optimisation of shock absorber parameters using failure mode and effect analysis and taguchi method

7/30/2019 Optimisation of Shock Absorber Parameters Using Failure Mode and Effect Analysis and Taguchi Method

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International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 6340(Print), ISSN 0976 6359(Online) Volume 3, Issue 2, May-August (2012), IAEME

328

OPTIMISATION OF SHOCK ABSORBER PARAMETERS USING

FAILURE MODE AND EFFECT ANALYSIS AND TAGUCHI

METHOD

A.Mariajayaprakash

Assistant Professor and Head, Department of Mechanical Engineering,

Rajiv Gandhi College of Engineering and Technology,Puducherry, India;

e-mail : [email protected]

Dr.T. SenthilVelan

Professor and Head , Department of Mechanical Engineering,

Pondicherry Engineering College, Puducherry, India;e-mail: [email protected]

K.P.Vivekananthan

Deputy General Manager (manufacturing),TENNECO, Puducherry, Indiae-mail: [email protected]

ABSTRACT

The various process parameters affecting the quality characteristics of the shock absorber

during the process were identified by using the Ishikawa diagram and FMEA. The

identified process parameters are: Welding process parameters (squeeze, heat control,wheel speed and air pressure), tamper sealing process parameters(load, hydraulic pressure

, air pressure and fixture height),Washing process parameters(total alkalinity,

temperature, PH value of rinsing water and timing),Painting processparameters(flowability, coating thickness, pointage and temperature).Out of above

mentioned four process parameters welding and tamper sealing process parameters were

optimized by using Taguchis method in the earlier work. In this paper, the remainingprocess parameters namely washing and painting process parameters are optimized by

using Taguchis method.

Key words: Taguchis method, Ishikawa diagram, ANOVA, FMEA

INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING

AND TECHNOLOGY (IJMET)

ISSN 0976 6340 (Print)ISSN 0976 6359 (Online)

Volume 3, Issue 2, May-August (2012), pp. 328-345

IAEME: www.iaeme.com/ijmet.html

Journal Impact Factor (2011):1.2083 (Calculated by GISI)www.jifactor.com

IJMET

I A E M E


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LIST OF SYMBOLS

r Number of tests in a trial

yi Response value of observation in the ith test

DOE Design of experiments Degrees of freedom

A Degrees of freedom for factor A

kA Number of levels for factor A

required Total degrees of freedom required

LN Total degrees of freedom of the available orthogonal array

N Number of trials

ANOVA Analysis of varianceM Overall mean percentage defects

VFactor Variance of factor

SS Sum of squareV Expected amount of variation

P Percent contribution

Mean

Level of riske Degrees of freedom for the error

Ve Error variance

F(, 1, e) F ratio required at the level of riskeff Effective number of replications

n Total number of experiments

CI Confidence interval

T Average values of defects at different levels

1.0 INTRODUCTION

Shock absorber is one of the major component used in automobiles. It is used to absorb

vibrations when the vehicle is moving. Otherwise that vibrations affect the vehicle andthe rider. The defects which are occurred during the process affect the quality of the

shock absorber. In order to minimize the defects and improve the quality, the following

tools are employed. First, Cause and effect diagram or Ishikawa diagram is used foridentifying the parameters that may affect the quality of the shock absorber during the

process. Then, Failure Mode Effect Analysis tool is applied to find out the most

significant process parameters that may affect the quality of the shock absorber. Finally,

Taguchi method is used to optimize the process parameters. Taguchi method is anefficient and powerful tool that can reduce the experimental trails necessary to determine

the optimal conditions. It has been widely used in many different fields (Yung-Tien Liu

et al., 2010).

Tsai et al. (2008) used Taguchi design experiments to identify the optimal abrasive jet

polishing parameters when applied to the polishing of electrical discharged machinedSKD 61 mold steel specimens. This paper shows that ANOVA provides an indication of


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the significance of the individual control factors. Mahapatra and Vedansh Chaturvedi

(2009) proved that Taguchis experimental design is a simple and systematic way ofanalyzing a complex process with less experimental design.

