Sensor-based Online Process Monitoring in Advanced Manufacturing

David Mathew Roberson III

Thesis submitted to the faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the requirements for the degree of

Master of Science

in Industrial and Systems Engineering

Zhenyu (James) Kong, Committee Chair Ran Jin

Prahalad K. Rao

August 8, 2016 Blacksburg, VA

Keywords: Additive Manufacturing, Fused Deposition Modeling, Machining, Turning,

Sensor-based Monitoring

Sensor-based Online Process Monitoring in Advanced Manufacturing

David Mathew Roberson III

ABSTRACT

Effective quality improvement in the manufacturing industry is continually pursued.

There is an increasing demand for real-time fault detection, and avoidance of destructive

post-process testing. Therefore, it is desirable to employ sensors for in-process monitoring,

allowing for real-time quality assurance. Chapter 3 describes the application of sensor-

based monitoring to additive manufacturing, in which sensors are attached to a desktop

model fused deposition modeling machine, to collect data during the manufacturing

process. A design of experiments plan is conducted to provide insight into the process,

particularly the occurrence of process failure. Subsequently, machine learning

classification techniques are applied to detect such failure, and successfully demonstrate

the future potential of this platform and methodology. Chapter 4 relates the application of

online, image-based quantification of the surface quality of workpieces produced by

cylindrical turning. Representative samples of cylindrical shafts, machined by turning

under various conditions, are utilized, and an apparatus is constructed for acquiring images

while the part remains mounted on a lathe. The surface quality of these specimens is

analyzed, employing an algebraic graph theoretic approach, and preliminary regression

modeling displays an average surface roughness (Ra) prediction error of less than 8%.

Prediction occurs in less than 2 seconds, showing the capability for future application in a

real-time, quality control setting. Both of these cases, in additive manufacturing and in

turning, are validated using real experimental data and analysis, showing application of

sensor-based online process monitoring in multiple manufacturing areas.

Sensor-based Online Process Monitoring in Advanced Manufacturing

David Mathew Roberson III

GENERAL AUDIENCE ABSTRACT

Effective quality improvement in the manufacturing industry is continually pursued,

and there is an increasing demand for real-time quality monitoring. Therefore, it is

desirable to employ sensors for in-process monitoring, allowing for real-time quality

assurance. This is explored in two manufacturing areas. The first section of this work is in

the area of additive manufacturing (“3D printing”), in which sensors are attached to a

desktop model machine, to collect data during the printing process. Experiments are

conducted to provide insight into how the process behaves, particularly the occurrence of

printing failure. Machine learning classification techniques are then applied to detect such

failure, and successfully demonstrate the future potential of this platform and methodology,

for real-time monitoring of the process. The second section of this work relates to the

conventional machining process of turning, and describes the application of image-based

measurement of surface roughness. An apparatus is constructed for acquiring images, while

the cylindrically turned shaft remains mounted on the lathe. The surface roughness is

measured, and preliminary modeling displays an average surface roughness prediction

error of less than 8%. This prediction occurs in less than 2 seconds, showing the capability

for future application in a real-time, quality control setting. Both of these cases, in additive

manufacturing and in turning, show the application of sensor-based monitoring in various

manufacturing areas. This work provides a basis for future research and application,

demonstrating how this sensor-based monitoring approach may be used for real-time

quality monitoring in manufacturing.

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ACKNOWLEDGEMENTS

I would like to acknowledge and thank my advisor, Dr. Kong, for his guidance. He has kindly

believed in and encouraged me, and without his support I would not have been able to begin my

graduate career. I thank Dr. Rao for his valuable influence, motivating me to pursue independent

and critical thought. And I also thank Dr. Jin, for taking his time in being a part of my committee,

and for his thoughtful advice and input.

I would also like to thank my fellow lab members, Jia Liu, Kaveh Bastani, Chen-ang Liu, and

Babak Barazandeh, for their selfless friendship and help.

Most of all, I’d like to thank my family for their constant love and support throughout my entire

life, even when I can’t physically be with them. Without them I could not have gotten this far, and

I love them very much.

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Table of Contents Chapter 1. Introduction ............................................................................................................................1

1.1 Motivation and Objective of the Proposed Research Related to Additive Manufacturing ..........2

1.2 Motivation and Objective of the Proposed Research Related to the Turning Operation .............3

Chapter 2. Literature Review ..................................................................................................................4

2.1 Review of the Literature Relating to Process Monitoring in Additive Manufacturing ................4

2.1.1 Typical defects in the FDM process ........................................................................................4

2.1.2 Prior experimental studies in FDM ........................................................................................10

2.1.3 Sensor-based monitoring in FDM ..........................................................................................11

2.2 Review of the Literature Relating to Surface Finish Monitoring in Turning .............................13

2.2.1 Optical methods for surface roughness estimation ................................................................13

2.2.2 Shortcomings in the existing literature ..................................................................................14

Chapter 3. Sensor-based Process Monitoring in Additive Manufacturing ........................................16

3.1 Process description of FDM .......................................................................................................17

3.2 Experimental Studies in FDM ....................................................................................................18

3.2.1 Design of standard test artifact ...............................................................................................18

3.2.2 Selection of process input and output variables .....................................................................19

3.2.3 Identification of process conditions leading to build failures ................................................21

3.2.4 Analysis of build failure in FDM ...........................................................................................24

3.3 Sensor-based Monitoring of FDM .............................................................................................26

3.3.1 Sensor platform for real-time monitoring of FDM process conditions .................................26

3.3.2 Discussion of acquired sensor data ........................................................................................30

3.4 Machine Learning Classification of FDM Process States ..........................................................32

3.4.1 Machine learning classification algorithm selection ..............................................................33

3.4.2 Sensor data selection ..............................................................................................................33

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3.4.3 Results of classification modeling for identifying FDM process states .................................34

3.4.4 Discussion of machine learning classification in FDM .........................................................40

Chapter 4. Image-based Online Monitoring of Surface Finish in Turning .......................................43

4.1 Limitations of the State-of-the-art ..............................................................................................43

4.2 Discussion of Surface Quality Characterization Metric .............................................................44

4.2.1 Methodology and background of surface quality metric .......................................................45

4.2.2 Case studies and simulations verifying surface quality metric ..............................................47

4.2.3 Fiedler number applicability to turning process ....................................................................47

4.3 Methodology of Image-based Approach for Surface Finish Monitoring ...................................48

4.3.1 Workpiece used for study ......................................................................................................48

4.3.2 Image acquisition setup ..........................................................................................................50

4.3.3 Processing and analysis of images .........................................................................................51

4.3.4 Results of image-based prediction of surface roughness .......................................................54

Chapter 5. Summary and Future Work ................................................................................................55

5.1 Research Contributions to Process Monitoring in Additive Manufacturing ..............................55

5.1.1 Future work in AM process monitoring .................................................................................56

5.1.2 Acknowledgements ................................................................................................................57

5.2 Research Contributions to Online Monitoring of the Turning Process ......................................57

5.2.1 Future work in image-based monitoring of turning ...............................................................58

5.2.2 Acknowledgements ................................................................................................................58

References 59

Appendix 66

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List of Figures Figure 3-1. Summary of the overall approach regarding process monitoring in additive manufacturing ...17

Figure 3-2. (a) Schematic of the FDM process; (b) Schematic of the FDM setup instrumented with multiple

sensors used for measuring process conditions in real-time [83] .......................................................18

Figure 3-3. Test part resembling the NAS 979 standard part used for experimental trials (note: dimensions

are in inches) [83] ................................................................................................................................19

Figure 3-4. Diagram showing the mean surface roughness (Ra, µm) for each TC, averaged over three

replications [83] ..................................................................................................................................23

Figure 3-5. Schematic representation of FDM failure due to nozzle clogging [83] ....................................24

Figure 3-6. Heterogeneous sensor array for real-time monitoring of FDM process [83] ............................27

Figure 3-7. System diagram denoting the data acquisition methodology ....................................................28

Figure 3-8. (Top) The three observed process states in FDM, demarcated as normal, abnormal, and failure;

(Bottom) The transition between normal, abnormal, and failure states, is evident from the time series

pattern of the non-contact IR temperature sensor [83] ........................................................................29

Figure 3-9. Summary of the machine learning classification approach .......................................................32

Figure 3-10. Model misclassification rates ..................................................................................................39

Figure 4-1. Summary of the overall approach regarding image-based surface finish prediction in turning

.............................................................................................................................................................48

Figure 4-2. Representative images of the 3 inch diameter workpiece, with the corresponding roughness

measurements (Ra) ...............................................................................................................................49

Figure 4-3. Experimental setup at Virginia Tech’s lab ................................................................................50

Figure 4-4. Experimental setup at CCAM’s lab ..........................................................................................50

Figure 4-5. Glare and toolmarks evident on the surface of the workpiece ..................................................51

Figure 4-6. Analysis procedure for surface roughness estimation ...............................................................52

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Figure 4-7. Fitted regression models using the 1 inch and 3 inch diameter workpieces; Error bars denote the

95% confidence interval bounds of the mean predicted Ra value, based upon the 30 acquired images

of each workpiece segment .................................................................................................................54

Figure B-1. Representative examples of raw data, from a normal condition and a failed condition ..........67

Figure C-1. Out-of-bag classification error vs. number of trees chosen, in Random Forest model type ....68

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List of Tables Table 2-1. Surface defects, with their respective causes and strategies for improvement .............................8

Table 2-2. Internal defects, with their respective causes and strategies for improvement ............................9

Table 3-1. Full factor design of experiments plan for FDM tests (blocked on replicates by filament color)

.............................................................................................................................................................22

Table 3-2. Sensor information and location .................................................................................................26

Table 3-3 List of possible features to be selected, along with the corresponding sensor ............................34

Table 3-4. Classification model results, using TC7 sensor data ..................................................................35

Table 3-5. Classification model results, using TC8 sensor data ..................................................................36

Table 3-6. Classification model results, using both TC7 and TC8 sensor data ...........................................38

Table 4-1. Workpiece conditions .................................................................................................................49

Table 4-2. Regression model test error ........................................................................................................54

Table A-1. Design of experiments plan, and corresponding experimental results ......................................66

Table D-1. Specifications of Point Grey Grasshopper 3 camera .................................................................69

Table E-1. Testing error of the regression model for the 1 inch diameter workpiece .................................70

Table E-2. Testing error of the regression model for the 3 inch diameter workpiece .................................70

1

Chapter 1. Introduction

In the modern manufacturing industry, as processes and technology evolve to become more

complex and more automated, the task of process monitoring continues to be applied for quality

and reliability improvement. There is an increasing demand for real-time fault detection and

quality monitoring, remaining an ongoing area of research [1-4]. Offline quality control testing

may be destructive and expensive, highlighting the need for prediction of product quality in a

timely manner [5]. A real-time approach to fault detection allows for maintenance to be performed,

and to detect abnormalities and avoid process failure, which may damage the product or system

components [2-4, 6, 7]. Such monitoring and control poses a challenge, requiring knowledge of

the process, and development of systems to aid in this critical pursuit of quality improvement [8].

