Docstoc

APPLICATION OF TAGUCHI METHOD IN THE OPTIMIZATION OF BORING PARAMETERS-2

Document Sample
APPLICATION OF TAGUCHI METHOD IN THE OPTIMIZATION OF BORING PARAMETERS-2 Powered By Docstoc
					  International Journal of Advanced Research OF ADVANCED RESEARCH IN
  INTERNATIONAL JOURNAL in Engineering and Technology (IJARET), ISSN
  0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 4, May – June (2013), © IAEME
             ENGINEERING AND TECHNOLOGY (IJARET)

ISSN 0976 - 6480 (Print)
ISSN 0976 - 6499 (Online)
                                                                        IJARET
Volume 4, Issue 4, May – June 2013, pp. 191-199
© IAEME: www.iaeme.com/ijaret.asp
Journal Impact Factor (2013): 5.8376 (Calculated by GISI)
                                                                        ©IAEME
www.jifactor.com




   APPLICATION OF TAGUCHI METHOD IN THE OPTIMIZATION OF
                    BORING PARAMETERS

              Ajeet Kumar rai*, Shalini yadav, Richa Dubey and Vivek Sachan
                             Mechanical Engineering Department
    Sam Higginbottom Institute of Agriculture, Technology and Sciences, Allahabad-211004,
                                             India


  ABSTRACT

          In the present study, Taguchi method is applied to find optimum process parameters
  in the boring operation of a cast iron work piece. A L27 orthogonal array, signal-to-noise ratio
  and analysis of variances are applied to study the performance characteristics of machining
  parameters (cutting speed, feed rate and depth of cut) with consideration of surface finish.
  Experimental results reveal that among the cutting parameters, the depth of cut is most
  significant machining parameter for surface roughness followed by feed rate and cutting
  speed in the specified test range.

  Keywords:, Optimization, Taguchi method, S/N ratio, Boring operation

  INTRODUCTION

          Taguchi parameter design offers a systematic approach for optimization of various
  parameters with regards to performance, quality and cost. And it is important in a sense to
  meet the challenge coming before the manufacturers, which are to increase the production
  rate, reducing operating cost and enhancing the quality of production. Taguchi primarily
  recommends experimental design as a tool to make products more robust- to make them less
  sensitive to noise factors. He views experimental design as a tool for reducing the effect of
  variation on product and process quality characteristics [1]. The complete procedure in
  Taguchi design method can be divided into three stages: system design, parameter design and
  tolerance design. Of the three design stages, the second stage- the parameter design –is
  considered to be the most important stage [2]. This stage of Taguchi parameter design
  requires that the factors affecting quality characteristics in the manufacturing process have to
  be determined. The major goal of this stage is to identify the optimal cutting conductions that

                                                191
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN
0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 4, May – June (2013), © IAEME

yield the lowest surface roughness value. Few steps to be followed in the Taguchi parameter
design are: selecting the proper orthogonal array (OA) according to the numbers of
controllable factors, running experiments based on the OA, analyzing data, Identifying the
optimum condition, and conducting confirmation runs with the optimal levels of all the
parameters. Taguchi method is used by several researchers to carry out their studies in
various machining operations like turning, end milling, drilling etc.
        Yang et al [3] used the Taguchi parameter design in order to identify optimum surface
roughness performance on an aluminum material with cutting parameters of depth of cut,
cutting speed, feed rate and tool diameter. It was found that tool diameter is not a significant
cutting factor affecting the surface roughness. Bagci et al [4] used the Taguchi method to
explore the effects of drilling parameters on the twist drill bit temperature for a design
optimization of cutting parameters. Zhang et al [5] performed a study of the Taguchi Design
application to optimize surface quality in a CNC face milling operation. Taguchi design was
successful in optimizing milling parameters for surface roughness. Nalbant et al. [6] used
Taguchi method to find optimum cutting parameters for surface roughness in turning of AISI
1030 carbon steel bars using TiN coated tools. Three cutting parameters namely, insert
radius, feed rate, and depth of cut are optimized with considerations of surface roughness. In
turning, use of greater insert radius, low feed rate and low depth of cut are recommended to
obtain better surface roughness for the specific test range. Ghani et al. [7] applied Taguchi
method to find optimum cutting parameters for surface roughness and cutting force in end
milling when machining hardened steel AISI H13 with TiN coated P10 carbide insert tool
under semi-finishing and finishing conditions of high speed cutting. The milling parameters
evaluated is cutting speed, feed rate, and depth of cut. In end milling, use of high cutting
speed, low feed rate and low depth of cut are recommended to obtain better surface roughness
and low cutting force. Kurt et al [6] employed the Taguchi method in the optimization of
cutting parameters for surface finish and hole diameter accuracy in dry drilling processes.
The validity of the Taguchi approach to process optimization was well established.
From the above stated literature review, it becomes clear that the Taguchi Design method has
been widely applied with great success for optimizing industrial/production processes.
Keeping this perspective the present work has been taken with the objective to investigate the
effects of different boring parameters on surface roughness, and is to determine the optimal
boring parameters using the Taguchi technique.

