Optimization of a pressure transmitter manufacturing line

Ashok Vydyanathan Electrical and Computer Engineering University of California, San Diego

San Diego, CA, USA. ashoksv@live.com

Anand Kumar, R. K. Mittal, Maneesha EEE, Mechanical, Mathematics

BITS Pilani, Dubai Campus Dubai, UAE.

{akumar, rkm maneesha} @dubai.bits-pilani.ac.in

Abstract— Assembly lines are sequential manufacturing processes, consisting of various stages at which parts are added to the product in a planned manner in order to create the finished product much faster than by handcrafting methods, and embodies the principle of the division of labor. At a multinational company in Dubai, an assembly line for pressure transmitters has been recently set up. This manufacturing line is stochastic and dynamic in nature due to a mixed automated and manual line. The operator takes less time for a manual stage with increasing learning. The line is a multi-product line with a line configuration that is neither U-line nor parallel lines. The product line follows more of a Workcenter model where the stations form a network of queues. The number of products completed at the end of the day is to be maximized while minimizing the queue waiting time at the stations where the bottlenecks occur. The bottlenecks occur, not due to large processing or cycle time of the station but rather due to the station being fed by batch processing stations. With this complex set up, the algorithm proposed is simulation based.

Keywords— assembly line, sequencing, balancing, mixed-model assembly, production planning and control

I. INTRODUCTION

A. Overview

An assembly line is a process paradigm where various parts of a product are added at various stages sequentially using planned techniques and logistics to create the product.

Assembly lines were originally developed as a cost-effective method of manufacturing standardized products which did not vary [7]. A modern assembly line can produce multiple products on the set of stations, by deploying different parts and tools at stations, and different station sequences. For example, BMW, a German car manufacturer, has a catalog that offers a theoretical 1032 models of BMWs and each model might require a different station sequence [4].

Assembly lines use a variety of techniques to produce multiple similar products on the same line, such as multi-purpose machines, swappable tools, and a variety of component parts. These lines have to meet varying customer demand, and as such, require optimization to achieve their maximum throughput. Optimization schemes have been developed for assembly lines.

The first is when setting up an assembly line is to balance the manufacturing load at each station [1][3][6]. This is done to remove bottlenecks, and to ensure that no single station is over-burdened at any point in time. Individual tasks required to assemble the product are analyzed, and the line is configured to have an optimal distribution of tasks across all the stations. This has been the core aim of Assembly Line Balancing, since its first mathematical formalization by Salveson, in 1955. However, modern approaches also aim to optimize the line keeping in mind practical constraints, such as the physical layout of the line.

The second step, once the assembly line is functional, is to manufacture products in an optimal sequence and mix in order to meet product demand [2]. The right numbers of each product type, keeping in mind its requirement in the market, have to be created. This process is known as Assembly Line Sequencing/Scheduling, and can be very complicated, depending on the line and product mix in question.

The main factors that increase the complexity of a sequencing problem are Problem size in terms of number of units to be produced, Multiple constraints and rules, Short time window for generating the sequence and Changes after sequence generation when new orders come in.

The type of algorithm used for sequencing also depends on the line itself, the type of product being manufactured (and the level of similarity between models), the types of stations available, and the amount of automation present in the line.

Based on the requirements presented by the market, there are different types of assembly line models in use today. Single-model assembly lines handle high volume production of a single commodity or multiple variations of a product requiring little setup time for the variations in equipment required. In mixed-model assembly lines [2][6], the setup times and operations between models can be reduced sufficiently enough to be ignored, so that intermixed model sequences can be produced on the same assembly line. These products are usually variations of the same base product, and only differ in specific customizable properties. In multi-model production, the similarity of the products being made and the processes they require is not enough to allow for short setup times and intermixing of different products in the production sequence. In order to

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avoid long setup times, production is organized in batches of different products, where each model is produced in lots of size decided according to orders and market demand. However, even in multi-model production, there must usually be a certain degree of similarity in the assembly operations of all the products, since they are manufactured using the same machines and resources.

