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Performance Improvement of a Zinc Plating Process Using Taguchi's Methodology: A Case Study
Performance Improvement of a Zinc Plating Process Using Taguchi's Methodology: A Case Study by P.B.S. Reddy and K. Nishina, Nagoya Institute of Technology, Nagoya, Japan, and A. Subash Babu, Indian Institute of Technology, Mumbai, India
G
lobalization of the world economy and borderless competition has forced organizations to rethink the way they do business. In response many organizations have started implementing various management concepts such as Total Quality Management (TQM), Business Process Reengineering, Change Management, etc. to improve their competitive edge and enhance their ability to provide customer satisfaction. In the process these organizations have realized that there is a need to reexamine/reengineer their business processes to achieve improvements in productivity and performance. I This article presents a case study illustrating the process improvement methodology. The study reported in this paper pertains to a company manufacturing typewriters. For the last 5 years this company has been facing stiff competition locally and globally. To achieve business excellence, this company adopted TQM as their business strategy. These intensive programs typically range from ISO 9000 implementation to process improvement using industrial experimentation. The main goal of industrial experimentation is to find optimum conditions for the process that achieve target values for the responses with minimum process variability. In this article one such success story is reported.
THE CASE STUDY
This study pertains to zinc plating in a company manufacturing typewriters. Many parts of the typewriter are plated for good finish and corrosion resistance. The plating process considered for the study is Zinthrobrite 936. Details of the process are explained below. 24
Details of the Process Zinthrobrite 936 is a slightly acidic, ammonia-free chloride zinc plating process, which produces bright, finegrained, ductile zinc deposits in both rack and barrel plating systems. The process will plate directly on carbonitrided, carbonized, or case-hardened steel and malleable iron. Problems Encountered In the final assembly of a typewriter many of the plated parts were not mating properly with their counterparts in the assembly operations. Moreover, the rejection rate of zinc-plated parts was quite high. To understand the problem and the associated causes a detailed preliminary study was carried out on the zinc plating process. These details are given below. Preliminary StUdy In the company, tank plating is used for large-size components. After discussions with various process personnel, variables that are likely to affect the quality of the deposit were decided. These included current, voltage, time, pH, surface tension, concentration of the bath, viscosity, etc. To learn about the pattern of variation of these variables during the process a pilot study was conducted. The following observations were made.
the plating thickness showed that there was a significant variation between pieces and also within the piece. To study the variation in the plating thickness, between pieces and within the piece, thickness has been measured in five positions of the product. Figures 2 and 3 show the variation in mean and standard deviation of coating thickness from position to position. Similarly, Figures 4 and 5 show the variations in mean thickness and standard deviation in the piece-to-piece case. The preliminary study revealed that there was a large variation in coating thickness. It was also observed that plating parameters were varying within a wide range. So, the process is not in control. To bring the process into control and to achieve uniform coating thickness, this process was optimized
-
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Figure 1. Variation in concentration of the bath.
1. Voltage varied from 5.1 to 5.5 V, current 170 to 195 A, plating time 13 to 19 minutes, and pH 5.1 to 5.5. 2. Figure 1 shows the variation in the concentration of the bath, which is in the range of 125 to 295 gIL. This study pertains to a period of 16 months. In this graph the actual concentration level, ideal level, and the level to which it was brought back are indicated. 3. The data collected on variation in
Figure 2. Variation in mean thickness (position to position).
© Copyright Elsevier Science Inc.
METAL FINISHING. OCTOBER 1998
1
I
using Taguchi's robust design methodology. Details of robust design methodology and the optimization procedure adopted for the present case are explained below.
DB'1'ERMD'rn THE OBJEC1TVES OF THE STIJDY IDENTIFY THE OPTIMUM COMBINADON OF PROCESS PARAMETERS TO ACHIEVE UNIFORM PLATING TIDCKNESS
TAGUCHI'S ROBUST DESIGN METHODOLOGY
Edward Deming is credited for his contribution to Japanese quality engineering through introduction of statistical process control. Taguchi's emphasis has been to make a further shift from production to design. Philosophically, robust design is an economical way to improve quality. This method consists of three steps: system design, parameter design, and tolerance design. The goal of robust design is to design a system so that its performance is insensitive to sources of variations (noise factors). This is achieved by choosing the settings of control factors to make the system robust to uncontrollable noise factors. The dramatic success of this methodology lies in combining statistical design of experimental methods with a deep understanding of engineering problems. The past few years have witnessed a revolution with innumerable success stories about this methodology in many areas of engineering. While Taguchi is universally praised for his novel idea many serious-minded exponents of statistical designs like Box have cautioned against such proliferation for various statistical reasons.? The discussion paper edited by Nair provides a good overview.' Some of the merits and criticisms of this methodology are explained below. 1. A novelty of robust design is in
selecting a standard array that is closest to the needs of the problem in hand. If necessary one may modify the standard orthogonal array to
CONDUCT EXPERIMENTS AT nm SPECIFICATION LEVELS OF FACTORS USING ~7 ARRY
Figure 4. Variation in mean thickness (piece to piece).
fit into the case in hand by dummy level, compound factor, or column merging techniques. Thus, the experimenter can study a large number of control factors in a small number of experiments and thereby save experimental cost. By using these ready-made procedures the experimenter would not know the rationale behind the manipulation of parameter settings executed during experimental study nor would he or she be able to alter the designs to meet any practical operational constraints faced in the industry." 2. Taguchi advocates that use of confirmation experiments to verify the predicted optimum settings do in fact lead to improved performance. For a number of reasons confirmation experiments can fail to verify the prediction in improved performance. Taguchi does not offer any specific guidelines on what further analyses should be done to isolate these problems. 3. The objective of parameter design is to identify the parameter levels at which the effect of noise variables is minimum. This assumes the interaction between the design parameters and noise variables. It is unreasonable to assume that the design variables interact with only noise variables and not among themselves.