EXPERIMENTAL DESIGN

        Table1 shows three factors and three levels used in the experiment. For selecting
appropriate arrays, degree of freedom of array is calculated. There are six degrees of freedom
owing to three machining parameters, so Taguchi based L27 orthogonal array is selected
(Table 2). Accordingly 27 experiments were carried out to study the effect of machining
input parameters. Each experiment was repeated three times in order to reduce experimental
errors.

                           Table 1: Level of process parameters
Symbol          Factors                               Level 1         Level 2       Level 3
A               Cutting Speed (m/min)                 80              100           120
B               Feed (mm/rev.)                        0.05            0.1           0.15
C               Depth of cut (mm)                     0.3             0.4           0.5

                                              192
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN
0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 4, May – June (2013), © IAEME

                         Table 2: Taguchi’s L27 orthogonal array
                                                   Factor
  Standard order                     A                          B                      C

          1                      80                      0.05                    0.3
          2                      80                      0.05                    0.4
          3                      80                      0.05                    0.5
          4                      80                       0.1                    0.3
          5                      80                       0.1                    0.4
          6                      80                       0.1                    0.5
          7                      80                      0.15                    0.3
          8                      80                      0.15                    0.4
          9                      80                      0.15                    0.5
          10                     100                     0.05                    0.3
          11                     100                     0.05                    0.4
          12                     100                     0.05                    0.5
          13                     100                      0.1                    0.3
          14                     100                      0.1                    0.4
          15                     100                      0.1                    0.5
          16                     100                     0.15                    0.3
          17                     100                     0.15                    0.4
          18                     100                     0.15                    0.5
          19                     120                     0.05                    0.3
          20                     120                     0.05                    0.4
          21                     120                     0.05                    0.5
          22                     120                      0.1                    0.3
          23                     120                      0.1                    0.4
          24                     120                      0.1                    0.5
          25                     120                     0.15                    0.3
          26                     120                     0.15                    0.4
          27                     120                     0.15                    0.5

RESULTS AND DISCUSSION

    The Taguchi method employs a generic signal- to–noise (S/N) ratio to quantify the
present variation. These S/N ratios are meant to be used as measures of the effect of noise
factors on performance characteristics. S/N ratios take into account both amount of variability
in the response data and closeness of the average response to target. There are several S/N
ratios available depending on type of characteristics: smaller is better, nominal is better and
larger is better. Twenty-seven experiments were performed using the design parameter

                                             193
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN
0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 4, May – June (2013), © IAEME

combinations in the specified orthogonal array table. Nine specimens were fabricated for
each of the parameter combinations. The complete response table for these data appears in
Table 3. In order to estimate the effect of factor A (Cutting Speed) on average value of
response variables, were summed together nine observed response at level 1 of factor A.
Then the sum was divided by nine to obtain the average response. Average responses at level
2 and level 3 were obtained in the similar manner. The estimated effects are presented
graphically in fig. 2. The range of average responses over the three levels of each
experimental factor is:
        For Cutting speed = 72.5
        For Feed rate = 41.6666
        For Depth of cut = 31.3889
In particular, factor A, B and C should be set at level 2, level 3 and level 1 respectively.