Therefore, this research investigates sensor-based solutions to these questions of online, real-

time process monitoring. This work examines application of this sensor-based monitoring

approach in two areas of manufacturing; one is the relatively novel technology of additive

manufacturing, and the other is the well-established, conventional machining process of turning.

The rest of this thesis is organized as follows: the remainder of Chapter 1 presents the

motivation and objective underlying the investigation of these two manufacturing technologies.

Following this is a review of the related literature, in Chapter 2. The research methodology and

experimental studies for the additive manufacturing process are detailed in Chapter 3, and for the

turning process in Chapter 4. Finally, Chapter 5 summarizes the results and contribution of this

work, and describes the potential future research for application of real-time, online process

monitoring.

2

1.1 Motivation and Objective of the Proposed Research Related to Additive Manufacturing

At this time, additive manufacturing processes, such as fused deposition modeling (FDM), are

relatively novel with respect to challenges concerning process reliability and consistency [9-12].

Quality control in FDM is largely limited to offline techniques, leading to high scrap rates [13].

Therefore, there exists a need to develop effective process control and monitoring techniques in

FDM [14], leading to more than merely failure diagnosis, but eventually to real-time prediction of

process failure [15].

Chapter 3 of this research describes the successful implementation of several sensors on a

desktop model FDM machine, for the purpose of online process monitoring. This is shown in the

following tasks:

1) Equipping an FDM machine (MakerBot Replicator 2X) with multiple types of online

sensors, including vibration and temperature sensors.

2) Using the sensor data for construction of machine learning classification models, to

demonstrate the capability for detection of process drifts in FDM.

This setup allows real-time monitoring and detection of FDM process drifts from ideal

conditions, in order to pursue corrective action and reduction of the occurrence of product defects.

This research demonstrates that the constructed platform and data collection is suitable for

potential in-process monitoring applications in the future.

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1.2 Motivation and Objective of the Proposed Research Related to the Turning Operation

Surface finish is critical for the functional integrity of engineering components, as it influences

aspects such as fatigue, thermal behavior, service life, etc. [16-20]. Quantification of the surface

finish is essential not only from a quality assurance perspective, but also for tracking the

performance of the manufacturing process [19, 21].

Chapter 4 of this research implements a non-contact, image-based surface quality estimation

method for machined surfaces. This is done for workpieces machined by turning on a lathe, as

turning is a matured manufacturing method in which improved productivity and quality control

are desirable. Specifically, this is done by:

1) Incorporating an optical imaging setup in a lathe, to acquire images of cylindrically

turned workpieces.

2) Conducting analysis of the processed images using an algebraic graph theoretic

approach, and demonstrating the ability of regression modeling to predict the arithmetic

mean surface roughness (Ra) of the workpiece, in near real-time.

This research shows the application of an existing graph theoretic quantifier [22] to non-contact,

image-based surface quality estimation of conventionally machined workpieces, which

traditionally are limited to contact-based methods. Such a non-contact approach shows potential

for future online process monitoring applications.

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Chapter 2. Literature Review

This chapter describes the research background and reviewed literature relating to process

monitoring in the two manufacturing technologies of fused deposition modeling (a type of additive

manufacturing), and the conventional machining process of turning.

2.1 Review of the Literature Relating to Process Monitoring in Additive Manufacturing

Here are reviewed typical defects which occur in the fused deposition modeling (FDM) process,

as well as prior experimental studies in FDM, and sensor-based monitoring in FDM, in order to

identify shortcomings in the current state-of-the-art.

2.1.1 Typical defects in the FDM process

The purpose of this section of this literature review is to present a general overview of the most

common predictable defects which occur in fused deposition modeling (FDM), as well as methods

to minimize or avoid such defects. There are two main categories of defects which may occur in

parts produced using FDM, namely surface and internal defects.

Surface defects are visible on the surface of the fabricated part, and include the staircase effect,

chordal errors, and overfill and underfill errors. The so-called staircase effect occurs due to the

layer by layer build method of the FDM process [23-25]. Any sloped region of a part, once

converted into slices, results in the predictable staircase effect. This is seen from a profile view of

the part as distinct edges where each layer is visible in a pattern resembling stairs, instead of the

ideal geometry of a smooth, sloped surface. Although a smaller layer or slice thickness will reduce

this effect, the build time will necessarily increase due to the increased number of layers. Choice

of careful part orientation [26-28], as well as adaptive slicing which varies the layer thickness [29,

30], help to decrease the staircase effect without negatively affecting build time of the part as

5

severely. Chordal errors may occur from approximation of the CAD model which the final part is

based on [23-25]. The original CAD file is converted to an STL file, and approximated by a surface

of triangles, which result in the curved surfaces becoming visible as a series of line segments in

the fabricated part. A proposed solution is to use a positive offset for the dimensions of the part,

and then to use post-processing to smooth the constructed part [23, 24]. This may be deemed

unnecessary if the software used to slice the model maintains an acceptably high resolution [25].

Overfill and underfill errors result when there is lack of coordination between motion of the nozzle

and the extrusion motors, and are visible in the part as excess material or a void, respectively [23-

25, 31, 32]. These may also occur if the material is not extruded at a constant rate due to slippage

or inconsistent filament diameter. If filament continues to be extruded while having reached the

end of the toolpath segment, excess material is deposited (overfill). Conversely, if extrusion stops

before reaching the end of the toolpath, a void appears (underfill). This problem is made more

difficult due to residual pressure of the melted filament within the extrusion chamber, causing

extrusion even after the motor stops. Avoiding these errors requires careful determination of the

point at which extrusion should be stopped, by optimization of the process parameters [33]. When

fabricating parts that include overhangs, we should consider the removal of the support structures

which can leave burrs on the surface of the part [23, 24]. Most surface defects in FDM may be

minimized through the use of post-processing techniques. Examples include CNC machining

operations [34], hot cutter machining [35], an abrasive jet machining based method [36], and

chemical treatment [37, 38]. Although such post-processing has proven to be beneficial in

improving surface finish, we must decide whether the time, cost, and effort are worthwhile.

Internal defects are the second category of defects in FDM, which include the occurrence of

voids and weak bonding of the deposited material within the part. These internal defects may be

6

considered more detrimental than surface defects, as they affect the structural integrity of the part

and cannot be easily eliminated by post-processing [23]. Sub-perimeter voids are seen as

incomplete filling of the area adjacent to the perimeter of the part [23, 24, 32, 39]. Such voids often

occur due to the raster pattern, when the toolpath includes sharp turns as it reaches the perimeter

of the part. To prevent these voids, a negative offset can be given in the toolpath so that the internal

fill pattern overlaps slightly with the perimeter path, or an increased flow rate can be used for

material deposited at points of intersection between the internal fill path and perimeter path [23,

24]. Inter-road voids occur when there is no physical contact between adjacent roads, most likely

due to insufficient flow rate, and may be evident when there is inconsistent material flow, due to

slipping in the extrusion mechanism or variation in filament diameter [23, 24]. These voids may

also be due to shrinkage of the cooling filament during long lengths of the toolpath, before the

adjacent road is deposited. Both inter-road and sub-perimeter voids may be reduced by a small

overlap of adjacent roads [23, 39]. Qiu and Langrana [40] also show that by careful toolpath

generation to minimize the number of discontinuous fill patterns, voids can be minimized. Core

voids can be thought of as a particular case of the inter-road void, resulting specifically from a

contour fill pattern, when an incorrect road width choice leads to a void in the center of the layer

[23, 24]. Weak bonding between roads shares similar causes with inter-road voids. Although the

deposited roads may be physically touching, the bond is not strong, which negatively affects the

physical strength of the produced part [23, 24, 41-43]. The temperature within the build chamber

has a significant effect on bond strength between deposited material filaments, and so should be

taken into account when part strength is desirable [44]. Chen et al. [45] conducted a study using

nano-focus computed tomography to examine the integrity of parts produced using FDM,

identifying the presence of air bubbles occurring within deposited filament (internal bubbles) and

7

within the bonding region between adjacent roads (necking bubbles). These necking bubbles in

particular can compromise the strength of the finished part, and the authors caution that the

presence of moisture on the filament can cause these air bubbles. Warping of a part may occur due

to temperature changes as the filament is extruded and solidifies, and the internal stresses lead to

part distortions. The longer the individual deposited line of material within the toolpath, the greater

the internal stress [25, 28, 46]. Zhang and Chou [47] show through FEA modeling and

experimental validation that deposition speed (of the extruded filament), followed by layer

thickness, are the most significant factors affecting residual stresses in the part which lead to

warping. Wang et al. [48] find that the presence of support structures may restrict warping of the

part. In addition, fabrication of a part in an enclosed chamber with a relatively high temperature

may alleviate the thermal stresses existing in the part, similar to an annealing process. Guerrero-

de-Mier et al. [49] present an approach to combat warping by splitting the part into square or

hexagonal bricks, effectively reducing the accumulated internal stresses within the part, and

thereby minimizing warping.

Peng and Xiao [25] demonstrate that while careful choice of process parameters can improve

part accuracy and eliminate defects, they may have contradictory effects. For example, a higher

build chamber temperature may decrease part warping, but also may affect the surface finish. And

while voids may be decreased by using an overlap in the toolpath, overfill errors may increase.

Therefore, when considering how to avoid the described defects in FDM produced parts, we must

develop a deeper understanding of these defects and their respective causes. A summary of the

surface and internal defects are shown in Table 2-1 and Table 2-2, respectively.