In this paper, test parameters are optimized for minimum wear by using Taguchis

method. Ganapathy et al. (2009) optimized the operating parameters in Jatropha bio

diesel engine by using Taguchi method.In this paper it is proved that signal to noise ratio is used to optimize the various input

parameters of the model. This paper shows that the Taguchi approach based

thermodynamic model has improved the performance parameters slightly. Pishbin et al.(2010) investigated the effects of processing parameters, including suspension

concentration, pH and electric field by using Taguchis DOE approach in electrophoretic

deposition of bio glass suspension. Mojtaba momeni et al. (2010) obtained the optimalcondition for DOS measurement based on Double- loop EPR technique in H2 SO4

containing KSCN solution using the Taguchi method. In this paper, it is concluded that

higher the S/N ratio, greater is the effect on the DOS measurement. Te-Sheng Li et al.(2009) explained how Taguchi method is utilized for optimizing the parameters in

thermal flow techniques for sub-35 nm contact-hole fabrication. In this article, theoptimal thermal flow parameter settings are determined by signal to noise ratio and

ANOVA analysis. Balamurugan Gopalsamy et al. (2009) applied Taguchi method to findoptimum process parameters for end milling while hard machining of hardened steel.

Results obtained from Taguchi method closely match with ANOVA and it is found that

cutting speed is most influencing parameters corresponding to quality characteristics.

Khandoker abul Hossain et al. (2010) illustrated that the cause and effect diagram

provides the solution to reduce the emission of co2. Andrejkovic et al. (2011) explainedthat Ishikawa diagram quickly identify the causes of quality problems. Tlale et al. (2008)

applied the usage of cause and effect diagram in cost drivers for manufacturing process.

Hu-chen liu et al. (2011) reported about the usage of FMEA tool and how it is appliedto identify the failure modes. Arabian-Hoseynabadi et al. (2010) explained that theFMEA tool is applied in wind turbine systems. In this paper, the reliability of the wind

turbine system is improved by applying FMEA tool. Boldrin et al. (2009) identified and

reduced the risks of failure in ITER NB injector using FMEA tool. .

2.0 CAUSE AND EFFECT DIAGRAM

Cause and effect diagram is a tool for identifying the root causes of quality problems. It is

also called as Ishikawa diagram or Fishbone diagram ( Ilie and Ciocoiu , 2010).An

Ishikawa diagram is constructed as shown in Fig.1. By using this diagram the various

process parameters that affect the quality of the shock absorber are identified ( Mohansen and shan, 2005).The identified process parameters are listed below:

1.Washing parameters

2.Welding parameters3.Painting parameters

4.Tamper sealing parameters

5.Assembly parameters6.Lubrication parameter


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7.Inspection and Checking Parameters

8.Dimensioning parameters9.Polishing parameter

10.Testing parameters

DEFECTSIN

SHOCK

ABSORBR

DEFECTSINSHOCK

ABSORBR

painting

DIMENSIONING

INSPECTION/CHECKING POLISHING

Paint notadheringRustcorrosion

Pointagetemperature

FlowabilityCoatingthickness

Cylinder notball sized

Ballsizing

Cylinder notball sized

1.concentricity2.cylinderbendchecking

1.Breakage2.burrs

1.Concentricity morethan specification2.wrong jaws3.worn out jaws

.

Setupnot correct

Poorsurfacefinish

Bad stonecondition

WASHING

Improperfrequency ofchange

Presence ofoil/grease

1.wrong parameters2.improperpreparationof solvent

Fig.1Cause and effect diagram

3. FAILURE MODE AND EFFECT ANALYSIS

After constructing Ishikawa diagram, it is important to perform Failure Mode Effect

Analysis. FMEA is a powerful tool which is used to define, identify and eliminate known

potential failures, problems, errors and so on (hu-chen liu et al, 2011).Today, FMEAtechnique has been applied in many places, such as automobiles, aerospace, military,

electricity and mechanical industries. FMEA is conventionally carried out by a team of

engineers. By using their knowledge and past data, Risk Priority Number(RPN) value isassigned for each failure component.(Zaifang Zhang and Xuening Chu, 2011). RPN is the

product of the occurrence (o), severity(s), and detection (d) of a failure. The three risk

factors are evaluated using 10 points scale. Failure modes with higher RPN values areassumed to be more important and are given higher priorities than those with lower RPN

values (Ying ming wang et al., 2009). In this paper, washing and painting process

parameters are having higher RPN values. Hence these two process parameters areconsidered as most significant process parameters.