", d
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I
I
I II II
I
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14, 2 3 4 6-.~'-.~'~'O~11~12-13~"-'~.~'.~17~"~"-'O
Figure 3. Variation in standard deviation of thickness (position to position). 26
Figure 5. Variation in standard deviation of thickness (piece to piece).
Figure 6. Flowchart for optimization of coating process.
This criticism inspired many researchers to develop various alternative methods.v? Moreover, it helped to develop various tailor-made solutions to problems using Taguchi's method. The case presented here is one such successful shop floor story. ROBUST DESIGN METHODOLOGY FOR ZINC PLATING PROCESS
The preliminary study on zinc plating revealed that there were large variations in the thickness of plating. It was also observed that variation in plating parameters was high. So, to bring the process into control, Taguchi's methodology was applied in a systematic way as shown in the flow chart in Figure 6.
Identification of the Problem Plated parts were not mating with their counterparts in the assembly operation because of nonuniform zinc plating. Setting the Objective The objective was to determine the combination of process parameter valMETAL FINISHING • OCTOBER 1998
Table I. Control Factors and Levels Levels Factors
Units
Concentration (A) Current (B) Time (C)
giL Amps Minutes
2
3
Normal
High
Low
170 12
185 15
200 18
ues so as to achieve required plating thickness with minimum variance. Quality Characteristic to be Measured The quality characteristic identified to be measured was plating thickness. Variations in coating thickness were observed between pieces and within a piece in the preliminary study. It was suspected that this might have been due to the inherent variations within the bath itself. To capture the variation in concentration and temperature along the length of the bath, test pieces were hung at four segments of the tank. From each segment a sample of five pieces was picked up to measure the plating thickness. Furthermore, to capture the effect of noise variation across the piece, the thickness was measured at five positions on each of the pieces. Selection of Factors and Their Levels Selection of factors and their levels is an important step in any industrial experimentation. Discussion with various process personnel and the results of preliminary study helped to identify the factors that affect the response. Using process knowledge and criticality analysis, three factors were identified as relevant for experimentation. These were concentration (A), current (B), and time (C). The pilot study helped in selecting the levels of the factors. Since the interest was in nonlinearity, three levels were chosen for each factor as with two levels curvature effects would be missed. Besides, interactions would definitely exist when a good number of variables cause change in the output. All two-factor interactions were considered in selecting the experimental layout. The factors and their levels are shown in Table I. Experimental Layout An efficient way to study the effect of several factors simultaneously is to plan the matrix experiments using or28
thogonal arrays. An orthogonal array for a particular problem can be constructed from the knowledge of the number of factors and their levels and the desire to study specific interactions. In this study there were three factors at three levels. Moreover, the two-factor interactions were considered for the study. In this case L9 array was fully saturated so it was not possible to study all the factors and their interactions. So the standard L 27 array was selected for this experiment. The main factors A, B, and C and their twofactor interactions were allocated to respective columns in the array with the help of linear graphs and interaction tables. This is shown in Table II.
plating thickness. Hence, Nominal-thebest case (NTB) was identified as suitable for calculating the SIN ratio for plating thickness.t The experimental results of the L 27 layout are shown in Table II. In the optimization process once the variation in the response (plating thickness) is reduced the next step is moving the response average towards the target with the help of adjustment parameters. These details are furnished in the subsequent sections.
Data Analysis For each experiment thickness was measured at five positions of each piece to capture the noise variation within the piece. As discussed earlier the entire bath was divided into four segments. At each segment five pieces were picked up to measure the plating thickness. The objective was to minimize the variance and bring the process mean on target for the response
1. Determine the factors having significant effect on the SIN ratio. This is done through the analysis of variance (ANOYA) of the SIN ratio. These factors are called control factors, implying that they control the process variability. For each control factor the level with the highest SIN ratio is to be chosen as the optimum level. Thus the overall SIN ratio is maximized.
Optimization In general, for problems involving a single response, Taguchi's optimization methodology consists of the following two steps:9,10
Table II. L27 Experimental Layout and Summary of Responses Response Column Numbers and Factors Assignment Experiment No. 1 2 3 4 5 6 7 8 9 10 11
12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
Thickness
A J
B 2
C 5
Mean (Microns)
SiN Ratio (dB)
1 1 I 1 1
1 1 1 2 2 2 3 3 3 1
1 2 3
I
2 3
14.65 18.86 21.20 16.84 20.37 21.99 18.52 21.41 24.23 15.80 19.50 21.78 17.86 20.60 22.41 19.37 21.50 24.62 14.37 18.12 20.45 17.26 19.35 21.28 17.65 22.38 23.23
5.87 6.82 8.59 6.50 8.42 9.87 7.77 8.83 10.05 6.47 8.08 8.82 7.04 8.55 9.06 8.24 8.59 10.38 5.71 7.38 8.43 6.60 7.57 8.67 7.32 8.10 9.09
I
1 I
1 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3
1 2 2 2 3 3 3 1 1 1 2 2 2 3 3 3
I
2 3 1 2 3 I
I
2 3 1 2 3 1 2 3 1 2 3 1 2 3
METAL FINISHING • OCTOBER 1998
Table III. ANOVA-SIN Data (Thickness) Source A B AxB C AxC BxC Error e1 e2 (e) Total
Pool
DF
Sum of Squares
Mean Square
% Contribution
N N Y N Y N Y N N
2 2 4 2 4 4 8 0 0 16 26
2.29 7.11 0.20 23.46 0.22 1.74 1.51 0.00 0.00 1.93 34.56
1.14 3.56 0.05 11.73 0.05 0.43 0.19
5.91 19.89
N
2. Select a factor that has the smallest effect on the SIN ratio among all factors that have a significant effect on the mean. Such a factor is called signal/adjustment factor. Then set the level of the signal factor so that the mean response is on target. In the present study this two-step approach was used. Following are the details pertinent to the selection of control factors, adjustment factors, and identification of optimum levels.
