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					 International Journal of of Industrial Engineering and Development (IJIERD), ISSN 0976
International Journal Industrial Engineering Research Research                            –
 6979(Print), ISSN 0976 – 6987(Online) Volume 2,                     IJIERD
and Development (IJIERD), ISSN 0976 Issue 1, May - October (2011), © IAEME
                                                 – 6979(Print)
ISSN 0976 – 6987(Online) Volume 2
Issue 1, May – October (2011), pp. 01-14
                                                                   ©IAEME
© IAEME, http://www.iaeme.com/ijierd.html



     IMPLEMENTATION OF TAGUCHI METHODOLOGY FOR
    DEFECT REDUCTION IN MANUFACTURING INDUSTRY “A
                     CASE STUDY”

                                     Rajender Kumar
                                   Student (M.Tech.)
                            Dept. of Mechanical Engineering
                              PEC University of technology
                         (Formerly Punjab Engineering College)
                          Sector-12, Chandigarh (India)-160012
                           E- mail: rajender629@yahoo.com

                                    Dr. D. R. Prajapati
            Assistant Professor in the department of Mechanical Engineering,
                             PEC University of Technology,
                               Chandigarh-160012 (India).
                            E-mail: praja_3000@yahoo.com

                                     Sukhraj Singh
             Research Scholar in the department of Mechanical Engineering,
                           PEC University of Technology,
                              Chandigarh-160012 (India).
                           E-mail: sukhraj7in@yahoo.co.in




ABSTRACT

Quality improvement technique, especially Taguchi methodology is used for designing
the experiments to investigate the effects of different parameters on the mean and
variance of process performance characteristics. In this paper, the Taguchi methodology
is used for the rejection control of flexible hose assembly in an automobile industry. An
attempt is made to correlate the effect of process parameters on the final product.
Experiments are carried out to validate the results, obtained by the implementation of
Taguchi methodology in automotive process industry and the most important parameters,
affecting the product life is found out. The optimal levels of the process parameters in
order to yield the optimum quality characteristics of the products are obtained. An
orthogonal array, the signal to noise (S/N) ratio, and analysis of variance (ANOVA) are
used to analyze the effect of selected process parameters and their levels. The results


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International Journal of Industrial Engineering Research and Development (IJIERD), ISSN 0976 –
6979(Print), ISSN 0976 – 6987(Online) Volume 2, Issue 1, May - October (2011), © IAEME

indicate that the selected process parameters significantly affect the defects of flexible
hose assembly. The optimized process parameters i.e. lower crimping depth of 0.35 mm
and cap inner diameter (oversize) of 16 mm is obtained and which lead to minimize the
crimping leakage defects.
Keywords: Taguchi methodology, Cause and effect diagram, ANOVA, S/N Ratio,
Manufacturing and Assembling Process.

1.0 INTRODUCTION

Over the past few years, Taguchi Methodology has been espoused by many world-class
companies and has also a lot of successful cases. The main benefit of a Taguchi
methodology is the elimination of subjectivity in decision-making by creating a system
where everyone in the organization collects, analyzes and displays data in a consistent
manner. For manufacturing companies, the direct benefit of Taguchi methodology results
from the reduction in the number of defects due to improved manufacturing processes.
For these companies, high quality level is a measure of the process defect rate and thus
can be used to measure the quality of the manufacturing process. In the Taguchi method,
the results of the experiments are analyzed to achieve the objectives:
 (i)    To establish the best or optimal condition for the product or process,
 (ii)   To establish the contribution of individual factors, and
 (iii) To estimate the response under optimal conditions.

Taguchi’s contributions to quality engineering include loss function associated with a
product or process, robust design and simplified statistical experiments using orthogonal
arrays. Taguchi methodology states that even the best available manufacturing
technology by itself is not an assurance that the final product will actually function in the
hands of its users as desired and so strongly advocated for the engineered products with
robust performance. Taguchi described entire concept in two basic ideas, namely, quality
should be measured by the deviation from a special target value rather than by
conformance to preset tolerance limits and quality cannot be ensured through the
inspection and rework, but must be built-in, through the appropriate design of the process
and product. The first concept underlines the basic difference between Taguchi methods
and statistical process control (SPC) methodology. The SPC emphasizes the attainment of
an attribute within tolerance range while Taguchi methods emphasize the attainment of
the specified target value and the elimination of variation.