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4. TAGUCHIS METHOD

Taguchi method is an efficient tool for acquiring optimal process parameters. In thismethod, number of experiments are reduced. In this method two important tools are used.

i) orthogonal array and ii) Signal to noise ratio ( Cevdet Gologlu and Nazim Sakarya,

2008).

First, experiment parameter factors and their levels are selected. In this study, fourpainting process parameters are used as control factors and each parameter is designed to

have three levels ( kilickap, 2010).The selected process parameters, ranges and their

levels are shown in Table 1.

Table 1 Process parameters with their ranges and values at three levels

Parameter

designationProcess parameters Range Level 1 Level 2 Level 3

A Flowability ( sec) 15-25 15 20 25

B Coatingthickness(microns)

20-50 20 40 50

C pointage 6-12 6 8 12

D Temperature ( C) 20-50 2 35 50

4.1. Selection of Orthogonal array

An orthogonal array for process design is applied on the knowledge of control factors and levels.The number of experiments can be reduced by using orthogonal array.( yu - tang yen et

al.,2010).The degrees of freedom for the orthogonal array should be greater than or at least equal

to those for the process parameters. In this study, L9 orthogonal array having 8 degrees of

freedom is selected ( Anawa and Olabi, 2008).This orthogonal array has four columns and nine

experiments runs and it is shown in Table 2.

Table 2 L9 orthogonal

array Table 3 Experimental L9 array

Run A B C D

1 1 1 1 1

2 1 2 2 2

3 1 3 3 3

4 2 1 2 3

5 2 2 3 1

6 2 3 1 27 3 1 3 2

8 3 2 1 3

9 3 3 2 1

Trial

no.A B C D

Flow

ability

( sec)

Coating

thickness

(microns)

pointageTempe-

rature (0C)

1 15 20 6 20

2 15 40 8 35

3 15 50 12 50

4 20 20 8 505 20 40 12 20

6 20 50 6 35

7 25 20 12 35

8 25 40 6 50

9 25 50 8 20


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4.2. Conducting the Experiment

After selecting the orthogonal array, the factors at different levels are assigned for eachtrial. The assigned experimental array is shown in Table 3.The experiments are conducted

using single randomization technique. Each experiment is repeated three times for the

same set off parameters.(Ross,1988 ; Hazura Mohamed et.al.,2008).In each trial, the

defects occurred are recorded and it is shown in Table 4. In this work, the defects thatoccur during the welding, washing, painting, and tamper sealing process are considered.

The percentage of the defects for each repetition was calculated by using the given

formula, and then the average of the defects were determined for each trial condition andit was shown in Table 5.

No. of defects occurring due to Painting processPercentage of defects =

Total no. of defects occurring in the process

(welding, washing, painting and tamper sealing)

Table 4 Defects and number of defects occurred during the experiments

Defects No. of defects

Trial

1Trial 2 Trial 3

paint not

adheringrusting

not

drying

not

cleaning4 4 4

paint not

adhering1 2 1

paint not

adhering

manual

coating

powder

deposit

over

heating4 4 4

powder deposit not drying 2 2 2

rusting not cleaning 2 2 2

paint not comingpowder

deposit2 2 2

paint not coming rustingover

heating2 4 4

paint not coming not drying 3 2 2

21 24 23


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Table 5 Painting defects values and signal to noise (S/N) ratios against trail numbers

4.3. S/N Ratio

Taguchi technique utilises the signal to noise ratio(S/N) approach to measure the qualitycharacteristic deviating from the desired value. S/N ratio is used as an objective function

for optimizing parameters. (Gunawan Setia Prihandana et.al., 2009).Control factors are

easily adjustable and it is set by the manufacturer. These factors are most important indetermining the quality characteristics. Noise factors are difficult, impossible, or

expensive to control (weather, temperature, humidity etc.). The S/N ratio is the ratio of

mean (signal) to the standard deviation (noise). There are several S/N ratios availabledepending on the type of characteristics

( Mahapatra et.al., 2008 ; Sanjit Moshat et al., 2010).Usually three types of S/N ratio

analysis are applicable:

1) smaller is better

n

= -10 log 1/n yi2

i=1

2) nominal is the bestn

= -10 log 1/n 2/2

i=1

3) higher is better.

n

= -10 log 1/n 1/yi2

i=1

= S/N ratio, yi = value of quality characteristic at ith setting, = Mean,n= total number of trial runs at ith setting, = standard deviation.