B. Description of Product Line

The MNC in Dubai assembles the 2051, 3051 and 3051S series of pressure transmitters [7]. A pressure transmitter, as the name implies, measures the pressure of a fluid or gas, and transmits the readings to a receiving station. In its basic form it is a transducer, producing a electrical signal that is a function of the pressure being measured. The standard signal range for most industrial pressure transmitters is 4-20 mA, corresponding to the lowest and highest pressures measureable by the device.

Types of sensors, based on the measurement method, include Variable capacitance sensors , Piezoresistive sensors, Piezoelectric sensors, Variable inductance sensors, Variable reluctance sensors, Vibrating wire sensors, Strain gauge sensors

II. PROBLEM ANALYSIS

A. Assembly Line

Unlike many assembly lines in factories today, especially those where optimization studies such as this one are conducted, the pressure transmitter assembly line at MNC in Dubai is not fully automated.

The production begins, after an order is placed, with the printing of a traveler card. This is a sheet of paper with a bar code and details about the transmitter to be assembled. Each individual transmitter to be assembled has a corresponding travel card, and each station has a barcode scanner to check the transmitter in and out of each station as it is assembled. This provides with the means to monitor production, and allows us to extract useful data for studying and understanding the line.

Assembly begins with the operator at the first station picking up traveler card and scanning the barcode at the station. A monitor displays information relevant to that station and that transmitter. For example, at the first station (Module Pull), the monitor displays the required sensor module that the operator has to select from inventory. When the task is completed, the barcode is scanned again at the station to check it out of that station, and the partially assembled transmitter is passed to the next station with its traveler card and the process continues from station to station. The traveler card accompanies the transmitter throughout the entire line.

The stations that assemble the transmitter are, in order [7]: Module pull, Tag engraving, Housing installation, Board installation, High-potential testing, Functionality testing, Label installation, Grounding, Flanging/manifold installation. The testing and calibration stations are: P1 Test (Hydrostatic testing), P0 Test (Static pressure test), Leak Check, and Calibration/High pressure calibration. The final stations in the

line are: Final Pack 1, Final Pack 2, Out-of-box-quality (OBQ) packaging, check, certification.

III. OPTIMIZATION APPROACH AND ALGORITHM

A. Approach

One of the basic concepts that concern any assembly line is the cycle time of each station on the line. The cycle time of a station is the time it takes to perform its required operations on the partially assembled product. This is essentially the time that the incomplete product spends at that station.

The average cycle times for stations on the pressure transmitter assembly line are shown in Table 1.

These average cycle times are based on data retrieved from the MNC’s computerized system, and cover data for assembled transmitters over a time period of roughly two months.

Based on customer requirements, there are a very large number of combinations of modules and tests that can result in complete pressure transmitters. There is a model string that is generated for each transmitter on its travel card, and theoretically there is a large number of model strings possible.

However, on the actual line, there are only 10 unique scenarios through the line that a transmitter can follow. These are shown in Table 2.

Table 2. The ten unique scenarios that are possible in the assembly line. Task X includes Module Pull, Tag Engraving, Housing Installation, Board Installation, Hi-Pot Test, Functionality Test, Label Installation, Grounding and Flanging Stations. Task Y includes Final Pack 1, Final Pack 2 and OBQ stations. In scenario G and H, Leak Check precedes and follows P1 Test. In scenario I and J, Leak Check precedes P1 Test and follows P0 Test.

Station Name Average Cycle Time (seconds)

Module Pull 58.33325822 Tag Engraving 87.56524022

Housing Installation 101.0510571 Board Installation 60.0859 High-potential test 93.46496959

Functionality testing 57.89696139 Label Installation 51.34845776

Grounding 19.28495575 Flanging/Manifold

Installation 174.3519766

P1 Test 945.9354 Leak Check 136.2891813

Calibration (13A) 649.9261006 Calibration (13B) 381.5184591 Calibration (13C) 116.2831605

Calibration (High Pressure) 2027.537 Final Pack 1 53.67800139 Final Pack 2 124.9223588

OBQ 137.8912485

Table 1 Average Cycle Times for Stations

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As each station has an average cycle time, it is possible to get a rough estimate of how long each individual path takes from start to finish. Based on the average cycle times in Table 1, the path times for paths A to J are computed and are given in Table 3. A few things are to be noted:

1. The three normal-range pressure calibration stations have been taken as one station for simplicity, with the cycle time of station 13A taken as the cycle time.