The characteristics can be controlled, such that a lower or a higher value in a particular
quality-influencing factor produces the preferred result. Thus, the levels of influencing
factors, to produce the best results, can be predicted. There are two different
methodologies in carrying out the complete Orthogonal Analysis. A common approach is
to analyze the average result of repetitive runs or a single run through ANOVA analysis,
as discussed above. The other approach, which is a better method for multiple runs, is to
use signal to noise (S/N) ratios for the same steps in the analysis. The objective of S/N
analysis is to determine the most optimum set of the operating conditions from variations
of the influencing factors within the results. The signals, in this case, will be those
factors, which are invariant.


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International Journal of Industrial Engineering Research and Development (IJIERD), ISSN 0976 –
6979(Print), ISSN 0976 – 6987(Online) Volume 2, Issue 1, May - October (2011), © IAEME


2.0 LITERATURE REVIEW

Taguchi (1986) has introduced several new statistical tools and concepts of quality
improvement that depend heavily on the statistical theory of experimental design. Some
applications of Taguchi’s methods in the foundry industry have shown that the variation
in casting quality caused by uncontrollable process variables could be minimized. Otto
and Antonsson (1991) extended the Taguchi’s method, which involved variables, each of
which having a range of values which might be used for analysis. Tuning parameters, as a
part of the design process, were also demonstrated within Taguchi’s method. They also
extended this method to solve design problems with constraints, involving the methods of
constrained optimization. They compared their design with other methods of searching
the design space and their definitions of an optimal solution. Ghani et al. (2004) used the
Taguchi method in the optimization of end milling process parameters. They applied it to
optimize the cutting parameters in end milling when machining hardened steel with a tin-
coated carbide insert tool under the semi-finishing and finishing conditions of high-speed
cutting.

Shaji and Radhakrishnan (2003) performed an analysis of the process parameters in
surface grinding with graphite as the lubricant based on the Taguchi method. They dealt
with the analysis of the process parameters such as speed, feed, in feed and mode of
dressing as influential factors on the force components and surface finish developed, and
based on Taguchi’s experimental design methods. Vlachogiannis and Roy (2005) worked
on fine-tuning of proportional integral derivative (PID) controllers, under model
parameter uncertainties (noise), using the Taguchi method. The Taguchi method provided
sub-optimal values for fine PID tuning in the presence of model parameter uncertainties
(noise). They also calculated the contribution of each factor to the variation of the mean
and the variability of error. The expected cost savings for PID under optimum condition
were calculated. They also performed confirmation experiments on a real PID controller.

Esme (2009) investigated the effect of welding parameters on the tensile shear strength in
the resistance spot welding (RSW) process. The experimental studies were conducted
under varying electrode forces, welding currents, electrode diameters, and welding times.
The level of importance of the welding parameters on the tensile shear strength was also
determined by using analysis of variance (ANOVA). The optimum welding parameter
combination was obtained by using the analysis of signal-to-noise (S/N) ratio. Their
confirmation tests indicated that it is possible to increase tensile shear strength
significantly by using the Taguchi method. The experimental results confirmed the
validity of the used Taguchi method for enhancing the welding performance and
optimizing the welding parameters in the resistance spot welding process. Mahapatra and
Chaturvedi (2009) work on the modelling and analysis of behaviour of composites
reinforced with short fibre drawn from agricultural resources. The optimum size of short
fibre, just capable of transferring the load and flexibility during preparation had been
studied through a systematic modelling approach and empirical model. The effect of
various test parameters and their interactions had been studied to find out optimal



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International Journal of Industrial Engineering Research and Development (IJIERD), ISSN 0976 –
6979(Print), ISSN 0976 – 6987(Online) Volume 2, Issue 1, May - October (2011), © IAEME

parameter setting for minimum wear (weight loss). The recommended fibre length of 7-8
mm for minimum wears of the composites.