Trial

no.% of defects in experiment

1 2 3 average S/N ratio

1 4.00 5.00 5.19 4.73 -13.55

2 2.67 2.50 1.30 1.71 -7.00

3 5.33 5.00 5.19 5.18 -14.28

4 1.33 3.75 2.60 2.56 -8.77

5 2.67 2.50 2.60 2.59 -8.26

6 2.67 2.50 2.60 2.59 -8.26

7 2.67 2.50 2.60 2.59 -8.26

8 2.67 3.75 5.19 4.31 -12.06

9 4.00 2.50 2.60 3.03 -9.85


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In this study, in order to minimize the defects, smaller is better S/N ratio is chosen.Smaller is better S/N ratios are computed and the values are recorded in table 5. The

main objective of shock absorber process is to minimize the defects by determining

optimal level of each factor. Since log is monotone decreasing function, it implies that

we should maximize the S/N ratio.(Fang-Jung Shiou and Chih-Cheng Hsu, 2008).Theaverage values of the painting defects and S/N ratios for each parameter at different

levels are calculated and are recorded in Table 6.

Table 6. Average values of painting defects and S/N ratios at different levels

Factors Level 1 Level 2 Level 3

Painting

defects

S/N

ratios

painting

defects

S/N

ratios

painting

defects

S/N

ratios

A 4.02 -11.61 2.58 -8.43 3.16 -10.06

B 3.29 -10.19 2.87 -9.10 3.60 -10.80C 3.73 -11.29 2.58 -8.54 3.45 -10.27

D 3.45 -10.55 2.45 -7.84 3.87 -11.70

The values given in table 6 are plotted in fig 2 and fig 3 .Fig 2 and fig 3 show the average

values of casting defects and S/N ratios for each parameter at different levels. From fig2and fig.3 it is clear that the casting defects are minimum at the parameter levels A2, B2,

C2 and D2 and the S/N ratios are maximum at the same levels of the parameters (A2, B2,

C2 and D2.). Since a higher S/N ratio means a better quality characteristic (minimumdefects), the optimal combination of control factor levels is therefore determined as

A2B2C2D2.

A1 A2 A3 B1 B2 B3 C1 C2 C3 D1 D2 D3

Fig 2 Average values of painting process defects for each parameters

at different levels


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A B C D

1 2 3 1 2 3 1 2 3 1 2 3Fig.3Average values of S/N ratios for each parameter at different levels

4.4. Analysis of Variance (ANOVA)

The purpose of ANOVA is to investigate which process parameters significantly affect

the quality characteristic. The total variation may be decomposed into manycomponents.In this paper, the total variation present in the process is decomposed to the

following components:

1. variation due to factor A2. variation due to factor B

3. variation due to factor C4. variation due to factor D

5. variation due to error

The total variation is calculated using the values given in table 4.

Total variation SST = SSA +SSB +SSC +SSD +SSe

Variation due to error SSe = SST (SSA +SSB +SSC +SSD)

Total degrees of freedom T =(A+B+C+D +e)

e =T (A+B+C+D)

= 26 (2+2+2+2)= 18


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Table. 7 ANOVA for painting defects and S/N ratios

sourcesum of

squares

Degrees of

freedom variance F ratio

Painting

defects

S/N

ratio

Painting

defects

S/N

ratio

Painting

defects

S/N

ratio

Painting

defects

S/N

ratio

A 9.44 14.97 2 2 4.72 7.49 8.90 24.87

B 2.40 4.20 2 2 1.20 2.10 2.26 6.98

C 6.44 11.39 2 2 3.22 5.70 6.07 18.92

D 9.63 23.41 2 2 4.81 11.70 9.07 38.89

error 9.56 0.60 18 2 0.53 0.30 1.00 1.00

total 37.47 54.57 26 8 1.44 6.82

The results of analysis of variance (ANOVA) are shown in table 7. In table 7, it is clearthat the parameters A, C and D significantly affect both mean and variation in the

painting defects.