2. Drying in the oven is also included here. Though not an actual station on the assembly line, the transmitters need to be placed in the autoclave oven for roughly 3 hours after the P1 and/or P0 tests for drying before they return to the line to continue assembly.

3. Leak check is carried out on all transmitters. However, depending on whether a flange or a manifold is installed, and other order specifications, it may be carried out before going for the P1 test. Regardless of this, leak check is always carried out after P1/P0 to ensure no leaks have arisen.

4. The P0 test is never done independently of P1. The possible combinations are either P1, or P1 + P0. The total times taken to execute the various scenarios are given in Table 3.

Based on this data, it would seem straightforward to devise a simple algorithm for rudimentary sequencing of order processing on the line. Any such design would deliver better results because of the lack of any type of sequencing algorithm in use currently on the assembly line. However, a study of each station in greater depth is required to properly understand the process and the data from that station, and make alterations to these values as necessary.

B. Sources of Error

The main sources of error on the assembly line are the lack of automation, and human error. Unlike most assembly lines elsewhere, this assembly line is somewhat unique in that there is actually only a small amount of automation on the line. Pieces are passed by hand from station to station by operators, checking in and out of stations is done by operators (a very important point), and the processes at the stations themselves are usually mostly manual tasks, with little actual automated machine work. Only the Leak Check and P1/P0 tests are

completely automated, and even these require initial setup to be done manually by the operator.

The assembly line also currently employs only 4 operators. These operators multitask between the various stations, and each operator is skilled at each station’s processes to varying levels. While all the operators can perform satisfactorily at all the stations, the variations can sometimes be sufficient to affect the extracted data.

One important source of error is the fact that checking in and out of a station is done by the operator, involving the scanning of the barcode on the traveler card. Due to the scarcity of operators on the line, this leads to situations where a workpiece is left at a station without being checked out, even though the process at that station is complete, because the operator is busy at another station. As the operator’s priority is completing station cycles, the process of checking in and out is sometimes overlooked. Unfortunately, as the times in and out of stations are our only source of data, the information we have needs to be examined carefully in order to filter out such sources of error.

C. Station Analysis

Module Pull: The operator picks up the traveler card and checks it into the station, and his monitor displays the specifications of the transmitter, and the module required for the transmitter. He searches through an organized rack of modules for the correct type, checks it visually for faults, and then places the module in a plastic bin, checks out the traveler card and passes the bin and card along to the next station.

The distribution of times taken at different stations, from check-in to check-out, is shown graphically in Fig. 1. As can be seen from the graphs, there is quite a spread of values. The “tail” of the graph is minimal enough to be neglected, and the final set of values, representing times over 200 seconds, can be neglected as erroneous measurements, or indicate that something out of the ordinary occurred to delay the process.

Module Pull: The part of the graph shaded in red comprises 89.6 percent of the observations, which is a reasonable proportion of the results to use for analysis. This portion of the resulting values, when analyzed, gives an average value of 38.49 seconds, which again, would seem to

Module Pull Housing Installation

Label Installation Grounding

Fig 1 Histogram of cycle times of some modules

Scenario Total Time Taken (minutes)

A 30.26 B 53.22 C 225.03 D 248.99 E 226.03 F 248.99 G 228.30 H 251.26 I 228.30 J 251.26

Table 3 Total Time taken for each scenario

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better represent this process for the purposes of designing an algorithm.

Housing Installation: The graph in Fig. 1 shows the spread of cycle time values at this station. The spread of values is more than at earlier stations, and there is a peak at the 10 second bin range, representing 13.25% of the values. However, this peak is assumed to mostly correspond to failures at this station [7]. Another reason for such a high peak is that each operator has been working on the line for at least a year now, and has probably learnt to understand the traveler card visually, and therefore choosing to install the housing first on the transmitter before checking in the piece, either for verification or merely to clear the piece to the next station.

