tools → Gage Study →
Gage Linearity and Bias Study …
4. In Part numbers, enter Part.
5. In Reference values, enter Reference data
6. In Measurement data, enter Measurement data.
7. In Process variation, enter either the variation by
multiplying six with the process standard
deviation calculated in step 1, alternatively, the
known process standard deviation.
8. From the result p-value for Linearity problem can
be obtained as p-value of Slope displayed in
Minitab. Furthermore, other acceptance criteria such as %Linearity, %Bias and p-value for
Bias problem, can be obtained as in Image 2: Result of Gage Linearity and Bias Study.
Attribute Agreement Analysis
1. Measurement data and Reference data as
well as the Part are inserted in Minitab in
columns, see Image 3.
2. Chose Stat → Quality Tools → Attribute
Agreement Analysis …
3. Chose Multiple columns, in which enter all
the Measurement data
4. In Number of appraisers enter the number
of MI that conducts the measurement,
which in this case is one
5. In Number of trials enter the number of
repetitive reading that MI conducts on each part, which in this case is three.
6. In Known standard/attribute, enter Reference.
7. From the result, the Kappa statistic Within Appraisers and Appraisers vs Standard can be
obtained.
Image 2: Result of Gage Linearity and Bias Study
Image 3: Worksheet for Attribute Agreement Analysis