5. RESULTS AND DISCUSSIONS

5.1. Percent contribution

Percent contribution is the function of the sum of squares of each significant item.Percent contribution to the total sum of square can be used to evaluate the importance of

a change in the process parameter on these quality characteristics

(Lakshminarayan and Balasubramanian, 2008; Balamurugan Gopalsamy et al.,2009). It is

calculated using the formulae given below:

Percent contribution (P) = (SSA/ SST) *100SSA = SSA (e) (A)

VA = VA+ V error

where VA is the expected amount of variation due solely to factor A given below:

VA = SSA /A

VA = SSA /A


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Table 8 ANOVA for painting defects, including percent contribution

source

Sum

ofsquares

Degrees

offreedom

Variance F ratioExpected

SS'

Percent

contribution(P)

A 9.44 2 4.72 8.90 6.94 18.52

B 2.40 2 1.20Pooled(2.6)

C 6.44 2 3.22 6.07 6.44 17.19

D 9.63 2 4.81 9.07 9.63 25.69

error

(pooled)9.56 18 0.53 1.00 14.46 38.60

total 37.47 26 1.44 37.47 100.00

Table 9 S/N ratios for painting defects, including percent contribution

sourcesum of

squares

Degrees

offreedom


SS'

Percent

contribution(P)

A 14.97 2 7.49 24.87 12.72 23.31

B 4.20

C 11.39 2 5.70 18.92 9.68 17.73

D 23.41 2 11.70 38.89 19.89 36.44

error(pooled) 0.6 2 0.30 1.00 12.29 22.52

Total 54.57 8 6.82 54.57 100.00

The percentage of contribution of flowability, , pointage, and temperature is shown in

table 8.

5.2. Estimating the meanFrom table 8, it is clear that factor B has the least effect on the quality characteristic. In

order to prevent over estimation, factor B is not considered and the estimation of meanfor painting defects is calculated by the following equation: (Te sheng Li et al., 2009)

=T+ (A2-T) + (C2-T) + (D2-T)

where T is the average values of painting defects at different levels.The mean for a selected trial condition for parameters at (A2,B2,C2,D2) is 1.11.

5.2.1. Confidence Interval around the estimated mean

An important step in Taguchis optimization technique is to conduct confirmation

experiments for validating the predicted values. Thus a 95% confidence interval (CI) for


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the predicted mean of optimum quality characteristic on a confirmation test is estimated

using the following two equations : (Horng Wen Wu and Hui Wen Gu, 2010).CI3 = [ F( , 1, e) Ve (1/eff +1/r)]1/2

eff = N/ (1+ total DOF associated in the estimate of mean )

where is the level of risk, Ve is the error variance, e is the degrees of freedom for the error,

eff is the effective number of replications and r is number of test trials.

Using the values in Table 5, the CI was calculated as follows

eff = N/ 1+ (total DOF associated in the estimate of mean )

= 27/ (1+2)= 9

= 1- confidence limits ( 95%)= 0.05

F ratio = (1, 0.05, 18)

= 4.41 (tabulated)Confidence Interval CI3 = 1.02

The 95% confidence interval of the predicted optimum of the shock absorber defect is:0.09


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Table 10 Optimum parameters under economic considerations

Parameter

designationParameter

Optimum

levels

Optimum

value

A Flowability (sec) 2 20B

Coating thickness(microns)

2 40

C pointage 2 8

D Temperature (oC) 2 35

In the same manner calculations are carried out and optimum values are found for

washing process.

Table 11 Process parameters with their ranges and values at three levels

Parameterdesignation

Process parameters Range Level 1 Level 2 Level 3

A Total Alkalinity (g/lit.) 40-80 40 70 80

B Temperature(oC) 45-75 45 60 75

C Rinsing water (PH) 5.5 -8.5 5.5 6.5 8.5

D Timing (min) 0-25 5 15 25

Table 12 Defects and number of defects occurred during the experiments

Defects defects number

oil presence over heatedmore time

taken3 2 3

oil presence over heated 1 2 1

oil presencepowder

deposited2 2 2

oil presence more time taken 1 2 1

oil presence 1 1 1

oil presence 1 1 1

over heated 1 1 1

over heated more time taken 1 1 2

over heated oil presenceMore time

taken1 3 2

12 15 14


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Table 13 washing defects values and signal to noise (S/N) ratios