Label Installation: The high peak at the ten second bin range corresponds to skilled operators installing one label to the transmitter with no problems, and finishing the operation in an ideal amount of time. Thus these values are not classified as being part of the tail.

Grounding: Approximately 50% of the pieces lie in the 10-20 second bin range, indicating that this process is usually fast. Hence the smaller values are not classified as being part of the tail.

The tail of the histogram, the percentage of data that form the tail and the average time calculated with the tail excluded is shown in Table 4 for all module times. It is clear that the average time obtained with this approach is more realistic than the values obtained in Table 4 through blind averaging.

D. Bottlenecks

One of the most visible indicators of inefficiency in a system is bottlenecking at a particular point, and assembly lines are no different. It has been apparent to Emerson that bottlenecking is occurring on the pressure transmitter assembly line to a significant extent.

The organization view was that bottlenecking was occurring at the P1 station, due to it taking the maximum cycle time on the line (excluding P0), especially when the 3 hours of drying time required afterwards is considered. However, by considering the different paths that transmitters can take through the line, it was found that the actual bottleneck occurs at the Leak Check station.

As seen in Fig. 2, all the paths finally converge on the Leak Check station. This situation is worsened by the fact that P1 (and P0) are batch processes, meaning that multiple pieces arrive after drying at the Leak Check station for testing. This means that even with ideal sequencing, there will always be a queue at the Leak Check station for pieces coming out of the drying process.

Leak Check itself is not the problem point, however. The process itself takes roughly two minutes only, as shown earlier. After the leak check, the Calibration stations present the main bottleneck in the line.

There are three calibration stations for normal pressure ranges: 13A, 13B and 13C. 13A calibrates for pressures up to 1500 kPa, 13B for up to 810 kPa and 13C for upto 240 kPa There is a single station for high pressure calibration, and this station’s range is 4000 kPa and above. The rough average time for a piece in normal calibration is 15 minutes, while high-pressure calibration takes roughly 30 minutes to complete.

As these stations take a significant amount of time to complete a cycle, queuing inevitably occurs if sequencing is not done carefully. Since this is the main bottleneck on the line, it is also the main target of the sequencing algorithm. To this end, a relation between the pressure range to be calibrated over (which is available from the traveler card) and the time taken for calibration is being investigated, since intuition suggests such a relation.

While P0 is the most time-consuming station on the line, taking roughly 45 minutes to complete a cycle, it has been left out of analysis so far. The reason for this is that, over the past year, MNC in Dubai has received only 11 orders for pieces that require the P0 test. Therefore, this station will initially be left out of analysis and algorithm design until a rough algorithm is implemented, and it was incorporated later on in development.

The main objective function, as stated earlier, is to

optimize the pressure transmitter assembly line and improve efficiency. The specific goals were to reduce, and if possible, eliminate, bottlenecking on the line and as much as possible avoid under-utilization of stations on the line.

After assessing the data from the line, and noting the relatively large amount of human interaction and variability on the line, it was decided that a rigorous, purely mathematical approach would be difficult to implement effectively. A more empirical, rule-based algorithm is required to provide the required results, while having a reasonable tolerance level to

Station Tail Percentage of Data included in calculating average time

Average time spent at station (seconds)

Module Pull <20, >80 89.6 38.49 Tag Engraving <20, >160 80.76 64.09 Housing Installation <20, >200 76.79 92.20 Board Installation <10, >70 82.33 26.90 High Potential Test <30, >130 85.09 70.14 Functionality Testing <20, >90 88.14 41.85 Label Installation <10, >90 88.09 37.47 Grounding <10, >40 87.79 16.65 Flanging <20, >260 82.13 117.38 Leak Check <120, >170 95.58 134.77 P1 Test <800,

>1150 93.67 923.56

Final Pack 1 <10, >90 85.59 31.78 Final Pack 2 <20, >220 88.02 88.30 OBQ <20, >210 86.02 92.73 Table 4 Average time spent at various stations after data filtering

Fig 2 Paths and Bottlenecks

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any variations that might occur. Such an algorithm is more adaptable to any changes to the line itself, such as a decrease in cycle times at stations.