ANOVA for SIN Ratios The SIN ratios obtained for the response plating thickness have been presented in Table II. These data were analyzed using analysis of variance.l!
0.12 1.33
67.19 3.63
9.10
The results are presented in Table III. To identify the control factors, which have a significant effect on SIN ratio, Taguchi introduced a new measure called percentage contribution. The larger the percentage contribution the more can be expected to be achieved by changing the level of that factor. Computation of the percentage contribution is illustrated in an article by M.S. Phadke et al. lO The percentage contribution of plating time (C) to the total sum of squares is 67%. Current (B) has moderate effect and its contribution is 20%. The interaction BC was found to have a moderate effect on the process. To obtain a better understanding of the effect of each factor, the relationship between the levels of each factor and their corresponding impact
Table IV. Factor Effects on SIN Data (Thickness) Factor Main Effects: A B C Interaction of AB: A Interaction of AC: A Interaction of BC: B
30
SIN Ratio (dB)
Level
1 2 3 1 2 3 1 2 3
8.08466 8.36376 7.65631 7.46822 7.92528 8.71123 6.83850 8.15357 9.11266
1 2 3
1 7.42944 7.79767 7.17755
2 7.93762 8.21916 7.61908 C
3 8.88692 9.07447 8.17230
1 2 3
1 6.71647 7.25221 6.54681
2 8.36152 8.41242 7.68676 C
3 9.17598 9.42666 8.73535
1 2 3
1 6.02173 6.71738 7.77638
2 7.76268 8.18460 8.51343
3 8.62023 8.87388 9.84387
B
on variation are shown in Table IV. For each significant factor the level corresponding to the highest SIN ratio has been chosen as the optimum level. Thus the overall SIN ratio is maximized. The optimum combination of these factor levels minimizes the process variability. The levels of the remaining factors can be set at any level within the experimental range. Preferably it was chosen to leave them at the initial level.
ANOVA for Mean of the Response For each run of experimental design the mean of the responses was computed. These data were analyzed using ANOVA to identify mean adjustment factors. The analysis of means (ANOM) identifies the statistically significant factors that affect the mean values of responses. The adjustment factors have no effect on SIN ratio and are capable of moving the mean to target. So, the key characteristics of the mean adjustment parameter are small or have no effect on variability and a large effect on the mean. The summary results of ANOVA for mean response plating thickness are shown in Table V. Factors C and B were found significant. The percentage contribution of plating time (C) and current (B) to total sum of squares is 70.18 and 23.35 respectively. Concentration has no effect at all. The mean plating thickness for each factor level is shown in Table VI. Selection of Optimum Conditions To find the optimum conditions further analysis was carried out. In Table VII the factors were arranged in descending order of their percentage contribution to total variation with reference to ANOVA and ANOM to facilitate identifying the control factors. The following inferences were derived: Control factors: A and B Adjustment factor: C The task of determining the best setting for each factor could become complicated if the conflict occurs. From the results in Table VII and Figures 7 and 8 the following important observations can be made about optimum settings. METAL FINISHING. OCTOBER 1998
Table V. ANOVA-Mean Response (Thickness) Source A B AxB
C AxC BxC Error
e1 e2 (e) Total
Pool
OF
Sum of Squares
Mean Square
% Contribution
N N Y N Y Y Y N N
2 2
4.91 44.27 0.38 131.84 0.59 2.64 2.37 0.00 0.00 5.98 187.01
2.45 22.14 0.09 65.92 0.15 0.66 0.29
2.30 23.35
4
2
4 4
8
o o
20 26
N
70.18
Main Effects: A B C Interaction of AB: A Interaction of AC: A Interaction of Be: B
Level
0.29 7.19
4.16
Mean Thickness
1 2 3 1 2 3 1 2 3
19.70 20.38 19.35 18.30 19.71 21.43 16.92 20.23 22.29
1 2 3
1 18.24 19.03 7.64
2 19.50 20.29 19.33 C
3 21.39 21.83 20.09
1 2 3
1 16.67 17.67 16.43
2 20.21 20.53 19.95 C
3 22.24 22.94 21.69
1 2 3
1 14.94 17.32 18.51
2 18.83 20.18 21.76
3 21.24 21.70 24.02
B
Table VII. Summary of Analysis Analysis A. SIN Ratio Optimum levels % contribution B. Mean Response Optimum levels % contribution
Factors A 2 5.91
B 3 19.89
C 3 67.19
2.30
3 23.35
1 70.18
Concentration (A) has moderate effect on SIN ratio of plating thickness. By increasing the concentration level from normal to high there is a change in uniformity of the thickness (since SIN ratio has improved from 8.08 to 8.36 dB), while decreasing the concentration there is a slight negative trend. It indicates that the impact of change in concentration level on quality characMETAL FINISHING • OCTOBER 1998
AB
~'
Figure 7. Interaction graph of Cwith A and B.