                       350

                       300

                       250
             Average




                       200

                       150

                       100

                        50

                        0
                             A1   A2   A3        B1    B2    B3        C1    C2   C3
                                                 Level of factor



                                  Figure 2: Estimated factor effects

         The sample standard deviation is generally accepted measure of variability in
statistical data analysis and experimental design. This statistics is somewhat more difficult to
calculate than the sample range, but it has desirable properties which make its use worth the
added effort.
         The standard deviation was calculated for each tube in five steps. First, y was
subtracted from each measurement in the sample (sample mean), then the square differences
obtained prior were calculated. Next, the squared obtained differences were and was divided
the sum by the sample size minus one (s2). Finally obtain the square root of s2. The sample
variance is written as

s2 = ∑(y-y)2 /(n-1)                                                    (1)

s = √s2                                                                (2)



                                                 194
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN
0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 4, May – June (2013), © IAEME

                      Table 3: Experimental data sample statistics

  Experiment       Observed response        Mean      Standard       Log of   S/N Ratio
   Number           values of Surface                 Deviation       S.D.
                    Roughness (µm)
        1            10           15         12.5       3.5355       0.5484    -21.9382
        2           280          300          290      14.1421       1.1505    -49.2479
        3           340          360          350      14.1421       1.1505    -50.8813
        4           400          425         412.5     17.6776       1.2474    -52.3084
        5           450          425         437.5     17.6776       1.2474    -52.8195
        6           440          415         427.5     17.6776       1.2474    -52.6187
        7           345          375          360      21.2132       1.3266    -51.1260
        8           290          320          305      21.2132       1.3266     -49.685
        9           300          335         317.5     24.7487       1.3935    -50.0348
       10           320          340          330      14.1421       1.1505    -50.3702
       11           335          350         342.5     10.6066       1.0255    -50.6932
       12           300          315         307.5     10.6066       1.0255     -49.756
       13           230          245         237.5     10.6066       1.0255    -47.5132
       14           145          165          155      14.1421       1.1505    -43.8066
       15           250          225         237.5     17.6776       1.2474    -47.5132
       16           240          220          230      14.1421       1.1505    -47.2345
       17           280          305         292.5     17.6776       1.2474    -49.3225
       18           115          140         127.5     17.6776       1.2474    -42.1102
       19           275          285          280       7.0710       0.8494    -48.9431
       20           210          225         217.5     10.6066       1.0255    -46.7491
       21           290          300          295       7.0710       0.8494    -49.3964
       22           250          280          265      21.2132       1.3266    -48.4649
       23           290          315        302.52     17.6776       1.2474    -49.6145
       24           275          250         262.5     17.6776       1.2474    -48.3825
       25           230          210          220      14.1421       1.1505    -46.8484
       26           275          300         287.5     17.6776       1.2474    -49.1727
       27           215          230         222.5     10.6066       1.0255    -46.9466


The estimated log s effects from Table 3 are plotted in Fig.3.In order to minimize the
variability the following optimum results were obtained.

       Factor A, Cutting Speed at level 3
       Factor B, feed rate at level 1
       Factor C, Depth of cut at level 1




                                            195
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN
0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 4, May – June (2013), © IAEME



                    1.4

                    1.2

                     1

                    0.8
          Log (s)




                    0.6

                    0.4

                    0.2

                     0
                          A1     A2   A3         B1      B2   B3       C1     C2   C3
                                                 Level of Factor



                               Figure 3: Estimated factor effects on log(s)

    In this work, the minimum surface roughness is the indication of better performance.
Therefore, the smaller-is-better for the surface roughness was selected for obtaining optimum
result. The following S/N ratios for the lower-is-better case could be calculated:S/NLB = −10
               2
Log (         i )




                               Fig 4 photograph showing experimentation




                                                   196
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN
0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 4, May – June (2013), © IAEME

                         -44.5
                          -45     A1   A2   A3         B1    B2    B3       C1    C2    C3
                         -45.5
                          -46
                         -46.5
             S/N Ratio



                          -47
                         -47.5
                          -48
                         -48.5
                          -49
                         -49.5
                                                       Level of Factor



                                  Figure 4: Plot of factor effects on S/N Ratio

                                       Table 4: Overall mean S/N Ratio
      Level                            Average S/N Ratio by factor level               Overall mean
                                     A                B                 C               S/N Ratio
         1                        -47.8510        -46.4417          -46.0829             -47.9072
         2                        -47.5910        -49.2268          -49.0123
         3                        -48.2798        -48.0534          -48.6266

        In order to maximize the S/N ratio the following assignments were done: factor A
(Cutting speed) – level 2, factor B (Feed rate) – level 1, factor C (Depth of cut) – level 1.
Figure 4 shows that factor C have a strong effect on S/N ratio response. Factor B is the next
most significant. The above analyses of table 3 and table 4 are summarized in table 5. In that
table the levels of key factors which are optimizing the response are listed. Some significant
levels are shown in fig. 2, 3 and 4. Keep in mind that the objective is to minimize the
response average, minimize log s, and maximize the S/N Ratio.