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Table 2-1. Surface defects, with their respective causes and strategies for improvement

Surface defect Caused by Solutions

Staircase effect [23-25]

Sloping part geometry Part orientation

Large layer thickness Adaptive slicing

Post-processing

Chordal errors [23-25] Software approximation of part model Positive dimensional offset with post-processing

Overfill [23-25, 31, 32]

Lack of coordination between extruder movement and filament extrusion Parameter optimization

Filament slippage Post-processing

Inconsistent filament diameter

Underfill [23-25, 31, 32]

Lack of coordination between extruder movement and filament extrusion Parameter optimization

Filament slippage

Inconsistent filament diameter

Support structure burrs [23, 24] Removal of support structures Post-processing

9

Table 2-2. Internal defects, with their respective causes and strategies for improvement

Internal defect Caused by Solutions

Sub-perimeter voids [23, 24, 32, 39]

Sharp turns in fill pattern toolpath Toolpath overlap

Increased material flow rate

Fill pattern optimization

Inter-road voids [23, 24]

Filament slippage Toolpath overlap

Inconsistent filament diameter Increased material flow rate

Filament cooling and shrinkage Fill pattern optimization

Core voids [23, 24] Incorrect road width in contour fill pattern Improved toolpath planning

Weak bonding [23, 24, 41-43]

Filament slippage Toolpath overlap

Inconsistent filament diameter Increased material flow rate

Filament cooling and shrinkage Increased build chamber temperature

Air bubbles [45] Presence of moisture Keep filament dry

Warping [25, 28, 46]

Slow deposition speed Increased build chamber temperature

Large layer thickness Support structures

Low build chamber temperature "Bricking"

Long toolpaths

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2.1.2 Prior experimental studies in FDM

Mahesh et al. [50] compared different additive manufacturing (AM) techniques, namely,

stereolithography (SLA), laminated object manufacturing (LOM), selective laser sintering (SLS),

and fused deposition modeling (FDM) based on their ability to adhere to geometric dimensions

and tolerance for a standardized test part. They reported that FDM is typically less suitable for

crafting parts with fine features compared to the other additive manufacturing processes studied;

defects were prominently visible on the FDM-crafted workpiece.

Peng et al. [51] investigated the effect of some of the process variables in FDM described in

this work, namely, extrusion temperature, temperature of the enclosure (ambient chamber

temperature), scanning speed of the extruder mechanism (feed rate), and extrusion speed (flow

rate) (see Section 3.2.2 for a brief explanation of the variables). They discussed the effect of

extruder (nozzle) temperature, and concluded that a nozzle temperature below 220 °C is inadequate

for melting the ABS plastic filament and leads to clogging, poor layer adhesion, and delamination.

The authors also made observations regarding feed rate and flow rate settings, noting that part

defects are minimized when their ratio approaches 1. Significant deviation from the 1:1 feed rate

to flow rate ratio can lead to clogs and scraping of the part by the nozzle. This work is valuable for

providing some of the physical basis for the experimental observations in this current research,

especially the reasons for explaining part build failures resulting from suboptimal feed rate to flow

rate ratios (see Section 3.2.4).

Wang et al. [48] proposed and experimentally validated a mathematical model for warp

deformation in FDM parts. The authors acknowledged the significance of numerous process

variables, such as ambient chamber temperature, layer thickness, extrusion temperature, extruder

11

head speed, geometric structure of the part, and tool path. However, quantitative correlations

between these factors and part deformation were unexplored.

Anitha et al. [52] used a Taguchi (L18) approach to specifically correlate certain process

parameters with the surface roughness (Ra) of an unspecified test part. The chosen parameters were

road width, layer thickness, and extrusion speed, each at three levels, while other variables such

as temperature were maintained at a constant (unspecified) level. They found all three chosen

factors to be statistically significant contributors in determining surface roughness. However, one

of the major shortcomings in this work is the absence of temperature variables, which have been

known to dominate build quality in FDM [53].

Armillotta [54] and Agarwala et al. [23] found that FDM errors may result from frequent start-

stop motion of the extruder head, warping of the part [55], or simply a failure to extrude the build

material (nozzle clog). Other studies have explored methods of improving part quality by

examining error compensation of the extrusion path [40, 56], determining the optimal part

orientation in relation to the build direction [57, 58], and consideration for the balancing of build

speed with accuracy by selectively decreasing the size of outer build layers to increase the part’s

precision [59, 60].

From the above review, it can be seen that although some work has been done in studying the

quality control issues in the additive manufacturing process, there is a lack of experimental work

and analysis for capturing process defects and failures using online real-time sensors.

2.1.3 Sensor-based monitoring in FDM

From a sensor-based monitoring perspective, Bukkapatnam et al. [61] investigated vibration

that occurs during the printing of a test part, by comparing mechanistic models with experimentally

obtained sensor data. The sensors used were two accelerometers affixed to the extruder head, and

12

two more on the frame of the machine. They validated their model using a 3-level factorial design,

varying extrusion speed (flow rate) and extruder mechanism movement speed (feed rate), resulting

in a total of 15 trial conditions. Bukkapatnam et al. demonstrate the ability to distinguish process

abnormalities from sensor data.

Online defect detection in extrusion-based layered manufacturing (resembling FDM) of

advanced multiple ceramics was investigated by Fang et al. [62]. They used machine vision

techniques to detect defects in layered manufacturing based on optical imaging of each layer during

the build. The optical images were used to visually identify various defects. Additionally, the

authors were able to evaluate the geometrical integrity of the build by comparing the optical image

of a layer to its original CAD design. In a related research, Fang et al. [31] further augmented their

monitoring approach using online signature analysis. They first mathematically represented (in the

form of a function) the ideal build morphology for each layer from the CAD file, which is termed

the process signature. Thereafter, the process signature was compared with the physical layer-by-

layer build pattern after image analysis to detect process anomalies.

Cheng and Jafari [63] further examined the surface pattern using image intensity information.

Build defects were classified into two types, namely, randomly occurring defects, and anomalies

due to assignable causes, e.g., improper extruder tool path. The randomly occurring defects were

detected by 2D texture analysis, while the assignable defects were identified using the process

signature technique developed previously by Fang et al. [31].

A key shortcoming evident from this literature review is the lack of real-time process

monitoring in order to detect the status of the build. Prior studies have rarely used multiple sensors

to evaluate the FDM process or sought to conduct process analysis in real-time. This research seeks

13

to address this gap by means of experimental studies on an FDM machine, which has been

integrated with multiple sensors for real-time process monitoring.

2.2 Review of the Literature Relating to Surface Finish Monitoring in Turning

Here are reviewed studies of optical methods for characterization of the surface finish of

machined workpieces, with respect to a desire for online application, and a focus on surfaces

machined by turning. Existing studies of surface roughness prediction note the benefits of optical

methods versus common stylus-based measurement. The most notable of which is the non-contact

nature of optical measurement, as well as less variation among measurements, due to the

evaluation of an area of the surface instead of the line segment obtained by a stylus. Optical and

image-based roughness measurement is especially suitable for online application, opening the door

to successful in-process quality control.

2.2.1 Optical methods for surface roughness estimation

This review of existing literature is therefore focused on optical methods for surface roughness

estimation of machined workpieces, specifically those that utilized computer-vision as the core

method of acquisition. A common approach in optical techniques is to obtain information by light

scattering - reflecting a laser or focused light off a surface and analyzing the distribution pattern

[64-67]. These methods deal with very smooth surfaces (<1 µm) machined by polishing or lapping,

and thus are not suitable for the applications pursued by this work, as the common roughness range

for turning is generally between 1 and 10 µm (Ra).

Computer vision techniques focus on obtaining image features that can be directly or indirectly

correlated to the quality of the surface being examined, whose chosen indicator is usually the

arithmetic surface roughness (Ra). The information extracted from the image is most often

classified as a first order or second order feature, dealing with direct single-pixel features or

14

pairwise pixel comparisons, respectively. The most common method of surface quality estimation

is to obtain gray level information from the image, and then to use this value (or a derived feature)

as a direct correlation with surface roughness [68-76]. Several works utilize neural or polynomial

networks for the construction of a prediction algorithm [74, 77-80]. A few noted works use features

derived through the Fourier or Wavelet transform of the image, as well [80, 81].

2.2.2 Shortcomings in the existing literature

The most commonly examined surface roughness range is between 0.10 and 4.0 µm (Ra), with

another common range being from 2-16 µm (Ra). By far the most prominent and limiting drawback,

often in studies with successful prediction, is in examining only a very narrow, smooth roughness

range (<2 µm) [70, 73, 74, 76, 82]. Several studies dealt with very wide-ranging roughness (45-

200, 25-60 µm), but often had large gaps of ~3-10 µm between each tested roughness value. These

works with a wider roughness range show a possible lack of precision for distinguishing 1-2

micron changes [68, 80, 81]. This lack of precision is undesirable.

In addition, some of the most successful prediction techniques require prior knowledge of the

cutting parameters for the workpiece, which limits application in a process control setting [72, 77-

79]. Methods which rely exclusively on image-derived data are able to be used without prior

process information, making these solely image-based approaches more attractive.

Two of the most notable successful approaches are those developed by Tsai et al. [80] and

Chang et al. [81]. Tsai et al. used the Fourier transform of the image in conjunction with a neural

network, for shaped and milled steel specimens with roughness of 6.3-100 and 1.6-50 µm,

respectively. Chang et al. utilized features extracted from the Wavelet transform of the image for

use in regression modeling of the surface roughness, examining shaped, ground, and polished steel

surfaces. Not all roughness values were explicitly listed, however the shaped steel specimens

15

varied from 2.032-15.469 µm. The speed of Tsai et al.’s method is stated to be ~2 sec, while the

speed of Chang et al.’s method is unspecified. However, the authors of these approaches did not

examine workpieces machined by turning.

In conclusion of this review of the existing literature, it is noted that very few prior studies

examined surfaces machined using turning. The most common material examined in prior work is

ground or milled steel, with only few examples of turned surfaces, which is the application of this

work’s method. Also, an almost universal shortcoming in the existing literature’s methodology is

a lack of online application of these acquisition techniques. The most notable differences between

this work and existing prediction methods are a more precise prediction range, and application in

turning. This research also demonstrates a clear potential for real-time, in-process implementation.

16

Chapter 3. Sensor-based Process Monitoring in Additive Manufacturing

The objective of this chapter is to demonstrate online monitoring of process conditions in

additive manufacturing using multiple sensors. The overall approach and structure of this research

has the following main parts (detailed also in Figure 3-1):

1) Experimental studies: Section 3.2 describes the investigation of the effect of three process

variables: extruder temperature, feed rate to flow rate ratio, and layer thickness, on the

build quality, evaluated as the arithmetic average surface roughness (Ra) of the workpiece,

using a design of experiments plan. It is notable to identify those process settings that lead

to build failures, and the physical root cause for these failures.