Table VI. Factor Effects on Mean Responses (Thickness) Factor
/----_.,'
BC 33 3.63
AC
teristics is not appreciably very high; however, high concentration slightly improves the uniformity of the coating. Current (B) has a very significant effect on uniformity of the coating (SIN ratio) and mean of the response. It is also acting both as control and adjustment factor. By increasing current the uniformity of thickness is improved to an extent of 0.45 dB at level
2 and 1.25 dB at level 3; hence, it was decided to set at level 3. Time (C) has a very significant effect on the uniformity of the coating since the SIN ratios are 1.32 dB and 2.28 dB at level 2 and 3, respectively. Moreover, it is a strong adjustment factor. As the plating time increases there is a reduction in deposition rate; hence, it is a productivity factor. For every company both quality and productivity are important for survival in the competitive market. The Taguchi method also advocates that the optimum combination obtained should be capable of achieving high quality and productivity; hence, time was considered as an adjustment factor and kept at level 1 to achieve high productivity, i.e., high deposition rate. From Figure 7, when C is levell, the optimum levels for control factors A and B are 2 and 3, respectively. So the optimum settings for this process are All B 3 , and C!. From Table II the SIN value for this optimum setup is 8.24 dB with coating thickness of 19.37 microns. Thus SIN value is improved from initial setup value (experiment 1) of 5.87 to 8.24 dB. This helped greatly in reducing the variation of coating thickness. Confirmation experiments were conducted to ascertain whether optimum conditions obtained through robust design were really capable of achieving improvement in performance or not. These details are furnished below.
1·/ I
/
/" .>
/"
~"~"
.-/
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)"
! j
(
Figure 8. Factor effects on mean response (thickness). 31
Confirmation Experiments With the above conditions (A z' B 3 , CI)' when the verification experiments were conducted, the SIN value obtained was 9.18 dB with coating thickness of 19.49 microns. Here, we observed that there was a little improvement in the SIN ratio from its previous experimental value (8.24 dB). The reasons for this improvement are explained below. To conduct confirmation experiments the entire tank was cleaned and fresh electrolytes were prepared. Metal pieces were cleaned to avoid ingress of duct and grease particles. These preparations helped register the improved SIN value. The results strengthened the confidence in the entire exercise. Thus, it was possible to greatly reduce the variation using the optimum conditions obtained through robust design methodology. The reduction in variation of plating thickness signifies a more uniform plating. CONCLUSIONS Optimization of the zinc plating process using Taguchi's methodology helped in variation reduction of plating thickness and achieving target values as shown above. The optimum setting obtained was robust against possible variations, such as between pieces and within the piece, segment to segment in the tank, etc. More importantly the reject rate of zinc-plated parts in the final assembly reduced significantly from 22% to 4%. The results of various analyses helped to understand the various intricate dynamics of the process. This is very useful for process personnel for future improvements. In the future the company desires to
have 10 microns of plating thickness. To achieve this we have to identify a new factor through research, which has interaction with plating time (C), such as B in the present case. If we keep C at a low level it helps to achieve desired thickness and also improves deposition rate (productivity). This changed situation helps the company in many ways related to cost, delivery, quality, and productivity, which are the dimensions of an organization's excellence. In brief the results of the study had given considerable confidence to the company in their ongoing TQM journey. With this success many teams started using various statistical diagnostic tools to improve the quality of products and processes. TQM is becoming a way of life for all company employees.
Acknowledgement The authors would like to thank Mr. Podar, quality assurance, and are very grateful to the plating department staff for their cooperation in conducting experiments. Biographies P.B.S. Reddy is presently a visiting researcher at the Department of Systems Engineering, Nagoya Institute of Technology. Prior to this he was associated with Corporate Quality, Crompton Greaves Ltd., Bombay, India. He received his masters degree in production engineering and Ph.D. in quality management from Indian Institute of Technology, Bombay, India. Ken Nishina is an associate professor at the Department of Systems Engineering, Nagoya Institute of Technology. He received a B.Eng. and
M.Eng. from Nagoya Institute of Technology in 1975 and 1977 and a Ph.D. from Tokyo Institute of Technology in 1990. He is a member of ASQC and JSQC. A. Subach Babu is a professor of Industrial Engineering and Operations Research at Indian Institute of Technology, Bombay, India.
References
1. Lockamy III, A. and W.I. Smith. International Journal of Production Economics, 50(2,3):141-154; 1997 2. Box, G.E.P. et aI., "An Explanation and Critique of Taguchi 's Contribution to Quality Engineering," Quality and Reliability Engineering International, 2(4):114-123; 1988 3. Nair, V.N. (ed), Technometrics, 34(2): 127-161; 1992 4. Goh, T.N., "Some Strategies for Experimentation Under Operational Constraints," Quality and Reliability Engineering International, 13(5):279-283; 1997 5. Dell Castillo, E. and D.C. Montgomery, Journal of Quality Technology. 25(3):199-204; 1993 6. Hamada, M. and J.A. Nelder, Journal of Quality Technology. 29(3):292-304; 1997 7. Parkinson, D.H., "Robust Design by Variability Optimization," Quality and Reliability Engineering International, 13(4):97-102; 1997 8. Phadke, M.S., Quality Engineering Using Robust Design, Prentice Hall, Englewood Cliffs, N.J., pp. 67-128; 1989 9. Phadke, M.S., AT&T Technical Journal. 65(2):51; 1986 10. Phadke, M.S. et al., The Bell Systems Technical Journal, 62(5):1273-1309; 1983 11. Montgomery, D.C., Design and Analysis of Experiments, John Wiley and Sons, New York; 1995 MF
Ollf CONIaOl
Circle006 on reader information card
34
METAL FINISHING. OCTOBER 1998
G
lobalization of the world economy and borderless competition has forced organizations to rethink the way they do business. In response many organizations have started implementing various management concepts such as Total Quality Management (TQM), Business Process Reengineering, Change Management, etc. to improve their competitive edge and enhance their ability to provide customer satisfaction. In the process these organizations have realized that there is a need to reexamine/reengineer their business processes to achieve improvements in productivity and performance. I This article presents a case study illustrating the process improvement methodology. The study reported in this paper pertains to a company manufacturing typewriters. For the last 5 years this company has been facing stiff competition locally and globally. To achieve business excellence, this company adopted TQM as their business strategy. These intensive programs typically range from ISO 9000 implementation to process improvement using industrial experimentation. The main goal of industrial experimentation is to find optimum conditions for the process that achieve target values for the responses with minimum process variability. In this article one such success story is reported.