Badkar et al. (2010) presented the application of Taguchi method and its utility concept
for optimizing the laser process parameters in laser transformation hardening of
commercially pure titanium using a continuous-wave 2-kW, Nd:YAG laser. They used
Taguchi tools, such as analysis of variance (ANOVA), signal-to-noise ratio, and additive
model had been used to analyze and evaluate the optimum combination levels of laser
transformation hardening process parameters. The optimization results revealed that a
combination of higher levels of scanning speed (SS) and FP focused position (FP), i.e.,
increase in defocused beam with negative focal length along with laser power (LP) in the
lower level, is an essential laser hardening parameter to simultaneously minimize the HD
and maximize the HBW.

Elangovan and Narayanan (2010) found that the formability of perforated sheet depend
on chemistry of the material, the forming parameters, dimensions of the perforations and
ligament width. The forming limit diagram for perforated Al 8011 sheet of 1mm
thickness had been evaluated and the influences of the diameters of perforations and
ligament widths on the forming limit strains had been studied. Wazed et al. (2010)
measured the individual and combined impacts of common parts and machines in
manufacturing, under bottleneck and uncertain conditions. They used machine
breakdown and quality variation to create uncertainty. The authors examined the delivery
performances such as (i) throughput of the finished products, (ii) average production time
and (iii) work-in-progress in the system for different experimental scenarios. They
developed the simulation models, based on a live case from a Malaysian company. They
concluded that batch size of 12 in bottleneck, 2 common parts and 4 common machines
ensure the best outcomes of the system under the storm of uncertainties. The main
contribution of their research is to find out the best batch size in bottleneck point under
uncertainties, commonalities and capacity constraint.

3.0 INTRODUCTION OF INDUSTRY AND PRODUCT

It is the pioneer industry in the manufacturing of automobile tubes in India and was
established in 1969. It supplies its products to New Holland, Suzuki, DCM Toyota,
Nissan, and Mahindra. It also manufactures automotive hoses and tubes. It signed up a
joint venture with TRI (Tokai Rubber Industries of Japan) in 2005. It is ISO-9000 and
ISO-14001certified since 2003. It supplies Pipe Assemblies, Hose Assemblies, Tubing’s,
Multi Layer Hoses, Stainer Assemblies, Molded Components, and Pipes etc. for all kinds
of worldwide automotives products. Flexible hose assembly pipes are used in auto motive
vehicles for fuel and air supply.

3.1 In-House Rejection

The main problem is associated with the crimping joint failure in the flexible hose
assembly. The in-house rejection data from April 2009 to March 2010 is considered for
this industry. A study is carried out on this internal rejection data and during the analysis.

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            International Journal of Industrial Engineering Research and Development (IJIERD), ISSN 0976 –
            6979(Print), ISSN 0976 – 6987(Online) Volume 2, Issue 1, May - October (2011), © IAEME

            It is observed that the main causes for the rejection are Crimping Leakage, Banjo Face
            Leakage, Pipe Leakage, Banjo Fault, Brazing Leakage, Crimping Fault, Crack (Nut,
            Nipple and Pipe), Hose Puncture, imperfect Fitment etc. Table 1 shows the various
            defects, contributing the rejection of Flexible Hose Assembly, as given below.

            Table 1: Defect wise in-house rejection of Flexible Hose Assembly
                                                    Rejection Quantity                            Rejection
                  Causes
                                                          (in Nos.)                            (in percentage)
            Crimping Leakage                          1598                             59.16
            Banjo Face Leakage                        587                              21.73
            Pipe Leakage                              147                              05.44
            Banjo Fault                               134                              04.96
            Brazing Leakage                           125                              04.62
            Crimping Fault                             64                              02.36
            Crack (Nut, Nipple & Pipe)                 21                              00.77
            Hose Puncture                              13                              00.48
            Improper Fitment                           12                              00.46
            TOTAL                                     2701                              100

            Table 1 shows that the leakage in the Crimping Joint is the major defect which,
            contributing about 59% in the rejection of Flexible Hose Assembly. The cause & effect
            diagram for the crimping leakage defect is prepared for finding the possible causes for
            leakage from crimping joint, as shown in Figure 1.