2) Sensor-based monitoring: The following Section 3.3 explains the design of a custom

sensor apparatus to record various aspects of the FDM process, using thermocouples,

accelerometers, a non-contact infrared (IR) temperature sensor, and a real-time video

borescope. Sensor data is acquired under various process conditions to learn how sensor

signal patterns are associated with different process states and the occurrence of build

failures.

3) Machine learning classification: Then, in Section 3.4 is shown the application of several

machine learning techniques for classification of the process state, based on the acquired

sensor data. This concludes the research by examining the potential of using such a

constructed sensor system in an online process monitoring scenario.

The experimental work of this research has also been utilized in an already published work [83],

in which a novel method using a nonparametric Bayesian DP mixture model and evidence theoretic

17

approach was used for classification of the process state. This research instead investigates the use

of existing machine learning classification algorithms for process state monitoring.

Figure 3-1. Summary of the overall approach regarding process monitoring in additive manufacturing

3.1 Process description of FDM

FDM is an additive manufacturing process in which an object is manufactured by depositing

progressive layers of extruded molten material [10, 13, 53, 84] (see Figure 3-2(a)). In FDM, the

material used for printing is most often ABS or PLA plastic filament. A spool of this plastic

material is fed through a heated mechanism (the extruder), and deposited in a layer by layer fashion

onto a heated platform (the “bed” or “table”). Figure 3-2(a) shows a diagram of this process; and

Figure 3-2(b) shows the attached sensors used for monitoring, described in detail later (see Section

3.3). Thermoplastic polymers, such as ABS and PLA, given their relatively low glass transition

temperatures (approximately 105°C and 65°C, respectively), are the materials of choice in FDM.

Although thermoplastic materials are prevalent in FDM, the process has evolved to accommodate

novel materials and application areas, e.g. mortar, clay, and lunar regolith [85, 86].

18

Figure 3-2. (a) Schematic of the FDM process; (b) Schematic of the FDM setup instrumented with multiple sensors used for measuring process conditions in real-time [83]

3.2 Experimental Studies in FDM

This section presents the conducted experimental studies. The objective of these experiments

is to understand the functioning of the process, and to identify part build failures (Section 3.2.3)

and understand their root causes (Section 3.2.4). The sensor data acquired during such build

failures is then used for machine learning classification (Section 3.4), to explore the potential for

online process monitoring.

3.2.1 Design of standard test artifact

Prior to conducting experiments using the FDM machine, it is important to select a standard

artifact. Different types of standard test artifacts have been suggested for evaluating AM processes,

e.g. by Mahesh et al. [50] and Kruth et al. [87]. Recently, the test part developed by Moylan has

been proposed for universal standardization for benchmarking different AM processes and

machines [88]. However, it was found that these parts could be cumbersome to produce on a

desktop FDM machine, such as the MakerBot Replicator 2X used in this work. The MakerBot has

a maximum layer resolution of 0.1 mm. Consequently, intricate features are difficult to produce

on this experimental setup. Therefore, it is necessary to design a custom test artifact that functions

19

well with the available machine. The test workpiece designed in this research is shown in Figure

3-3. It resembles the NAS 979 standard test artifact, and also is popularly known as the circle-

diamond-square test, which is widely used for assessing accuracy of CNC machine tools [89]. At

0.3 mm layer height this part takes between 25 to 35 minutes, while at 0.2 mm the build time is

close to 50 to 55 min; the exact times are contingent on the chosen feed rate, which for this case is

in the range of 40 to 50 mm/sec.

Figure 3-3. Test part resembling the NAS 979 standard part used for experimental trials (note: dimensions are in inches) [83]

3.2.2 Selection of process input and output variables

The experimental tests were conducted to understand the functioning of the process, and to

observe the range of feasible operating conditions of the parameter settings. The process output

variable was chosen to be the overall arithmetic mean surface roughness (Ra) of the workpiece,

which is the average of surface roughness (Ra) measurements taken at 10 different predetermined

locations. These measurements were recorded using a profilometer (Precision Devices Analog

Surfometer). Based on prior literature, the process input variables were selected to be the ratio of

20

feed to flow rates, layer height, and extruder temperature [48, 51, 52, 90]. Here is explained the

physical reason for choosing the particular settings for each of the process input variables.

1) Feed rate (fr) to flow rate (fw) ratio (𝑣): Feed rate (fr) is the movement speed of

fabrication; it is the velocity at which the extruder travels. Flow rate (fw) refers to the rate

at which material is being pulled through the extruder nozzle (Figure 3-2(a)). As described

in the literature review (Section Chapter 2), Peng et al. [51] have found that veering farther

away from a 1:1 ratio for the feed rate to flow rate leads to extruder clogs and failed builds.

This ratio is denoted by the symbol 𝑣(=fr/ fw). Preliminary studies were conducted to find

the appropriate operating range for 𝑣. The exploratory experimental trials indicate that

choosing 𝑣< 0.75 and 𝑣 > 1.25 invariably leads to build failures irrespective of the setting

of other variables. At the lower end, i.e. 𝑣< 0.75, the deposited layer is thick enough to

make contact with the extruder head on subsequent passes. While at higher values (𝑣>

1.25) the deposited layers are of uneven thickness and the extruded material is inconsistent;

the build frequently features voids, and the material often peels off and sticks to the

extruder causing the nozzle to clog. Therefore, the ratio of feed rate to flow rate (𝑣) is

selected in the range of 0.8 ≤ 𝑣≤ 1.2 for further investigation.

2) Layer height (h0): Layer height (ho) refers to the nominal distance setting between each

deposited material layer; it is effectively the distance by which the build table retracts

(moves down) between layers of the build. The lower the layer height the better the

resolution of the build, and thus the finer the feature detail. However, there is a lower limit

to the layer height, which is primarily governed by machine parameters, such as the

resolution of the motor coupled to the table (bed), the quality of the lead screws driving the

table, the stiffness of the machine structure, and the skew of the table with respect to the

21

nozzle. It was found that ho less than 0.2 mm on this machine often led to the nozzle making

contact with the workpiece. If the layer height is set too high (ho > 0.4 mm in this case),

the thickness of the extruded material will be less than the movement of the table, and as a

result the material is more liable to be deposited unevenly on the previous layer. Therefore,

ho was maintained in the range of 0.20 mm ≤ ho ≤ 0.30 mm.

3) Extruder temperature (te): The extruder temperature (te) governs the viscosity of the

extruded material [51]. At higher te, occurring in the 250 °C – 260 °C range on the

MakerBot machine, the extruded material is far too liquefied, and therefore may not

solidify quickly and will not adhere well to the previous layer. Consequently, there is a

tendency for the material to adhere to the nozzle on subsequent passes over an

insufficiently solidified part of the surface. This may cause the nozzle to have extraneous

material at the tip which then scrapes fresh layers, in the process damaging the part quality

and sometimes leading to nozzle clogs. On the other hand, at te < 225 °C, the extruded

material is quite viscous, and as a result, the material can fail to extrude from the nozzle,

leading to a failed build. Consequently, the extruder (nozzle) temperature was maintained

in the range of 230 °C ≤ te ≤ 245 °C for the experimental tests.

3.2.3 Identification of process conditions leading to build failures

The feed to flow rate ratio (𝑣) was chosen to vary at 3 levels, the layer height (h0) at 2 levels,

and the temperature of the extruder (te) at 2 levels (see Table 3-1). The effect of these three factors

(process input variables) on build integrity was tested using a full factorial design with 12 (= 3 ×

2 × 2) treatment conditions (TCs) plus one center point (repeated three times) for a total of 13 TCs

and 15 runs (= 3 × 2 × 2 + 3 × center point), as shown in Table 3-1. In all trials, the flow rate was

set at 50 mm/s, while the feed rate varied between 40 mm/s, 50 mm/s, and 60 mm/s, resulting in 𝑣

22

of 0.8, 1.0, and 1.2, respectively. The layer heights (h0) tested were 0.2 mm, 0.25 mm (center

point), and 0.3 mm. The temperature of the extruder (te) varied with settings at 230 °C, 237.5 °C

(center point), and 245 °C. For these experiments the ambient temperature of the build chamber

was maintained at 32.5 ± 2.5 °C.

It should be noted that the experimental design does not have equal levels for all three factors.

This is because preliminary exploratory experiments had two levels for all three factors, giving a

total of 8 (= 2 × 2 × 2) treatment conditions. These are conditions TC1 to TC8 in Table 3-1.

However, the failure region of TC7 and TC8 was encountered, which led to an extension of the

feed to flow rate ratio (𝑣) to a third level in order to facilitate further exploration of the process

(seen as conditions TC9 to TC12). Three replications of these 13 TCs were conducted. Each

replication used a different filament color (𝒸), namely, red, white, and blue. Essentially, this is akin

to blocking the design on replicates based on the filament color [91].

Table 3-1. Full factor design of experiments plan for FDM tests (blocked on replicates by filament color)

Treatment Condition

(TC)

Feed/Flow Rate Ratio (mm/sec)

(𝑣)

Layer Height (mm) (h0)

Extruder Temperature

(°C) (te)

Remarks

TC1 50/50 0.3 245 TC2 50/50 0.3 230 TC3 50/50 0.2 245 TC4 50/50 0.2 230 TC5 40/50 0.3 245 TC6 40/50 0.3 230 TC7 40/50 0.2 245 Failed build in five

out of eight trials TC8 40/50 0.2 230 TC9 60/50 0.3 245

TC10 60/50 0.3 230 TC11 60/50 0.2 245 Gives lowest surface

roughness (Ra) TC12 60/50 0.2 230

TC13 50/50 0.25 237.5 Center Point with three repetitions

23

Results from the experimentation (see Figure 3-4) show a clear optimal region (TCs 11 and 12),

as well as a consistent failure region (TCs 7 and 8). Based on Figure 3-4 the least surface roughness

(Ra) is obtained at = 1.0 to 1.2, h0 0.20 mm, and te = 237.5 °C to 245 °C. The complete results

are tabulated in Table A-1. It is straightforward to build a regression model to predict Ra based on

this data using a technique such as response surface methodology [91]. Many examples of such

statistical modeling and optimization studies are available in the AM literature [48, 51, 52, 92].

However, the goal of this present research is not to build a statistical model in order to optimize

the FDM process; instead it is desired to predict the onset of process anomalies, such as TC7 and

TC8, using sensor data (see Section 3.3). In the failed build conditions TC7 and TC8, the nozzle

clogged during the process for five out of eight trials. The physical causes for these failures are

discussed in detail in the following Section 3.2.4.