THE CASE STUDY
This study pertains to zinc plating in a company manufacturing typewriters. Many parts of the typewriter are plated for good finish and corrosion resistance. The plating process considered for the study is Zinthrobrite 936. Details of the process are explained below. 24
Details of the Process Zinthrobrite 936 is a slightly acidic, ammonia-free chloride zinc plating process, which produces bright, finegrained, ductile zinc deposits in both rack and barrel plating systems. The process will plate directly on carbonitrided, carbonized, or case-hardened steel and malleable iron. Problems Encountered In the final assembly of a typewriter many of the plated parts were not mating properly with their counterparts in the assembly operations. Moreover, the rejection rate of zinc-plated parts was quite high. To understand the problem and the associated causes a detailed preliminary study was carried out on the zinc plating process. These details are given below. Preliminary StUdy In the company, tank plating is used for large-size components. After discussions with various process personnel, variables that are likely to affect the quality of the deposit were decided. These included current, voltage, time, pH, surface tension, concentration of the bath, viscosity, etc. To learn about the pattern of variation of these variables during the process a pilot study was conducted. The following observations were made.
the plating thickness showed that there was a significant variation between pieces and also within the piece. To study the variation in the plating thickness, between pieces and within the piece, thickness has been measured in five positions of the product. Figures 2 and 3 show the variation in mean and standard deviation of coating thickness from position to position. Similarly, Figures 4 and 5 show the variations in mean thickness and standard deviation in the piece-to-piece case. The preliminary study revealed that there was a large variation in coating thickness. It was also observed that plating parameters were varying within a wide range. So, the process is not in control. To bring the process into control and to achieve uniform coating thickness, this process was optimized
-
ObM'Yld
---4---
Conlrollitd
- - ldetlll
Figure 1. Variation in concentration of the bath.
1. Voltage varied from 5.1 to 5.5 V, current 170 to 195 A, plating time 13 to 19 minutes, and pH 5.1 to 5.5. 2. Figure 1 shows the variation in the concentration of the bath, which is in the range of 125 to 295 gIL. This study pertains to a period of 16 months. In this graph the actual concentration level, ideal level, and the level to which it was brought back are indicated. 3. The data collected on variation in
Figure 2. Variation in mean thickness (position to position).
© Copyright Elsevier Science Inc.
METAL FINISHING. OCTOBER 1998
1
I
using Taguchi's robust design methodology. Details of robust design methodology and the optimization procedure adopted for the present case are explained below.
DB'1'ERMD'rn THE OBJEC1TVES OF THE STIJDY IDENTIFY THE OPTIMUM COMBINADON OF PROCESS PARAMETERS TO ACHIEVE UNIFORM PLATING TIDCKNESS
TAGUCHI'S ROBUST DESIGN METHODOLOGY
Edward Deming is credited for his contribution to Japanese quality engineering through introduction of statistical process control. Taguchi's emphasis has been to make a further shift from production to design. Philosophically, robust design is an economical way to improve quality. This method consists of three steps: system design, parameter design, and tolerance design. The goal of robust design is to design a system so that its performance is insensitive to sources of variations (noise factors). This is achieved by choosing the settings of control factors to make the system robust to uncontrollable noise factors. The dramatic success of this methodology lies in combining statistical design of experimental methods with a deep understanding of engineering problems. The past few years have witnessed a revolution with innumerable success stories about this methodology in many areas of engineering. While Taguchi is universally praised for his novel idea many serious-minded exponents of statistical designs like Box have cautioned against such proliferation for various statistical reasons.? The discussion paper edited by Nair provides a good overview.' Some of the merits and criticisms of this methodology are explained below. 1. A novelty of robust design is in
selecting a standard array that is closest to the needs of the problem in hand. If necessary one may modify the standard orthogonal array to
CONDUCT EXPERIMENTS AT nm SPECIFICATION LEVELS OF FACTORS USING ~7 ARRY
Figure 4. Variation in mean thickness (piece to piece).
fit into the case in hand by dummy level, compound factor, or column merging techniques. Thus, the experimenter can study a large number of control factors in a small number of experiments and thereby save experimental cost. By using these ready-made procedures the experimenter would not know the rationale behind the manipulation of parameter settings executed during experimental study nor would he or she be able to alter the designs to meet any practical operational constraints faced in the industry." 2. Taguchi advocates that use of confirmation experiments to verify the predicted optimum settings do in fact lead to improved performance. For a number of reasons confirmation experiments can fail to verify the prediction in improved performance. Taguchi does not offer any specific guidelines on what further analyses should be done to isolate these problems. 3. The objective of parameter design is to identify the parameter levels at which the effect of noise variables is minimum. This assumes the interaction between the design parameters and noise variables. It is unreasonable to assume that the design variables interact with only noise variables and not among themselves.