against trail numbers

Trial

no.% of defects in experiment

1 2 3 average S/N ratio

1 4.00 2.50 3.90 3.47 -10.96

2 1.33 2.50 1.30 1.71 -5.10

3 2.67 2.50 2.60 2.59 -8.27

4 1.33 2.50 1.30 1.71 -5.10

5 1.33 1.25 1.30 1.29 -2.24

6 1.33 1.25 1.30 1.29 -2.24

7 1.33 1.25 1.30 1.29 -2.24

8 1.33 1.25 2.60 1.73 -5.279 1.33 3.75 2.60 2.56 -8.77

Table 14 Average values of washing process defects and S/N ratios

at different levels

Factors Level 1 Level 2 Level 3

washing

defects

S/N

ratios

washing

defectsS/N ratios

washing

defects

S/N

ratios

A 2.59 -8.11 1.43 -3.19 1.86 -5.43

B 2.16 -6.10 1.58 -4.20 2.15 -6.43

C 2.16 -6.16 2.00 -6.32 1.73 -4.25

D 2.44 -7.32 1.43 -3.19 2.00 -6.21

Table 15 ANOVA for washing process defects and S/N ratios

sourcesum of

squares

Degrees of

freedomVariance F ratio

washing

defects

S/N

ratio

washing

defects

S/N

ratio

washing

defects

S/N

ratio

washing

defects

S/N

ratio

A 6.14 36.25 2 2 3.07 18.13 7.51 112.84

B 1.98 8.54 2 2 0.99 4.27 2.42 26.58

C 0.87 7.85 2 2 0.44 3.93 1.07 24.45

D 4.59 27.30 2 2 2.30 13.65 5.62 84.97

error 7.36 0.32 18 2 0.41 0.16 1.00 1.00

total 20.95 108.23 26 8 13.53


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Table 16 ANOVA for washing process defects, including percent contribution

sourcesum of

squares

Degrees

offreedom


SS'

Percent

contribution(P)

A 6.14 2 3.07 7.51 5.32 25.40

B 1.98 2 0.99 pooled(2.42)

C 0.87 2 0.44 pooled(1.06)

D 4.59 2 2.30 5.62 3.78 18.02

error

(pooled)7.36 18 0.41 1.00 11.85 56.58

total 20.95 26 20.95 100.00

Table 17 S/N ratios for washing process defects, including percent contribution

sourcesum of

squares

Degreesof

freedom


SS'

Percent

contribution(P)

A 36.25 2.00 18.13 112.84 35.93 33.20

B 8.54 2.00 4.27 26.58 8.22 7.59

C 7.85

D 27.30 2.00 13.65 84.97 26.98 24.93

error

(pooled) 0.32 2.00 0.16 1.00 37.10 34.28

Total 108.23 8.00 13.53 108.23 100.00

The mean for a selected trial condition for parameters at (A2, B2, C2, D3) is 0.90.

The 95% confidence interval of the predicted optimum of the shock absorber defect is:

0.13< 0.90 < 1.67.

Table 18 Optimum parameters under economic considerations

Parameter

designation ParameterOptimum

levels

Optimum

value

A Total Alkalinity (g/lit.) 2 70

B Temperature(oC) 2 60

C Rinsing water(PH) 2 7.5

D Timing(min) 3 10


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6. Conclusion

In this study, Taguchi method has been employed for optimizing the process parametersof painting and washing during shock absorber manufacturing and the following

conclusions are drawn:

* It is proved that, the quality of shock absorber is improved by Taguchis method at the

lowest possible cost.* Ishikawa diagram or cause and effect diagram is very effective to sort out all the

possible causes affecting the shock absorber quality.

* The most significant parameters affecting the quality of the shock absorber areidentified by using the FMEA tool.

* The parameter pointage is significantly affect the painting process and parameter timing

is significantlyaffect the washing process.

* The optimum levels of flowability, coating thickness, pointage, and temperature in

painting process and total alkalinity, temperature, rinsing water, and timing in washingprocess are estimated.

* The predicted range of optimum painting defects is 0.09


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