Figure 3-4. Diagram showing the mean surface roughness (Ra, μm) for each TC, averaged over three replications [83]

24

3.2.4 Analysis of build failure in FDM

The failure region at TC7 and TC8 provides important information regarding the FDM process

characteristics. Understanding the physical effects of the process input variables will allow one to

seek the optimal region and avoid build failure. Here are discussed the physical reasons for the

observed build failures, which are primarily determined by feed to flow rate ratio ( ) and layer

height (h0) settings.

A lower value for the feed to flow rate ratio ( indicates extrusion of material (flow rate) at a

faster rate than movement of the extruder head (feed rate). This results in thicker layers and a

gradual buildup of extruded material around the extruder head, typically leading to clogs (Figure

3-5). This condition, if allowed to continue, will lead to filament breakage. Because of breakage,

a portion of the filament will remain lodged inside the extruder. Clearing out such clogs is an

involved process, requiring disassembly of the extruder mechanism. From a productivity

perspective, it is valuable for such failure to be prevented.

Figure 3-5. Schematic representation of FDM failure due to nozzle clogging [83]

25

In contrast, the observed optimal region (see Figure 3-4) has an increased 𝑣(= 1.2), and the

faster movement of the extruder (feed rate) allows more room for the material to be deposited on

the layer, resulting in relatively smoother layers and no clogging. As mentioned previously in

Section 3.2.3, the varying effect of 𝑣 on extrusion success is explained by Peng et al. [51].

Layer height (h0) is the second significant process parameter relating to build failure. A higher

layer height setting (= 0.3 mm) means that at each successive layer the table moves down by 0.3

mm, causing the filament to be extruded farther from the previous layer, therefore increasing the

average height of each road (leading to a rougher appearance). Conversely, the lower layer height

of 0.2 mm results in a lower Ra, as the material is deposited closer to the previous layer and the

between-layer road width is decreased. However, h0 = 0.2 mm typically leads to failed builds at a

feed to flow rate ratio (𝑣) of 0.8, as indicated in Figure 3-4. This is due to the close proximity of

the extruder nozzle to the layer, increasing the possibility of clogging.

26

3.3 Sensor-based Monitoring of FDM

3.3.1 Sensor platform for real-time monitoring of FDM process conditions

The MakerBot Replicator 2X FDM machine was instrumented with multiple sensors, which

give quantitative information about the process in real-time (see Figure 3-2(b)). The technical

details of the various sensors used in this research are provided in Table 3-2.

Table 3-2. Sensor information and location

Temperature and vibration information was acquired at several different locations on the machine:

i. Extruder Temperature: A thermocouple was placed directly on the extruder using a high

temperature adhesive (Figure 3-6 (a)).

ii. Meltpool Temperature: A non-contact infrared (IR) temperature sensor (Figure 3-6 (a))

was oriented to measure the temperature at the contact point where material is deposited

onto the workpiece surface, i.e., the so-called material meltpool.

iii. Borescope Video: Also visible in Figure 3-6 (a) is a borescope camera (15 fps), which is

used to record the entire printing process for each run. This camera gives up-close, live

video, which allows monitoring of the extrusion process visually from start to finish.

Sensor Placement Measurement Model Vendor

Thermocouple (x4) Table Table temperature 5TC-GG-K-20-36 Omega

Thermocouple Extruder head Extruder temperature 5TC-GG-K-20-36 Omega

Thermocouple Inside machine Ambient temperature 5TC-GG-K-20-36 Omega

Accelerometer (tri-axis) Table Table vibration ADXL335 Analog Devices

Accelerometer (tri-axis) Extruder arm Extruder arm vibration ADXL335 Analog Devices

IR temperature sensor Extruder head Temperature of deposited

material UIRT/C.4-440F Exergen

Borescope Extruder head Real-time video N006 Supereyes

27

iv. Extruder and Table Vibration: Two 3-axis miniature MEMS accelerometers were used

to measure vibration on the table and on the extruder head. The accelerometers on the

extruder head and table are shown in Figure 3-6 (b) and (c), respectively.

v. Table Temperature: Four K-type thermocouples were attached to the table with two on

each side of the table as shown in Figure 3-6 (c). A thermocouple was also mounted inside

the machine enclosure to obtain ambient temperature measurements.

Figure 3-6. Heterogeneous sensor array for real-time monitoring of FDM process [83]

(a) Non-contact IR temperature sensor for measuring meltpool temperature, borescope camera

for real-time video capture capability, and thermocouple to measure extruder temperature.

(b) MEMS accelerometer to measure extruder vibration.

(c) MEMS accelerometer to measure table vibration, and thermocouples (x4) to measure table

temperature.

To acquire data from the sensors (excluding the IR temperature sensor and borescope) an

Arduino Mega 2560 microcontroller is used. Data from the six thermocouples and two vibration

sensors (3 channels each) is collated in real-time and streamed using MATLAB for visualization.

These 12 different sensor channels are synchronized and recorded at a sampling rate of ~ 2.5 Hz.

The IR temperature sensor is pre-amplified separately, and has a sampling rate of 1 Hz. In all,

including the borescope video data, 14 channels of sensor data are acquired. Figure 3-7 provides

28

the system diagram; including the FDM machine, the sensors, data acquisition layout, and

connection to the computer for data collection and subsequent analysis. Representative examples

of collected sensor data are shown in Figure B-1.

Figure 3-7. System diagram denoting the data acquisition methodology

29

Figure 3-8. (Top) The three observed process states in FDM, demarcated as normal, abnormal, and failure; (Bottom) The transition between normal, abnormal, and failure states, is evident from the time series pattern

of the non-contact IR temperature sensor [83]

By observing video recordings of the process from the borescope camera (Figure 3-8), three

typical working process states are identified from the experimental studies, these are:

i. normal operation of the process (Figure 3-8 (a));

ii. abnormal operation due to insufficient extrusion (Figure 3-8 (b)); and

iii. failure to extrude due to nozzle clog (Figure 3-8(c)).

The documented failures, TC7 and TC8, both exhibited clogging of the extruder nozzle (see

Figure 3-4). However, the nozzle clog progresses gradually. The process remains in the normal

30

state for the initial third of the layers (~ 15 minutes) with stable extrusion. Thereafter the extrusion

tends to be intermittent; at this stage the extruded material is relatively less consistent (almost

stringy), and this is termed the abnormal process state. Finally, the extrusion process completely

stops or fails on account of a nozzle clog. It would be desirable to detect the process drift as it

progresses from normal to abnormal, so that corrective action can be taken and failure can be

avoided. It should be noted that further investigation of such corrective action, although valuable

for future study, is outside the scope of this research. Each of these process states manifests in a

pattern recognizable on the IR temperature sensor data (see Figure 3-8).

3.3.2 Discussion of acquired sensor data

Having understood the process states from the results of the previously described experiments,

focus is turned to those factors (i.e. sensor data channels) which are or are not suitable for use in

constructing a classification model.

First, the IR temperature is clearly observed as changing with respect to the change in process

state (seen in Figure 3-8). This coincides with understanding of the process, being that if the

process fails, material is no longer deposited onto the surface of the part being constructed. The IR

temperature is a measurement of the area directly underneath the extruder mechanism, therefore it

follows that IR temperature would decrease if no material is currently being extruded. No other

sensors have such a clearly visible change. For this reason, IR temperature was chosen as the initial

feature to be included in all classification models.

When considering the mechanics of the process, as well as the conducted experiments, certain

factors are not beneficial to use in attempts to build a classification model. These factors and

parameters are described here:

31

1) The feed/flow rate ratio and layer height parameters are equal for all failed experiments

(TC7 and TC8), therefore these parameters are not considered as factors for

classification.

2) Replication number essentially describes the different color of material used in the

process. Attempting to use this as a factor would not yield useful information. This is

because replication 1 contains two failed and two normal conditions, replication 2

contains one normal and one failed condition, and replication 3 contains two failed

conditions. Due to the low number of experiments within each replication, it is

reasonable to suspect that the existing data is not an accurate representation of the real

population of experiments from each replication. There are an uneven number of

experiments from each condition and each replication, due to time constraints when the

experiments were originally conducted.

3) Extruder temperature, as a parameter, was set at different levels for the TC7 and TC8

experiments. Therefore, we expect the sensor recording this data to show this consistent

difference. Also, similarly to considering the replication number, there are an unequal

number of TC7 and TC8 experiments (five of TC7, and three of TC8).

4) When examining the process in general, the following conclusions can be drawn:

o A failed experiment will begin at normal, then progress to abnormal, then to failure.

o Ambient temperature will tend to increase over time.

o Bed temperature will tend to increase over time.

The machine has an enclosed chamber, which naturally heats up as the process is running. This

means that failed conditions will appear to indicate that failure is more likely if ambient or bed

32

temperature increases, which is not necessarily a valid conclusion, as temperature increases over

time in successful experiments as well.

The candidate features that remain, in addition to the IR temperature sensor data, are the six

channels of sensor data from the accelerometers. The objective of the subsequent section (Section

3.4) is to investigate classification of the three process states defined above, using the data acquired

from different sensors, and then to examine application of this classifier for real-time process

monitoring.

3.4 Machine Learning Classification of FDM Process States

This section describes the application of machine learning classification methods to the process

and data introduced previously, in order to show the potential of this platform for use in future

development of online, real-time process monitoring in AM. The following Figure 3-9 shows a

diagram summarizing the overall classification approach.

Figure 3-9. Summary of the machine learning classification approach

33

3.4.1 Machine learning classification algorithm selection

The chosen techniques for model construction and classification are:

i. SVM (linear)

ii. SVM (using radial basis kernel function)

iii. LDA

iv. QDA

v. Logistic Regression

vi. Naïve Bayes

vii. Random Forest

These are chosen as representative, mature classification methods that currently exist in the

field of machine learning. Although different or novel approaches may be applicable, as seen in

the already published work utilizing these experimental results [83], as well as a separate

publication utilizing the concept of sparse estimation of sensor data [93], the intended purpose of

this research is to construct a platform which demonstrates potential to be used for future process

monitoring of defects and failure. Therefore, analysis and modeling was limited to the above listed

techniques.

3.4.2 Sensor data selection

Selection of sensor data to include as model features was conducted by sequential forward

selection, using 10-fold stratified cross validation, and observing the overall misclassification rate

of each model. Features were added while observing the decrease in overall misclassification rate,

stopping when improvement was less than 0.1%. In addition, the tuning parameter for the Random

Forest method (the number of trees) is seen to reach stability at 100 trees (see Figure C-1). The

candidate features (described in Section 3.3.2) are listed in the following table.