", d
10~----------
1\
21
r----\
1\
22•
I\
c
I ::t':)~1 \ r-;<.I \\\J,t\\\ I
J "
17
te H5
'
\) \
I
I
I II II
I
Ii l
14, 2 3 4 6-.~'-.~'~'O~11~12-13~"-'~.~'.~17~"~"-'O
Figure 3. Variation in standard deviation of thickness (position to position). 26
Figure 5. Variation in standard deviation of thickness (piece to piece).
Figure 6. Flowchart for optimization of coating process.
This criticism inspired many researchers to develop various alternative methods.v? Moreover, it helped to develop various tailor-made solutions to problems using Taguchi's method. The case presented here is one such successful shop floor story. ROBUST DESIGN METHODOLOGY FOR ZINC PLATING PROCESS
The preliminary study on zinc plating revealed that there were large variations in the thickness of plating. It was also observed that variation in plating parameters was high. So, to bring the process into control, Taguchi's methodology was applied in a systematic way as shown in the flow chart in Figure 6.
Identification of the Problem Plated parts were not mating with their counterparts in the assembly operation because of nonuniform zinc plating. Setting the Objective The objective was to determine the combination of process parameter valMETAL FINISHING • OCTOBER 1998
Table I. Control Factors and Levels Levels Factors
Units
Concentration (A) Current (B) Time (C)
giL Amps Minutes
2
3
Normal
High
Low
170 12
185 15
200 18
ues so as to achieve required plating thickness with minimum variance. Quality Characteristic to be Measured The quality characteristic identified to be measured was plating thickness. Variations in coating thickness were observed between pieces and within a piece in the preliminary study. It was suspected that this might have been due to the inherent variations within the bath itself. To capture the variation in concentration and temperature along the length of the bath, test pieces were hung at four segments of the tank. From each segment a sample of five pieces was picked up to measure the plating thickness. Furthermore, to capture the effect of noise variation across the piece, the thickness was measured at five positions on each of the pieces. Selection of Factors and Their Levels Selection of factors and their levels is an important step in any industrial experimentation. Discussion with various process personnel and the results of preliminary study helped to identify the factors that affect the response. Using process knowledge and criticality analysis, three factors were identified as relevant for experimentation. These were concentration (A), current (B), and time (C). The pilot study helped in selecting the levels of the factors. Since the interest was in nonlinearity, three levels were chosen for each factor as with two levels curvature effects would be missed. Besides, interactions would definitely exist when a good number of variables cause change in the output. All two-factor interactions were considered in selecting the experimental layout. The factors and their levels are shown in Table I. Experimental Layout An efficient way to study the effect of several factors simultaneously is to plan the matrix experiments using or28
thogonal arrays. An orthogonal array for a particular problem can be constructed from the knowledge of the number of factors and their levels and the desire to study specific interactions. In this study there were three factors at three levels. Moreover, the two-factor interactions were considered for the study. In this case L9 array was fully saturated so it was not possible to study all the factors and their interactions. So the standard L 27 array was selected for this experiment. The main factors A, B, and C and their twofactor interactions were allocated to respective columns in the array with the help of linear graphs and interaction tables. This is shown in Table II.
plating thickness. Hence, Nominal-thebest case (NTB) was identified as suitable for calculating the SIN ratio for plating thickness.t The experimental results of the L 27 layout are shown in Table II. In the optimization process once the variation in the response (plating thickness) is reduced the next step is moving the response average towards the target with the help of adjustment parameters. These details are furnished in the subsequent sections.
Data Analysis For each experiment thickness was measured at five positions of each piece to capture the noise variation within the piece. As discussed earlier the entire bath was divided into four segments. At each segment five pieces were picked up to measure the plating thickness. The objective was to minimize the variance and bring the process mean on target for the response
1. Determine the factors having significant effect on the SIN ratio. This is done through the analysis of variance (ANOYA) of the SIN ratio. These factors are called control factors, implying that they control the process variability. For each control factor the level with the highest SIN ratio is to be chosen as the optimum level. Thus the overall SIN ratio is maximized.
Optimization In general, for problems involving a single response, Taguchi's optimization methodology consists of the following two steps:9,10
Table II. L27 Experimental Layout and Summary of Responses Response Column Numbers and Factors Assignment Experiment No. 1 2 3 4 5 6 7 8 9 10 11
12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
Thickness
A J
B 2
C 5
Mean (Microns)
SiN Ratio (dB)
1 1 I 1 1
1 1 1 2 2 2 3 3 3 1
1 2 3
I
2 3
14.65 18.86 21.20 16.84 20.37 21.99 18.52 21.41 24.23 15.80 19.50 21.78 17.86 20.60 22.41 19.37 21.50 24.62 14.37 18.12 20.45 17.26 19.35 21.28 17.65 22.38 23.23
5.87 6.82 8.59 6.50 8.42 9.87 7.77 8.83 10.05 6.47 8.08 8.82 7.04 8.55 9.06 8.24 8.59 10.38 5.71 7.38 8.43 6.60 7.57 8.67 7.32 8.10 9.09
I
1 I
1 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3
1 2 2 2 3 3 3 1 1 1 2 2 2 3 3 3
I
2 3 1 2 3 I
I
2 3 1 2 3 1 2 3 1 2 3 1 2 3
METAL FINISHING • OCTOBER 1998
Table III. ANOVA-SIN Data (Thickness) Source A B AxB C AxC BxC Error e1 e2 (e) Total
Pool
DF
Sum of Squares
Mean Square
% Contribution
N N Y N Y N Y N N
2 2 4 2 4 4 8 0 0 16 26
2.29 7.11 0.20 23.46 0.22 1.74 1.51 0.00 0.00 1.93 34.56
1.14 3.56 0.05 11.73 0.05 0.43 0.19
5.91 19.89
N
2. Select a factor that has the smallest effect on the SIN ratio among all factors that have a significant effect on the mean. Such a factor is called signal/adjustment factor. Then set the level of the signal factor so that the mean response is on target. In the present study this two-step approach was used. Following are the details pertinent to the selection of control factors, adjustment factors, and identification of optimum levels.