            Figure 1: Cause and effect diagram of crimping joint failure

                                                                         Materials, Policies, Regulations, etc
                      Personnel, Staffing, etc

                Unskilled Worker                                   Cap ID Oversize
                     Absenteeism                                   Improper Material

Causes of
 Failure              Less Crimping Pressure                      Crimping die not checked
                                                                 Less Crimping Force                         Crimping Joint
                                                                                                                Failure

                     Crimping die worn out
             Variation in Insertion Length               Crimping joint fouling
                        of pipe                       with sharp corner of the Tank




                 Procedures, Methods, Specs, etc.                      Plant, Machines and Equipments,
                                                                                     etc.



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International Journal of Industrial Engineering Research and Development (IJIERD), ISSN 0976 –
6979(Print), ISSN 0976 – 6987(Online) Volume 2, Issue 1, May - October (2011), © IAEME

3.2 Monthly rejection details
Further it is analyzed that one of the major defect for internal rejection is the leakage
from the Crimping Joint. Table 2 shows the rejection quantity in parts per million (PPM)
and monthly percentage rejection as given below.
Table 2: Monthly crimping leakage rejection
                                                                Parts Per Million    Percentage
  Month       Rejection Quantity        Total Production
                                                                     (PPM)            Rejection

  APR’09           126             126700                              994          0.0994
                                                                                      48
 MAY’09            128             104999                             1219          0.1219
                                                                                      06
  JUN’09           136             121409                             1120          0.1120
                                                                                      18
  JUL’09           142             120545                             1178          0.1177
                                                                                      98
  AUG’09           155             141523                             1095          0.1095
                                                                                      23
  SEP’09           138             131710                             1048          0.1047
                                                                                      76
  OCT’09           135             144045                              937          0.0937
                                                                                      21
  NOV’09           136             150705                              902          0.0902
                                                                                      43
  DEC’09           118             115959                             1018          0.1017
                                                                                       6
  JAN’10           124             144493                              858          0.0858
                                                                                      17
  FEB’10           132             130767                             1009          0.1009
                                                                                      43
 MAR’10            128             141878                              902          0.0902
                                                                                      18
  TOTAL           1598             157473                            12282
                                        3
4.0 ANALYSIS

The Probable causes of the Crimping leakage problem are found from cause and effect
diagram and listed below.
    1. Less crimping pressure
    2. Less Insertion length of pipe hose/ Hose in cap
    3. Cap Inner Diameter oversize
    4. Uneven crimping depth
    5. Improper Materials
    6. Crimping operation not performed.

4.1 Application of Taguchi Approach

Taguchi methodology is used to find the optimal solution regarding causes for rejection.
Three main defects: Less Crimping Depth, Hose Length Variation and Cap Inner
Diameter (Oversize) are considered for the Crimping Leakage of Hose Pipe Assembly.
Table 3 shows the process parameter with their ranges, as shown below.




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International Journal of Industrial Engineering Research and Development (IJIERD), ISSN 0976 –
6979(Print), ISSN 0976 – 6987(Online) Volume 2, Issue 1, May - October (2011), © IAEME

Table 3: Process parameters with their ranges and values at three
                             levels
        Parameter designation           Process parameter                   Range

               A                    Less Crimping Depth (mm)                0.35-0.4

               B                   Hose Length Variation (mm)                18-22

               C                   Cap I.D. Oversize (mm)                    15.5-16

4.2 Selection of Orthogonal Array
Before selecting the particular orthogonal array, two major factors are considered:
1.       The number of parameters and interaction of interest,
2.       The number of levels for the parameters of interest.
In this case study, the L8 orthogonal array is considered, as shown in Table 4.