34

Table 3-3 List of possible features to be selected, along with the corresponding sensor

Sensor Feature

IR Thermocouple IR Temperature (initial included feature)

Extruder Accelerometer Extruder Vibration X axis, Extruder Vibration Y axis, Extruder Vibration Z axis

Bed Accelerometer Bed Vibration X axis, Bed Vibration Y axis, Bed Vibration Z axis

3.4.3 Results of classification modeling for identifying FDM process states

For each model type, classification was conducted using 10-fold cross validation. This was

done in three main divisions, (i) data from the TC7 experiments, (ii) data from the TC8

experiments, and (iii) all data including both TC7 and TC8 experiments. These results are shown

in Table 3-4, Table 3-5, and Table 3-6, respectively. Relative model success is seen through

examination of the misclassification rates and F-scores (also known as F1 scores).

The results shown in Figure 3-10 are in terms of each model type’s misclassification rate with

respect to the number of features included in the model. These results are divided into columns

based on the data used (TC7, TC8, or both TC7 and TC8); and rows based on specific

misclassification for separate labels (overall misclassification, misclassification rate for data

labeled as normal, then abnormal, then failure).

35

Table 3-4. Classification model results, using TC7 sensor data

36

Table 3-5. Classification model results, using TC8 sensor data

37

38

Table 3-6. Classification model results, using both TC7 and TC8 sensor data

39

Figure 3-10. Model misclassification rates

40

3.4.4 Discussion of machine learning classification in FDM

This section provides a discussion of the classification results, and notable conclusions that can

be drawn, as well as considerations for future applications of machine learning classification in

this particular process setting.

Modeling results

i. Using the TC7 sensor data, the SVM model (using radial basis kernel) provides the

highest average F-score of 0.6087, using three features (IR temperature, Z axis bed

vibration, and X axis bed vibration).

ii. Using the TC8 sensor data, the SVM model (using radial basis kernel) provides the

highest average F-score of 0.5746, using one feature (IR temperature).

iii. Using both the TC7 and TC8 sensor data, the Random Forest model provides the

highest average F-score of 0.5328, using three features (IR temperature, Z axis bed

vibration, and Y axis bed vibration).

It should be noted that these results (i.e. the F-scores) differ from those found for the same

model types in the published work [83]. This is due to the fact that the published work constructed

its models using individual experimental runs, whereas in this current research models were

constructed using multiple runs (e.g. multiple runs from TC7, as opposed to a single TC7 run).

Also, labeling of the sensor data (i.e. as normal, abnormal, and failure) was conducted

independently in this work, introducing some variation in which data was and was not labeled

accordingly, particularly for the difficult to identify abnormal process state.

Sensor data selection

In addition to the originally chosen IR temperature, subsequent additional features did not

generally appear to significantly improve the average F-scores. Although significant improvement

is not evident in every case, the marginally improved model classification F-scores, due to

41

inclusion of additional features, follows from a physical understanding of the process. As

previously discussed, during process failure, the deposited material begins to block the extruder

nozzle. As well as stopping the extrusion of new material, this can lead to the extruder nozzle

scraping the part itself, which can perhaps be seen in vibration of the bed, and of the extruder itself.

However, this effect appears to be slight, and in addition the accelerometers used have a relatively

low resolution and sensitivity. In the future, higher quality sensors may provide more useful data.

Data selection

A major question to ask is whether to combine or separate the TC7 and TC8 data, as they only

differ in a single parameter setting (extruder temperature). Ideally a single model could be

constructed which applies to all cases of parameter settings in the FDM process. However, based

upon the current results, it seems that models tend to perform better when trained and tested using

separated data sets, particularly in the failure case. In the case of this research, it is not wise to

combine the TC7 and TC8 data sets because there are not an equal number of experiments from

each. When combining all of the data, it could be the case that a model performs extremely well

on TC7 but quite poorly on TC8. If the quantity of data is weighted in favor of TC7, this would

skew the apparent accuracy of the model. However, for future work, testing of classification

methods will need to be conducted for data collected during any parameter conditions.

Data preprocessing

In this research, the raw sensor data was utilized, in order to avoid the loss of potentially useful

information. In future work, data preprocessing may be investigated, such as smoothing of the

data, or selecting particular features to be used, such as the mean and variance. However, care must

be taken to avoid any loss of information.

42

Abnormal process state

A key conclusion of this research is recognition of the abnormal process state. Recognition of

the abnormal state is potentially very important from a quality monitoring perspective, since if we

can detect this in real-time, it provides the opportunity for corrective action or maintenance. It is

observed that constructed models perform very poorly when attempting to classify the abnormal

state. There are multiple reasons for this. First, it should be noted that of all collected experimental

data only 5.6% is designated to be from the abnormal state. This means there is relatively very

little data that can be used for construction of a model. Also, classifying the process state as

abnormal is difficult to do from a human perspective, as this is done by observing the recorded

video and noting roughly at what time the extrusion of material seems to deviate from normal. It

is quite difficult to determine the exact time the process transitions between the states. In this

regard, identification of the abnormal state is a key area that should be investigated in any future

work.

43

Chapter 4. Image-based Online Monitoring of Surface Finish in Turning

The objective of this chapter is to demonstrate near real-time monitoring of the surface finish

of turned workpieces, using an image-based approach. The overall structure of this research has

the following main parts:

1) Discussion of Surface Quality Quantifier: Section 4.2 details the background of the

graph theoretic quantifier used to characterize the surface morphology of turned

workpieces. It provides a summary of the reference work published by Rao et al. [22],

which demonstrates its capability for use in characterization of the morphology of ultra-

precision engineering components, specifically copper wafers processed by chemical

mechanical planarization.

2) Methodology: The following Section 4.3 describes the overall methodology of this

research, discussion of the workpieces chosen for analysis, the physical setup of the image

acquisition system, processing of the acquired images, and the regression modeling for

prediction of the surface finish.

4.1 Limitations of the State-of-the-art

Online surface finish assessment is challenging, particularly more for conventional machining

than for precision surfaces. This is because high-resolution optical techniques, e.g. laser light

scattering and laser speckle techniques, are limited by the wavelength of visible light [16, 64-67,

94-97]. These approaches are not physically capable of measuring surfaces whose root mean

square roughness (i.e. Rq, Sq) is greater than the wavelength of light (~ 400 to 700 nanometers)

[94, 96-99]. The typical roughness range for conventional machining processes, e.g. turning,

44

milling, shaping, planing, drilling, etc., is generally between 0.5 and 10 micrometers [20, 95, 96,

100]. Therefore, most high-resolution optical methods are more suitable for measurement of

precision, sub-micron (<1 micrometer) and ultraprecision (<50 nanometer) surface finish, such as

those obtained from polishing, lapping, and diamond turning [94-97, 101].

In addition, it is infeasible to incorporate these techniques into a conventional machining setup,

with its relatively harsh operating conditions (vibration, chip formation, coolant splashes, etc.).

Consequently, non-contact assessment of surface morphology for conventional machining

remains an open research problem. To address this, researchers have sought to use indirect sensor-

based techniques, e.g. vibration, force, and acoustic emissions, for online assessment of surface

quality in various machining operations [94-97, 101, 102]. This current research uses captured

images, and therefore has the potential to overcome the common difficulties in online monitoring

of conventional machining operations.

4.2 Discussion of Surface Quality Characterization Metric

This section details the background and basis of the surface characterization approach used in

this research, by providing a summary of the relevant portions of the reference work published by

Rao et al. [22]. The authors utilize optical micrograph images for quantification of surface

morphology variations of copper semiconductor wafers. They discuss key shortcomings of current

analytical approaches used for surface morphology quantification, and note the need for an

approach that is non-contact, preferably optical, and has the ability to capture the underlying

morphology of the surface. Rao et al. therefore propose an algebraic graph-theoretic approach for

interpreting the morphology of a surface based on its topographical connectivity. (The robustness

of this approach may be noted in that this graph theoretic quantifier has also been used recently,

45

in quantifying the dimensional integrity of AM constructed parts, through the use of 3D point

cloud coordinate measurements [103].)

The background and focus of their work is on the surface morphology of ultraprecision

engineering components, specifically semiconductor wafers produced by chemical mechanical

planarization (CMP). Rao et al. demonstrate that traditional statistical quantifiers can be

inadequate for capturing surface morphology variations.

4.2.1 Methodology and background of surface quality metric

There are two phases in the approach used by Rao et al. The first regards representation of the

raw input measurement (e.g. a 2D image) as a network graph, and the second uses the Fiedler

number to quantify the connectivity of this network graph.

The first phase is representation of the image as a network graph. Each row (or column) of the

image is considered to be an individual node of the graph. Row-wise pixel comparisons, as

opposed to individual pixel comparisons, are chosen in order to reduce the computational load.

First, pairwise comparison metrics wij (of the nodes) are computed using a kernel function. Then,

a threshold function converts wij to binary form, i.e. either 1 or 0. This is defined as the similarity

matrix S. The threshold function used to construct the similarity matrix is a manually tuned

heuristic. Rao et al. note that using a threshold function which is set equal to the average of wij

yields superior results, based upon both numerical and experimental studies. Two parallel

approaches are proposed for pairwise comparison, to obtain the similarity matrix S.

The first method is termed “ɛ-neighborhood graph representation”, and involves a preliminary

step of converting the original image, using Canny edge detection, into a binary image. Once the

Canny filter is applied, pairwise comparison of the binary image is conducted using the Euclidean

kernel function, yielding a similarity matrix in which the nodes are connected relative to their

46

topological closeness. This results in the ɛ-neighborhood graph being suitable for capturing surface

defects, such as scratches, pits, voids, etc. However, surface texture information is lost due to the

initial filtering of the image.

The second approach is termed “edge-weighted graph representation”, which uses the raw

grayscale image directly. In this case, the pairwise comparison is computed using a Gaussian radial

basis function, meaning that nodes are connected directly based upon their similarity of texture

(i.e. their relative pixel contrast). A similarity matrix constructed using this method compares the

actual surface texture between pixels of the image.

Once the similarity matrix of the image is obtained, the second phase uses the graph-theoretic

topological invariant Fiedler number to quantify the connectivity of the network graph. First, the

diagonal degree matrix of the graph is computed. The degree of an individual node is simply the

count of the number of edges that are connected to that node (e.g. an isolated node has degree 0).