ANOVA for SIN Ratios The SIN ratios obtained for the response plating thickness have been presented in Table II. These data were analyzed using analysis of variance.l!
0.12 1.33
67.19 3.63
9.10
The results are presented in Table III. To identify the control factors, which have a significant effect on SIN ratio, Taguchi introduced a new measure called percentage contribution. The larger the percentage contribution the more can be expected to be achieved by changing the level of that factor. Computation of the percentage contribution is illustrated in an article by M.S. Phadke et al. lO The percentage contribution of plating time (C) to the total sum of squares is 67%. Current (B) has moderate effect and its contribution is 20%. The interaction BC was found to have a moderate effect on the process. To obtain a better understanding of the effect of each factor, the relationship between the levels of each factor and their corresponding impact
Table IV. Factor Effects on SIN Data (Thickness) Factor Main Effects: A B C Interaction of AB: A Interaction of AC: A Interaction of BC: B
30
SIN Ratio (dB)
Level
1 2 3 1 2 3 1 2 3
8.08466 8.36376 7.65631 7.46822 7.92528 8.71123 6.83850 8.15357 9.11266
1 2 3
1 7.42944 7.79767 7.17755
2 7.93762 8.21916 7.61908 C
3 8.88692 9.07447 8.17230
1 2 3
1 6.71647 7.25221 6.54681
2 8.36152 8.41242 7.68676 C
3 9.17598 9.42666 8.73535
1 2 3
1 6.02173 6.71738 7.77638
2 7.76268 8.18460 8.51343
3 8.62023 8.87388 9.84387
B
on variation are shown in Table IV. For each significant factor the level corresponding to the highest SIN ratio has been chosen as the optimum level. Thus the overall SIN ratio is maximized. The optimum combination of these factor levels minimizes the process variability. The levels of the remaining factors can be set at any level within the experimental range. Preferably it was chosen to leave them at the initial level.
ANOVA for Mean of the Response For each run of experimental design the mean of the responses was computed. These data were analyzed using ANOVA to identify mean adjustment factors. The analysis of means (ANOM) identifies the statistically significant factors that affect the mean values of responses. The adjustment factors have no effect on SIN ratio and are capable of moving the mean to target. So, the key characteristics of the mean adjustment parameter are small or have no effect on variability and a large effect on the mean. The summary results of ANOVA for mean response plating thickness are shown in Table V. Factors C and B were found significant. The percentage contribution of plating time (C) and current (B) to total sum of squares is 70.18 and 23.35 respectively. Concentration has no effect at all. The mean plating thickness for each factor level is shown in Table VI. Selection of Optimum Conditions To find the optimum conditions further analysis was carried out. In Table VII the factors were arranged in descending order of their percentage contribution to total variation with reference to ANOVA and ANOM to facilitate identifying the control factors. The following inferences were derived: Control factors: A and B Adjustment factor: C The task of determining the best setting for each factor could become complicated if the conflict occurs. From the results in Table VII and Figures 7 and 8 the following important observations can be made about optimum settings. METAL FINISHING. OCTOBER 1998
Table V. ANOVA-Mean Response (Thickness) Source A B AxB
C AxC BxC Error
e1 e2 (e) Total
Pool
OF
Sum of Squares
Mean Square
% Contribution
N N Y N Y Y Y N N
2 2
4.91 44.27 0.38 131.84 0.59 2.64 2.37 0.00 0.00 5.98 187.01
2.45 22.14 0.09 65.92 0.15 0.66 0.29
2.30 23.35
4
2
4 4
8
o o
20 26
N
70.18
Main Effects: A B C Interaction of AB: A Interaction of AC: A Interaction of Be: B
Level
0.29 7.19
4.16
Mean Thickness
1 2 3 1 2 3 1 2 3
19.70 20.38 19.35 18.30 19.71 21.43 16.92 20.23 22.29
1 2 3
1 18.24 19.03 7.64
2 19.50 20.29 19.33 C
3 21.39 21.83 20.09
1 2 3
1 16.67 17.67 16.43
2 20.21 20.53 19.95 C
3 22.24 22.94 21.69
1 2 3
1 14.94 17.32 18.51
2 18.83 20.18 21.76
3 21.24 21.70 24.02
B
Table VII. Summary of Analysis Analysis A. SIN Ratio Optimum levels % contribution B. Mean Response Optimum levels % contribution
Factors A 2 5.91
B 3 19.89
C 3 67.19
2.30
3 23.35
1 70.18
Concentration (A) has moderate effect on SIN ratio of plating thickness. By increasing the concentration level from normal to high there is a change in uniformity of the thickness (since SIN ratio has improved from 8.08 to 8.36 dB), while decreasing the concentration there is a slight negative trend. It indicates that the impact of change in concentration level on quality characMETAL FINISHING • OCTOBER 1998
AB
~'
Figure 7. Interaction graph of Cwith A and B.