Table 4: L8 orthogonal array
  Trial
   No.          A         B       A*B         C          A*C          B*C       A*B*C
    1           1          1       1           1            1          1             1
    2           1          1       1           2            2          2             2
    3           1          2       2           1            1          2             2
    4           1          2       2           2            2          1             1
    5           2          1       2           1            2          1             2
    6           2          1       2           2            1          2             1
    7           2          2       1           1            2          2             1
    8           2          2       1           2            1          1             2

5.0 EXPERIMENTATION

Once the parameters and interaction of parameter are assigned to a particular column of
the selected orthogonal array, the factors at different levels are assigned for each trial.
The experiments are conducted twice for the same set of parameters, using a single
repetition randomization technique. The crimping leakage defects occurring in each trial
conditions are measured. The flexible hose assembly is made against the trial conditions,
as given in Table 5.

Table 5: Experimental L8 Array
Trial No.             A                   B                             C
               Less Crimping     Hose Length Variation          Cap I.D. Oversize
                Depth (mm)              (mm)                          (mm)
   1                0.35                  18                           15.5
   2                0.35                  18                           16.0
   3                0.35                  22                           15.5
   4                0.35                  22                           16.0
   5                 0.4                  18                           15.5



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International Journal of Industrial Engineering Research and Development (IJIERD), ISSN 0976 –
6979(Print), ISSN 0976 – 6987(Online) Volume 2, Issue 1, May - October (2011), © IAEME

     6             0.4                      18                     16.0
     7             0.4                      22                     15.5
     8             0.4                      22                     16.0

The average of the crimping leakage defects is computed for each trial condition and it is
shown in Table 6.

Table 6: Percentage defects in Experiments
Trail                              Percentage Defects in Experiments
No.                                 Level 1                    Level 2
 1                          16.5                        17.3
 2                          18.4                        19.2
 3                          16.8                        17.5
 4                          17.3                        19.1
 5                          18.4                        21.2
 6                          22.4                        23.2
 7                          18.2                        21.3
 8                          19.3                        22.2


5.1 Sample Calculations for Signal to Noise Ratios
The crimping leakage defects are “Lower the Better” type of quality characteristics.
Lower the better S/N ratios are computed for each of the 8 trials and sample calculations
are also given as under.
Lower is better: S/NLB ratio = -10 log [(Σy2i)/n]

For the case of minimizing the performance characteristic, the following values of the
S/N ratio are calculated as:




For the case of maximizing the performance characteristic, the S/N ratio is calculated as:




 Where, n is the number of observation and yi are the different experimental values for
various trials.

Sample calculations
For Example: Trial No., Table Number 7;
Percentage sum of defects = percentage defect at level 1 (16.5) + percentage defect in
level 2 (17.3) = 33.8.
Average percentage defect = percentage sum of defects/ 2 and
Average S/N ratio= -10log (16.52+17.32)/2 = -24.56


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International Journal of Industrial Engineering Research and Development (IJIERD), ISSN 0976 –
6979(Print), ISSN 0976 – 6987(Online) Volume 2, Issue 1, May - October (2011), © IAEME

Table 7 shows the Crimping defect values and S/N ratios as shown below.
Table 7: Crimping defect values and S/N ratio against trial number

                                    Percentage Defects in Experiment
    Trial No.       1              2           Sum             Average                 S/N Ratio
        1          16.5           17.3         33.8             16.9                    -24.56
        2          18.4           19.2         37.6             18.8                    -25.48
        3          16.8           17.5         34.3             17.15                   -24.68
        4          17.3           19.1         36.4             18.2                    -25.21
        5          18.4           21.2         39.6             19.8                    -25.95
        6          22.4           23.2         45.6             22.8                    -27.16
        7          18.2           21.3         39.5             19.75                   -25.93
        8          19.3           22.2         41.5             20.75                   -26.36
      Total       147.3           161                                                   -205.33

5.2 Other sample Calculations

1. Correction Factor (C.F.)
Correction Factor (C.F.) = (Sum Total) 2/ No. of Observations
                          = (16.5+17.3+…………..+22.2) 2 / 16
                     C.F. = 5940.56

2. The total sum of squares
              SStot = Y2 - C.F.
                     = (16.5) 2 + (17.3) 2 +………(22.2) 2 – 5940.56
              SStot = 69.0344

 3. Average value for casting defects and S/N ratio at different levels
 The mean response, referring to the average value of the performance characteristics (the
crimping leakage defects and S/N ratios) for each parameter at different levels is given in
Table 8. The Figure 2 shows the graphical representation of the crimping leakage defects
at different levels.