Initial algorithm design was made with a slight simplification of the problem definition. Four unique paths on the line are defined as follows:

• A: No optional tests performed, normal-range calibration

• B: No optional tests performed, high-pressure-range calibration

• C: P1 test carried out, normal-range calibration • D: P1 test carried out, high-pressure-range calibration These paths represent a simplification of the original

paths described in earlier sections. One major omission is the P0 test, and all the paths involving it. Since this test has been required only on very rare occasions, it was decided that it would be left out of algorithm development, and if the need arose, manual operator sequencing would be used when this test is required. Another major difference is that paths that have the Leak Check performed before as well as after the P1 test have been merged with the paths that have it performed only after the test. The variation between the two paths would be equal to the time required for the Leak Check station, which is roughly two minutes. Such a difference is of low significance when compared to the variations encountered in the system, and is therefore overlooked in order to facilitate problem simplification.

The basic approach behind the algorithm is the concept of nested “loops” which is well known in Computer programming is now applied to represent sequences of pieces on the assembly line. The basic assumptions for this algorithm are:

• Paths requiring high-pressure calibration take longer than paths requiring normal-range calibration

• Paths not requiring the P1 test, require significantly less time that those that do

In other words,

• BTIME > ATIME & DTIME > CTIME

• (CTIME, DTIME) >> (ATIME, BTIME)

Therefore, for each piece requiring the P1 test, multiple pieces not requiring the test could be “nested” within the time cycle for the earlier path, and so on. A graphical representation of this is shown in Fig. 3.

For example, the minimum time required between successive batches of transmitters requiring the P1 test is equal to the cycle time for the P1 test itself, which is roughly 15 minutes. Instead of halting the line for this period of time, pieces not requiring the P1 test could be started until the next batch of pieces requiring the P1 test arrives on the line. Similarly, high-pressure calibration requires roughly 30 minutes of cycle time, and therefore the gap between successive pieces requiring it would have to be of this much time as well. Using the same principle, pieces requiring only normal-range calibration could be started until the next piece requiring high-pressure calibration is ready to start.

In this manner, given a set of transmitters to be sequenced, a simple set of rules based on the algorithm described above, a sequence consisting of sets within sets can be easily generated.

Another issue the algorithm will have to account for is the spacing time between successive pieces to ensure that there is no queuing at any station as much as possible. Since the P1 test is a batch process, a batch emerging from the autoclave oven for leak check will inevitably cause a queue, but such a situation is unavoidable, and can only be alleviated with the addition of more leak check stations to the line, which is outside the scope of this thesis. The algorithm is described in a flow chart in Fig. 4.

The four paths mentioned earlier are unique paths on the assembly line, but the MNC’s system clearly does not implement any such classification scheme in their line.

Therefore, the algorithm must integrate with MNC’s existing scheme in order to achieve smooth implementation. MNC in Dubai classifies its wide variety of pressure transmitters according to a model string, which contains relevant information on the assembly of the transmitter, such as the calibration range and the tests required [7].

A relation between the pressure range to be calibrated for and the time taken for calibration was investigated, but no relationship could be found. As a result, in order to continue with algorithm and program development [7], it was decided to assume an average time of 10 minutes (600 seconds) for normal-range calibrations (regardless of actual calibration range) and 2030 seconds for high-pressure-range calibrations. The algorithm is implemented in C# [5][7]. The implementation reads the model string for each piece from an Excel file. The program User Interface (UI) allows the operator to override a sequence by providing the model string. The final program developed has the option of changing individual station times, so after further data analysis, if these station times are refined, they can be altered, and the program will adjust the sequencing algorithm accordingly.

E. Preliminary Results

After completion of the program, two weeks of data from MNC in Dubai is collected for testing the algorithm. This data consisted of lists of all pieces started on the line each day over the course of two weeks, along with model strings and other associated information.