Table VI. Factor Effects on Mean Responses (Thickness) Factor
/----_.,'
BC 33 3.63
AC
teristics is not appreciably very high; however, high concentration slightly improves the uniformity of the coating. Current (B) has a very significant effect on uniformity of the coating (SIN ratio) and mean of the response. It is also acting both as control and adjustment factor. By increasing current the uniformity of thickness is improved to an extent of 0.45 dB at level
2 and 1.25 dB at level 3; hence, it was decided to set at level 3. Time (C) has a very significant effect on the uniformity of the coating since the SIN ratios are 1.32 dB and 2.28 dB at level 2 and 3, respectively. Moreover, it is a strong adjustment factor. As the plating time increases there is a reduction in deposition rate; hence, it is a productivity factor. For every company both quality and productivity are important for survival in the competitive market. The Taguchi method also advocates that the optimum combination obtained should be capable of achieving high quality and productivity; hence, time was considered as an adjustment factor and kept at level 1 to achieve high productivity, i.e., high deposition rate. From Figure 7, when C is levell, the optimum levels for control factors A and B are 2 and 3, respectively. So the optimum settings for this process are All B 3 , and C!. From Table II the SIN value for this optimum setup is 8.24 dB with coating thickness of 19.37 microns. Thus SIN value is improved from initial setup value (experiment 1) of 5.87 to 8.24 dB. This helped greatly in reducing the variation of coating thickness. Confirmation experiments were conducted to ascertain whether optimum conditions obtained through robust design were really capable of achieving improvement in performance or not. These details are furnished below.
1·/ I
/
/" .>
/"
~"~"
.-/
"I
)"
! j
(
Figure 8. Factor effects on mean response (thickness). 31
Confirmation Experiments With the above conditions (A z' B 3 , CI)' when the verification experiments were conducted, the SIN value obtained was 9.18 dB with coating thickness of 19.49 microns. Here, we observed that there was a little improvement in the SIN ratio from its previous experimental value (8.24 dB). The reasons for this improvement are explained below. To conduct confirmation experiments the entire tank was cleaned and fresh electrolytes were prepared. Metal pieces were cleaned to avoid ingress of duct and grease particles. These preparations helped register the improved SIN value. The results strengthened the confidence in the entire exercise. Thus, it was possible to greatly reduce the variation using the optimum conditions obtained through robust design methodology. The reduction in variation of plating thickness signifies a more uniform plating. CONCLUSIONS Optimization of the zinc plating process using Taguchi's methodology helped in variation reduction of plating thickness and achieving target values as shown above. The optimum setting obtained was robust against possible variations, such as between pieces and within the piece, segment to segment in the tank, etc. More importantly the reject rate of zinc-plated parts in the final assembly reduced significantly from 22% to 4%. The results of various analyses helped to understand the various intricate dynamics of the process. This is very useful for process personnel for future improvements. In the future the company desires to
have 10 microns of plating thickness. To achieve this we have to identify a new factor through research, which has interaction with plating time (C), such as B in the present case. If we keep C at a low level it helps to achieve desired thickness and also improves deposition rate (productivity). This changed situation helps the company in many ways related to cost, delivery, quality, and productivity, which are the dimensions of an organization's excellence. In brief the results of the study had given considerable confidence to the company in their ongoing TQM journey. With this success many teams started using various statistical diagnostic tools to improve the quality of products and processes. TQM is becoming a way of life for all company employees.
Acknowledgement The authors would like to thank Mr. Podar, quality assurance, and are very grateful to the plating department staff for their cooperation in conducting experiments. Biographies P.B.S. Reddy is presently a visiting researcher at the Department of Systems Engineering, Nagoya Institute of Technology. Prior to this he was associated with Corporate Quality, Crompton Greaves Ltd., Bombay, India. He received his masters degree in production engineering and Ph.D. in quality management from Indian Institute of Technology, Bombay, India. Ken Nishina is an associate professor at the Department of Systems Engineering, Nagoya Institute of Technology. He received a B.Eng. and
M.Eng. from Nagoya Institute of Technology in 1975 and 1977 and a Ph.D. from Tokyo Institute of Technology in 1990. He is a member of ASQC and JSQC. A. Subach Babu is a professor of Industrial Engineering and Operations Research at Indian Institute of Technology, Bombay, India.
References
1. Lockamy III, A. and W.I. Smith. International Journal of Production Economics, 50(2,3):141-154; 1997 2. Box, G.E.P. et aI., "An Explanation and Critique of Taguchi 's Contribution to Quality Engineering," Quality and Reliability Engineering International, 2(4):114-123; 1988 3. Nair, V.N. (ed), Technometrics, 34(2): 127-161; 1992 4. Goh, T.N., "Some Strategies for Experimentation Under Operational Constraints," Quality and Reliability Engineering International, 13(5):279-283; 1997 5. Dell Castillo, E. and D.C. Montgomery, Journal of Quality Technology. 25(3):199-204; 1993 6. Hamada, M. and J.A. Nelder, Journal of Quality Technology. 29(3):292-304; 1997 7. Parkinson, D.H., "Robust Design by Variability Optimization," Quality and Reliability Engineering International, 13(4):97-102; 1997 8. Phadke, M.S., Quality Engineering Using Robust Design, Prentice Hall, Englewood Cliffs, N.J., pp. 67-128; 1989 9. Phadke, M.S., AT&T Technical Journal. 65(2):51; 1986 10. Phadke, M.S. et al., The Bell Systems Technical Journal, 62(5):1273-1309; 1983 11. Montgomery, D.C., Design and Analysis of Experiments, John Wiley and Sons, New York; 1995 MF
Ollf CONIaOl
Circle006 on reader information card
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METAL FINISHING. OCTOBER 1998