Table 8: Average value for crimping defects and S/N ratios at different levels
                          Average value of casting defects and S/N ratio at different levels

   Factors                       Level 1                                     Level 2

                Crimping Defect            S/N ratio       Crimping Defect             S/N ratio

       A
                        17.76                -24.98               20.78                     -26.35
       B
                        19.58                -25.79               18.96                     -25.55
       C
                          18.4               -25.28               20.14                     -26.05


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International Journal of Industrial Engineering Research and Development (IJIERD), ISSN 0976 –
6979(Print), ISSN 0976 – 6987(Online) Volume 2, Issue 1, May - October (2011), © IAEME


Figure 2: Average value of Crimping Defect on different levels

         21
                         20.78
        20.5
    D
    E                                                           20.14
        20
    F
    E
    C 19.5                           19.58
    T
         19                                  18.96                        Series1
    %

        18.5
                                                         18.4

         18
                 17.76
        17.5

         17
               A1   A2        B1   B2        C1   C2
                 AVERAGE VALUE OF CRIMPING DEFECT ON
                          DIFFERENT LEVEL'S




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International Journal of Industrial Engineering Research and Development (IJIERD), ISSN 0976 –
6979(Print), ISSN 0976 – 6987(Online) Volume 2, Issue 1, May - October (2011), © IAEME

Figure 3 shows the graphical representation of the average values of S/N ratio at
different levels.

                              -26.5
                                                          -26.35


                               -26                                                                              -26.05

                                                                             -25.79
Signal to Noise Ratio (S/N)




                              -25.5                                                    -25.55

                                                                                                       -25.28

                               -25              -24.98



                              -24.5



                               -24
                                          A1         A2                 B1        B2             C1         C2
                                                     Average value of S/N ratio at different level's



4. Calculation of Sum of Squares for different factors
        SSA = (Sum total in level 1)2 + (Sum total in level 2)2 - C.F.
                              No. of Observations
             = 36.3
Similarly, the sums of squares for other factors are calculated, as shown in Table 9.

       Table 9: Sum of Squares for various factors
                                      Sr. No.                  Source                           Sum of Squares
                               1                                 A                              36.3
                               2                                 B                               1.5
                               3                                A*B                              0.77
                               4                                 C                              12.08
                               5                                A*C                             0.28
                               6                                B*C                              2.03
                               7                               A*B*C                             0.33
                                                                SStot                           53.29

Calculation of the error sum of squares
      SSe = SStot – ( SSA+ SSB+…………………………+SSD)
           = 69.034 – 53.294
           = 15.74


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International Journal of Industrial Engineering Research and Development (IJIERD), ISSN 0976 –
6979(Print), ISSN 0976 – 6987(Online) Volume 2, Issue 1, May - October (2011), © IAEME


5.3 Analysis of Experimental Results
Table 10 shows the ANOVA table for crimping leakage defect in flexible hose assembly.

Table 10: ANOVA TABLE for crimping defects in flexible hose
assembly
                       ANOVA for crimping defects
  Source        Sum of   Degree of    Variance             F ratio      Results        Percentage
                Square   Freedom                                                       Contribution
                 (SS)
    A             36.3      1            36.3              18.4498       Significant       52.58
    B              1.5      1             1.5              0.76239                         0.021
   A*B            0.77      1            0.77              0.39136                         0.011
    C            12.08      1           12.08              6.13977       Significant       17.49
   A*C            0.28      1            0.28              0.14231                         0.004
   B*C            2.03      1            2.03              1.03177                         0.029
  A*B*C           0.33      1            0.33              0.16773                         0.004
 Error (e)       15.74      8           1.9675
   Total         69.03      15

6.0 RESULTS

It is clear from the ANOVA table that the Less Crimping Depth (mm) and Cap Inner
Diameter (Oversize) have significant effect on the Flexible Hose Assembly. The
optimum levels for these factors can be obtained by examining the level averages of the
factors, as shown in Table 11.