The similarity and degree matrices are used to form the combinatorial Laplacian matrix. Then the

combinatorial Laplacian and degree matrices are used to form the normalized Laplacian matrix.

The smallest non-zero eigenvalue of the normalized Laplacian is called the Fiedler number. For

further detail regarding the formulation of the Fiedler number itself, the reader may refer to the

work by Rao et al. [22], as well as the original work by Fiedler [104].

Intuitively, the Fiedler number measures how easy it is to isolate a subset of nodes in a graph,

i.e. the “weakest links”. A more homogeneous image results in a smaller Fiedler number,

indicating that the nodes are difficult to isolate. And conversely, a relatively larger Fiedler number

indicates a more heterogeneous image, with nodes that are easier to isolate. The obtained Fiedler

number can be used to quantify the morphological characteristics of a surface, by using an optical

image.

47

4.2.2 Case studies and simulations verifying surface quality metric

The authors use several simulations of different surface morphologies to show that the Fiedler

number is capable of reflecting the changes, where conventional statistics are unsuitable. They

examine: 1) surfaces with different morphologies but identical feature density, 2) surfaces with

identical morphology but varying feature density, and 3) surfaces with different autocorrelation

lengths (Sal), but are statistically identical with respect to the first four statistical moments of their

peak heights (Sa, Sq, Ssk, and Sku). The Fiedler number’s superiority was also verified using real

CMP wafer surface data.

4.2.3 Fiedler number applicability to turning process

Having understood the capability of the Fiedler number in its application to ultraprecision

surfaces such as CMP processed wafers, this current research shows the Fiedler number’s

capability in application to the conventional machining process of turning.

There are two techniques used for the first phase of the authors’ method, termed the ɛ-

neighborhood graph representation, and the edge-weighted graph representation. As previously

mentioned, the edge-weighted approach is noted to be suitable for characterizing surface texture;

and the ɛ-neighborhood graph representation is useful for capturing prominent surface defects such

as scratches, voids, etc. This makes it appropriate for use in characterization of the surface of

turned workpieces, as the toolmarks are quite evident, resulting from the machining process. These

toolmarks are analogous to surface defects, and this essentially allows the turned workpiece to be

evaluated based upon the density of these toolmarks. From this perspective, we may use the Fiedler

number technique as the basis for the methodology described in the following sections of this

work.

48

Rao et al. demonstrates that, in high-precision applications such as CMP, the Fiedler number

outperforms conventional surface roughness parameters. In the case study examining three types

of surfaces with different autocorrelation lengths (Sal), these conventional parameters are identical,

while the Fiedler number captures the changes in surface morphology.

For the turning process, one can imagine that using a simplistic metric (e.g. counting the number

of toolmarks) may be an approach that perhaps yields results, but there is no guarantee such an

approach would be feasible in more complex scenarios. However, the Fiedler number has been

shown to be flexible and sensitive to subtle changes in surface morphology. In other words,

demonstrating the capability of the Fiedler number in this case provides a foundation for future

application in more challenging situations.

4.3 Methodology of Image-based Approach for Surface Finish Monitoring

This section, describing the methodology of this work, is organized as follows: discussion of

the workpieces chosen for analysis, the setup of the image acquisition system, and the processing

and analysis of the acquired images. Steps of the methodology are detailed in Figure 4-1.

Figure 4-1. Summary of the overall approach regarding image-based surface finish prediction in turning

4.3.1 Workpiece used for study

The chosen workpieces are turned steel shafts (AISI 4340) with 1 inch and 3 inch diameters.

These two workpieces were provided by the Commonwealth Center for Advanced Manufacturing

49

(CCAM), and various roughness levels were achieved by varying the feed rate, as shown in Table

4-1. Representative images of the workpiece segments are shown in Figure 4-2.

Table 4-1. Workpiece conditions

Feed (in/rev) 1 inch diameter Roughness (Ra)

(µm)

3 inch diameter Roughness (Ra)

(µm)

0.0140650 10.2345 10.0000 0.0135800 9.2870 9.8920 0.0126100 8.2630 8.6565 0.0114460 6.5600 7.2665 0.0104760 5.2200 6.2115 0.0095060 4.1840 5.1340 0.0087300 3.5705 4.2875 0.0077600 2.6315 3.4600 0.0067900 2.1140 2.6935 0.0055775 1.3585 1.8335

Figure 4-2. Representative images of the 3 inch diameter workpiece, with the corresponding roughness measurements (Ra)

50

4.3.2 Image acquisition setup

The image acquisition system consists of a CCD camera (specifications shown in Appendix D)

and a light source (AI Diffuse Axial LED Illuminator) affixed to the slide on a small manual lathe,

for images captured in Virginia Tech’s lab (of the 1 inch diameter workpiece), shown in Figure

4-3. Images of the 3 inch diameter workpiece have been acquired at the CCAM research lab by

attaching the camera to the turret of an industrial CNC lathe, using the built-in lighting of the

machine (see Figure 4-4).

Figure 4-3. Experimental setup at Virginia Tech’s lab

Figure 4-4. Experimental setup at CCAM’s lab

51

Positioning of the camera with respect to the workpiece was carefully determined, with the

desire to achieve the highest possible image resolution. Due to the nature of the workpiece, distinct

glare was reflected off of the surface, rendering certain portions of the image unusable for analysis.

In addition, portions of the image demonstrated the apparent distortion of the toolmarks along the

surface, noted in Figure 4-5. We ideally desire a flat surface, meaning that cropping and rotation

of the image are needed to produce a final image with parallel toolmarks and even illumination.

Figure 4-5. Glare and toolmarks evident on the surface of the workpiece

4.3.3 Processing and analysis of images

Acquired images are processed and analyzed using MATLAB, with the overall procedure

shown in Figure 4-6. For each workpiece (i.e. any new camera position or lighting condition),

calibration is required to choose the crop area for an evenly illuminated portion of the image, the

rotation angle needed to achieve near-vertical toolmarks, and the proper parameters for the Canny

edge detection filter. Once calibration is finished, the final Fiedler number calculation can be

performed. For image acquisition, the workpiece is mounted on a lathe and rotated to simulate

capturing images in an actual turning process. For image acquisition on the lathe in Virginia Tech’s

52

lab, the camera was within 1.0-1.5 inches of the surface of the workpiece, while the position of the

camera on the CCAM lathe was 1.5-2.0 inches from the surface. The camera’s field of view is

aligned for each segment (i.e. each Ra condition), and 30 images are captured, manually moving

the camera to each subsequent segment. The Fiedler numbers calculated from these 30 images are

correlated with the surface roughness (Ra) and used for construction of a regression model to

evaluate the prediction capability (see Section 4.3.4).

Figure 4-6. Analysis procedure for surface roughness estimation

Determination of the crop area is done heuristically, avoiding areas of the workpiece surface

affected by glare from the light source. Crop size is limited partially by computation time, notable

because the intended goal of this research is to develop a method which can in the future be applied

as an online, real-time measurement system. Although a larger image size generally improves

53

roughness prediction, it also directly increases computation time. Maximum size of the cropped

image is also limited by the need to avoid areas obscured by glare.

Image rotation is conducted based upon the need to align workpiece toolmarks vertically, as the

nature of the Fiedler number calculation is a column-by-column pixel comparison. The rotation

angle was chosen by assigning the sum of column variance as a “cost” for each particular angle; a

minimum variance score indicates near-vertical toolmarks.

Canny edge detection was used to extract texture primitives from the acquired image, in order

to prepare the image for the final step in analysis. Parameter choice for the Canny filter appears to

have the largest effect on the final successful prediction of surface roughness. These parameters

are the sigma value, designating the size of the Gaussian blur, and the threshold values for edge

detection. Canny parameter values are chosen which suitably limit noise in the images, while

preserving the characteristic toolmarks on the workpiece surface. In this research, these parameters

have been chosen heuristically, adjusted until the output roughness prediction is considered

sufficient. This initial heuristic calibration, although time consuming, only needs to be conducted

once for a given camera placement or lighting condition.

The key element in this approach for surface roughness prediction is the use of the

characterization parameter termed the Fiedler number (see Section 4.2). This quantifier is

calculated from the Canny filtered image, giving an associated output for each particular image.

This output value is directly used for comparison, and correlation with the obtained surface

roughness (Ra) of the workpiece. The computation time for this metric is highly dependent on

image size. For a typical 500x300-pixel image, total computation time is less than 2 seconds,

indicating suitability for real-time application. The results of analysis are discussed in the

following section.

54

4.3.4 Results of image-based prediction of surface roughness

Figure 4-7 plots the output Fiedler number (mean of 30 images) versus the respective measured

surface roughness (Ra) of each corresponding workpiece segment. Regression analysis is

conducted, with these final models constructed using the average of the 10-fold cross validation

model parameters. Table 4-2 shows the prediction results of the regression models, demonstrating

an overall average prediction error of ~7.85%. Further detailed results can be found in Appendix

E. Equations (1) and (2) show the constructed regression models, which are graphed in Figure 4-7.

Figure 4-7. Fitted regression models using the 1 inch and 3 inch diameter workpieces; Error bars denote the 95% confidence interval bounds of the mean predicted Ra value, based upon the 30 acquired images of each workpiece segment

Table 4-2. Regression model test error

Error 1 inch 3 inch Relative error (mean) (%) 8.1807 7.5269 Absolute error (mean) (µm) 0.2853 0.3956

RMSE (µm) 0.3321 0.4976

𝑅+ = 1.3103 +9.1735

(1 + 1044.5678(6.9558:;<=>?=@))6.A7BC (1)

𝑅+ = 2.0384 +11.0191

(1 + 10B8.G7BG(6.566G:;<=>?=@))6.48A6 (2)

55

Chapter 5. Summary and Future Work

This chapter summarizes the contributions of this research in sensor-based, online process

monitoring of additive manufacturing and turning, as well as the direction of future work in this

area. Multiple types of sensors are integrated, and these two methodologies are validated through

real experimental data and analysis.

5.1 Research Contributions to Process Monitoring in Additive Manufacturing

In order to successfully monitor the FDM process, this research develops and implements an

array of 14 in situ sensors on the machine, which provide data quantifying some of the most

important factors in FDM. Specific contributions are as follows:

1) Implementation of sensor platform for online process monitoring in FDM: A

heterogeneous sensor platform, consisting of accelerometers, thermocouples, an IR

temperature sensor, and close proximity video, is integrated with the MakerBot Replicator

2X FDM machine. This acquires the following process information: (i) bed (or table)

temperature; (ii) extruder temperature; (iii) ambient temperature; (iv) meltpool

temperature; (v) extruder vibration; (vi) table vibration; and (vii) video. This

comprehensive monitoring of the FDM process has not been attempted before.