MNC in Dubai, due to a lack of a proper sequencing algorithm, frequently has backlogs from previous days. As a result, the operators start pieces on the line in the morning, but

Fig 3 Visualization of the Nesting of Loops in the Algorithm (not drawn according to time scale)

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leave these incomplete as they finish pieces that were started on the line on earlier days. This, of course, results in a backlog for the next day as well. Therefore, a difficulty arises in comparing MNC’s current performance to the results given by the sequencing program.

One test measure is to sequence all the pieces that are started on the line each day, and see if it is possible to complete them on the same day they were started. For a sample range covering 9 days, this is achieved without exception. Table 1 is a summary of the test range of nine days shows the breakup in terms of numbers of pieces completed each day. As can be seen, the program is able to approach roughly 100 pieces a day, depending on the particular product mix on that day. Using the overall list of transmitters started over the test period, and allowing the program to select the best possible mix, it is able to sequence 100 pieces in a 9-hour work day. This is a marked improvement over the roughly 40 pieces a day that MNC reports as its current average output.

Another testing set, consisting of data from an Excel file generated by the helper program onsite at MNC, consisted of a list of 187 pieces, which represents all unstarted pieces in MNC’s system as of a certain date. With this data as input, the sequencer program was able to sequence 111 pieces within a 9-hour workday, achieving target output thresholds. The sequence data is given in Table 5.

IV. CONCLUSIONS

This paper proposed a sequencing algorithm for the MNC’s pressure transmitter assembly line in Dubai to address bottlenecks and under-utilization in a manufacturing line that

can be characterized as stochastic, dynamic, multi-product, configure as a network of queues. The number of products completed at the end of the day is to be maximized while minimizing the queue waiting time at the stations where the bottlenecks occur. The bottlenecks occur, not due to large processing or cycle time of the station but rather due to the station being fed by batch processing stations. With this complex set up, the algorithm proposed is simulation based. The empirical algorithm that addressed distinctive manufacturing line characteristics improved the number of pressure transmitter pieces that could be assembled and tested in a day from the existing 40 to over 100.

The algorithm presented in the course of this paper is applicable only for the MNC’s line and lines similar to it. There are a number of further optimizations possible for the existing manufacturing line or for different and more complex scenarios.

ACKNOWLEDGMENT

Authors acknowledge the enthusiastic support and cooperation of the management of MNC in Dubai. This work was done as a part of Thesis requirements by the first author, who was a student at Birla Institute of Technology and Science, Pilani, India's Dubai Campus in DIAC, Dubai, UAE.

REFERENCES [1] Boysen, N., Fliedner, M., & Scholl, A. (2006). Assembly line balancing:

Which model to use when? Friedrich-Schiller-Universität Jena.

[2] Chutima, P., & Pinkoompee, P. (2009). Multi-objective sequencing problems of mixed-model assembly systems using memetic algorithms. ScienceAsia Vol. 35, pp. 295-305.

[3] Hakansson, J., Skoog, E., & Eriksson, K. (n.d.). A review of assembly line balancing and sequencing including line layouts. hv.divaportal.org/smash/get/diva2:313377/FULLTEXT01 (downloaded Dec 2012)

[4] Meyr, H. (2004). Supply chain planning in the German automotive industry. OR Spectrum Vol. 26, pp. 447-470.

[5] Schildt, H. (2009). C#: The Complete Reference. Tata-McGraw-Hill.

[6] Tambe, P. Y. (2006). Balancing Mixed-Model Assembly Line to reduce work overload in a multi-level production system.

[7] Ashok Vydyanathan, “Optimization of the pressure transmitter manufacturing line At Rosemount DMMC Emerson Process Management,” Thesis, June 2012, BITS Pilani, Dubai Campus.

Fig 4 Flow Chart of Algorithm

Day Number

Number Completed

Time Taken

(Hours) 1 52 9 2 24 6 3 33 2.5 4 37 4.5 5 19 5.5 6 88 6.5 7 27 6 8 38 6.5 9 28 6.5

Table 5 Overall Sequencing Results

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