Table 11: Affecting Parameters value at the different levels average
      Factors                                   Level Average
                                         1                              2
     Less Crimping Depth (mm)          17.76                          20.78
      Oversized Cap I.D. (mm)          18.4                           20.14


Various experiments are conducted on flexible hose assembly product for this case study.
ANOVA, along with interpretation method is used to obtain the percentage contribution
of each parameter and optimum levels of each parameter are shown in Table 12.

Table 12: Process parameter optimum level with optimal value
 Parameter         Process parameter                Optimum          Optimum      Percent
 designation                                          level           value       Contribution (%)
        A          Less Crimping Depth (mm)            1               0.35            52.58
        C          Cap I.D. Oversize (mm)              2               16              17.49




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International Journal of Industrial Engineering Research and Development (IJIERD), ISSN 0976 –
6979(Print), ISSN 0976 – 6987(Online) Volume 2, Issue 1, May - October (2011), © IAEME



7.0 CONCLUSIONS

The contribution of individual quality influencing factors is the deciding key of the
control to be enforced on the product design. A commonly applied statistical treatment -
The Analysis of Variance (ANOVA) is used to analyze the results of the Orthogonal
Array (OA) experiment in product design and to determine how much variation each
quality-influencing factor has contributed. By studying the main effects of each one of
the factors, the general trends of the influencing factors towards the product or process
can be characterized.

Table 12 clearly shows that two most significant parameters affect both the mean and
variation of the crimping leakage defects and also shows the optimal settings of each
parameter to reduce the crimping leakage defects and hence improves the quality of
flexible hose assembly at lowest cost. Before the application of Taguchi’s method, the
parameters of the crimping leakage process were more arbitrary and difficult to control
and, of course, the product quality has instability problems. Taguchi’s method yielded
optimized control factors, resulting in superior product quality and stability. From the
analysis, it is found that the improvement in the quality at the lowest possible cost can be
achieved by Taguchi’s method of parameter design. It is also possible to identify the
optimum levels of signal factors at which the noise factor’s effect on the response
parameter is minimized. The optimized process parameters i.e. lower crimping depth of
0.35 mm and cap inner diameter (oversize) of 16 mm are obtained by Taguchi
methodology, which lead to minimize the crimping leakage defects.
The confirmation experiments also indicate a clear picture of every factor’s contribution
to the variation in the flexible hose assembly manufacturing process and improvement in
the quality without any additional investment.

REFERENCES

   1. Badkar, D.S.; Pandey, K. S. and Buvanashekaran, G. (2010),“Parameter
       optimization of laser transformation hardening by using Taguchi method and
       utility concept,” International Journal of Advanced Manufacturing Technology,
       Volume 1, Number 1, pp. 123-135.
   2. Elangovan, K. and Narayanan, C.S. (2010),“Application of Taguchi approach on
       investigation of formability for perforated Al 8011 sheets,” International Journal
       of Engineering, Science and Technology, Volume 2, Number 5, pp. 300-309.
   3. Esme, U. (2009),“Application of Taguchi Method for the optimization of
       resistance spot welding process,” The Arabian Journal for Science and
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International Journal of Industrial Engineering Research and Development (IJIERD), ISSN 0976 –
6979(Print), ISSN 0976 – 6987(Online) Volume 2, Issue 1, May - October (2011), © IAEME

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   11. Wazed, M. A.; Ahmed, S. and Nukman, Y. (2010),“Application of Taguchi
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       Volume 5, Number 10, pp. 1576-1591.




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