2) Correlation of process input variables to workpiece quality: Experiments are conducted

to study the effect of three process input variables, namely, feed/flow rate ratio (𝑣), layer

height (h0), and extruder temperature (te), with the part build quality in terms of the surface

roughness (Ra). Also described are the physical effects of these variables on build failure.

With the current experimental data set, it is found that 𝑣 = 1.2, h0 = 0.2, te = 230 °C gives

the best surface roughness (Ra ~ 7 µm).

56

3) Classification of process state using machine learning techniques: Representative,

mature machine learning classification methods are applied, using the FDM process data

collected using the constructed platform. This provides useful conclusions which help to

understand the considerations that need to be made regarding future research and

application in FDM process monitoring. In particular, it shows the difficulty, and

importance, of recognizing the abnormal process state; it shows the value of the sensor

data, in particular the importance of the IR temperature data; and it shows that existing

methods, such as machine learning classification techniques, can be used in conjunction

with this platform for online process monitoring of AM.

5.1.1 Future work in AM process monitoring

A key conclusion resulting from this work is recognition of the so-called abnormal process

state, which describes the critical transition period between the normal and failure states. Detection

of process failure is essential, and it would be beneficial to avoid such failure if possible. This

would require detection of the abnormal state as soon as possible. For this purpose, further data

collection and improved sensor resolution are desirable, and are suspected to provide superior

results in future work. With respect to classification and analysis, future improvement may be

sought by considering the time series nature of the sensor data, as well as by exploring additional

classification methods. The next step of future study may be in replication of this sensor platform

using a different FDM machine. Even with the same sensors, and same experimental studies, this

would give more and better understanding of the process, and of nozzle clogging in particular, as

machines differ mechanically, even though the process is the same.

57

5.1.2 Acknowledgements

This research uses work conducted as part of a project funded by the following grant from the

National Science Foundation (NSF): CMMI 1436592. We would like to thank the machinists and

technicians at the Harris Manufacturing Lab at Virginia Tech, and we also thank the ISE senior

design team, Wade Andersen, Nick Bambino, Jake Snyder, and Diego Valdez, who helped with

the experiments.

5.2 Research Contributions to Online Monitoring of the Turning Process

This research presents a method for near real-time, online analysis of the surface finish of

workpieces finished through turning operations, using an image-based approach in conjunction

with a graph-theoretic quantifier. Benefits and key features of this proposed method are as follows:

1) Online incorporation of an image-based, non-contact monitoring method: A high

resolution grayscale camera is attached to both small and industrial-scale lathes, and utilized

to capture images of turned steel shafts. These images are recorded while the machines are

running, simulating actual operating conditions of the pre-machined workpieces. This

online, image-based monitoring of the turning process has not been attempted before.

2) Capability for real-time implementation, featuring rapid computation (~1-2 sec): The

captured images are quantified using the Fiedler number metric. The entire process occurs

in less than 2 seconds, demonstrating the capability for use in real production and quality

monitoring scenarios.

3) Ability to predict a wide and precise Ra range (1-10 µm): Regression modeling is utilized

to demonstrate the ability of the Fiedler number to be used to predict surface roughness of

a workpiece, with an average relative error of less than 8%. This is accomplished solely

using captured images, requiring no prior process knowledge.

58

5.2.1 Future work in image-based monitoring of turning

We note that calibration of the image analysis procedure, for the acquisition setups at the

Virginia Tech lab and the CCAM lab, suggests that this approach is stable to use in different

environments. Initial calibration is time-consuming, and must be conducted for each different

camera position and lighting condition. In particular, choosing the Canny filter parameters is

heuristic, and the prediction results are very sensitive to changes of these Canny filter parameters.

In future study, it may be worth investigating application of the Fiedler number method which

does not use the Canny filter.

Future work in applying this proposed prediction method can easily be pursued concerning

implementation in a live turning process, needing to account for process-specific features such as

coolant and chips, which may obscure the field-of-view of the camera. We may also pursue

application of this method to other materials finished by turning, other types of workpieces, and

surfaces machined by other manufacturing processes. This has already been demonstrated, in part,

in the measurement of copper wafers produced by chemical-mechanical planarization [105].

5.2.2 Acknowledgements

This research comes from a study sponsored by the Commonwealth Center for Advanced

Manufacturing (CCAM), as part of the project, “In-Process Surface Finish Measuring”. This work

was carried out under the supervision of Dr. James Kong, associate professor, Grado Department

of Industrial & Systems Engineering (ISE), and director of the Sensing and Analytics for

Innovative Manufacturing (SAIM) Laboratory at Virginia Tech. Finally, we would also like to

thank the machinists and technicians at the Harris Manufacturing Lab: M/s. P. Ratcliff, R.

Waldron, W.R. Monk, K. Snidow, and J. Linkous, at Virginia Tech for allowing use of their

machines and equipment.

59

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Appendix

Appendix A. Experimental studies in AM

Shown below are the results from the FDM experimental tests. The arithmetic average surface

roughness (Ra) is measured at ten different predetermined locations on the workpiece surface. The

average Ra over these ten locations is reported along with the standard deviation (shown in

parenthesis) in the table below.

Table A-1. Design of experiments plan, and corresponding experimental results

Treatment Condition

(TC)

Process Input Variables Process Output Variable (Surface Roughness, Ra)

Feed/Flow Rate Ratio (mm/sec)

(𝑣)

Layer Height (mm) (h0)

Extruder Temperature

(°C) (te)

Replication 1 Red filament

(mean Ra, µm) (std. deviation, µm)

Replication 2 White filament (mean Ra, µm)

(std. deviation, µm)

Replication 3 Blue filament (mean Ra, µm)

(std. deviation, µm)

TC1 50/50 0.3 245 15.56 (5.30) 13.69 (4.00) 12.82 (3.68)

TC2 50/50 0.3 230 16.60 (5.33) 15.08 (3.12) 14.75 (4.04)

TC3 50/50 0.2 245 8.61 (3.07) 7.63 (2.85) 6.64 (1.52)

TC4 50/50 0.2 230 6.72 (2.27) 7.63 (2.04) 10.77 (4.37)

TC5 40/50 0.3 245 24.88 (5.23) 21.38 (8.66) 25.14 (6.54)

TC6 40/50 0.3 230 27.99 (5.19) 29.61 (6.23) 26.43 (4.36)

TC7 40/50 0.2 245 21.80 (5.83) 19.92 (7.72) Failed to build

TC8 40/50 0.2 230 20.91 (6.77) Failed to build Failed to build

TC9 60/50 0.3 245 15.77 (3.78) 15.10 (3.26) 12.01 (3.69)

TC10 60/50 0.3 230 13.70 (3.15) 14.23 (3.55) 12.59 (3.54)

TC11 60/50 0.2 245 6.72 (1.90) 7.06 (1.52) 6.50 (2.59)

TC12 60/50 0.2 230 6.99 (0.85) 7.02 (0.79) 5.49 (1.28)

TC13 (Center Point)

50/50 0.25 237.5 11.96 (4.35) 12.71 (5.13) 9.84 (4.04)

50/50 0.25 237.5 12.02 (4.62) 11.87 (3.21) 7.91 (2.72)

50/50 0.25 237.5 12.43 (4.08) 13.31 (5.91) 9.63 (4.82)

67

Appendix B. Sensor-based monitoring in AM

Figure B-1. Representative examples of raw data, from a normal condition and a failed condition

68

Appendix C. Machine learning classification in AM

Figure C-1. Out-of-bag classification error vs. number of trees chosen, in Random Forest model type

69

Appendix D. Image acquisition in turning

Table D-1. Specifications of Point Grey Grasshopper 3 camera

Specification

Model Number GS3-U3-28S4M-C Imaging Device Progressive Scan Camera Sensor Format 1/1.8" Type of Sensor Sony ICX687 CCD Pixels (H x V) 1928 x 1448 Pixel Size, H x V (µm) 3.69 Pixel Depth 8, 12, 16 and 24-bit digital data Frame Rate (fps) 26 Type of Shutter Global shutter Electronic Shutter 0.03ms - 30s Image Format Mono8, Mono12, Mono16 Transfer Speed (Gbit/s) 5 Image Buffer 128MB frame buffer Memory (MB) 2MB non-volatile Flash memory Synchronization External or Via Software Video Output USB 3.0 interface with screw locks Mount C-Mount Dimensions (mm) 44 x 29 x 58 Operating Temperature (°C) 0 to +50 Storage Temperature (°C) -30 to +60 Weight (g) 90

70

Appendix E. Surface quality estimation in turning

Table E-1. Testing error of the regression model for the 1 inch diameter workpiece

Workpiece condition: 1 2 3 4 5 6 7 8 9 10

Actual Ra (µm) 1.3585 2.1140 2.6315 3.5705 4.1840 5.2200 6.5600 8.2630 9.2870 10.2345

Predicted Ra (µm) 1.6697 1.8517 2.4371 4.0805 4.0586 4.8144 7.0844 7.8456 9.2827 10.3329

Predicted Ra 95% CI (±) (µm) 0.0721 0.2310 0.2987 0.6639 0.6069 0.6229 0.6006 0.6074 0.4889 0.0446

Absolute Error (µm) 0.3112 0.2623 0.1944 0.5100 0.1254 0.4056 0.5244 0.4174 0.0043 0.0984

Relative Error (%) 22.9076 12.4078 7.3874 14.2837 2.9971 7.7701 7.9939 5.0514 0.0463 0.9615

Table E-2. Testing error of the regression model for the 3 inch diameter workpiece

Workpiece condition: 1 2 3 4 5 6 7 8 9 10

Actual Ra (µm) 1.8335 2.6935 3.4600 4.2875 5.1340 6.2115 7.2665 8.6565 9.8920 10.0000

Predicted Ra (µm) 2.0928 2.4549 3.1436 4.5552 5.0650 7.0239 6.7509 9.0341 9.8434 11.0508

Predicted Ra 95% CI (±) (µm) 0.2840 0.2131 0.3032 0.9566 1.0092 0.8334 0.6525 0.7350 0.4231 0.3204

Absolute Error (µm) 0.2593 0.2386 0.3164 0.2677 0.0690 0.8124 0.5156 0.3776 0.0486 1.0508

Relative Error (%) 14.1424 8.8584 9.1445 6.2437 1.3440 13.0790 7.0956 4.3620 0.4913 10.5080