                                 Table 5. Summary of analyses of factor effects
                                          Level which was optimized
    Factor                                   y                       Log s               S/N Ratio
      A                                      2                          3                    2
      B                                      3                          1                    1
      C                                      1                          1                    1

In this study factor A and B were dominant. For parameter C, reducing log s will have little
effect on the performance than the S/N ratio. So level 1 is optimized. The final optimized
values are:-
    1) Cutting speed: - Level 2 – 100 m/min.
    2) Feed rate: - Level 1 - 0.15 mm/rev.
    3) Depth of cut: - Level 1 - 0.3 mm.

                                                      197
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN
0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 4, May – June (2013), © IAEME

CONCLUSIONS

        In this study, the Taguchi optimization method was applied to find the optimal
process parameters, which minimizes the surface roughness during the boring of cast iron. A
Taguchi orthogonal array, the signal to noise(S/N) ratio and the analysis of variance
(ANOVA), were used for the optimization of cutting parameters. Results show that depth of
cut will have great influence on the surface roughness followed by feed and cutting speed.

REFERENCES

[1] ] Lochner R.H. Matar J.E.,(1990) Design for quality- An introduction to the best of
Taguchi and western methods of statistical experimental design. New York.
[2] Taguchi G., Sayed M.Ei., and Hsaing C., (1989) Quality engineering and quality systems.
McGraw-Hill NY.
[3]Yang , J. L., Chen J.C. (2001) A systematic approach for identifying optimum surface
roughness performance in end-milling operations. Journal of Industrial Technology, vol 17,
No 2, P1-8.
[4] Bagci E., Ozcelik B. (2006) Analysis of temperature changes on the twist drill under
different drilling conditions based on Taguchi method during dry drilling of AI 7075-T65.
International JOUrnal of Advanced manufacturing Technology, vol 29,no 7-8, p 629-636.
[5] Zhang, J.Z.; Chen, J.C.; and Kirby, E.D. (2007). Surface roughness optimization in an
end-milling operation using the Taguchi design method. Journal of Material Processing
Technology, 184(1-3), 233-239.
[6] Nalbant, M.; Gokkaya, H.; and Sur, G. (2007). Application of Taguchi method in the
optimization of cutting parameters for surface roughness in turning. Materials & Design,
28(4), 1379-1385.
[7] Ghani, J.A.; Chodhury, I.A.; and Hassan, H.H. (2004). Application of Taguchi method in
the optimization of end milling parameters. Journal of Material Processing Technology,
145(1), 84-92.
[8] Kurt M, Bagci E., Kaynak Y., (2009) Application of Taguchi methods in the optimization
of cutting parameters for surface finish and hole diameter accuracy in dry drilling processes.
T. Childs, K. Maekawa, T. Obikawa and Y. Yamane, metal cutting theory and application,
New York, USA (2000).
[9] Ajeet Kumar Rai, Vivek Sachan and Maheep Kumar, “Experimental Investigation of a
Double Slope Solar Still with a Latent Heat Storage Medium”, International Journal of
Mechanical Engineering & Technology (IJMET), Volume 4, Issue 1, 2013, pp. 22 - 29,
ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359.
[10] Ajeet Kumar Rai and Ashish Kumar, “A Review on Phase Change Materials & Their
Applications”, International Journal of Advanced Research in Engineering & Technology
(IJARET), Volume 3, Issue 2, 2012, pp. 214 - 225, ISSN Print: 0976-6480, ISSN Online:
0976-6499
[11] Ajeet Kumar Rai, Richa Dubey, Shalini Yadav and Vivek Sachan, “Turning Parameters
Optimization for Surface Roughness by Taguchi Method”, International Journal of
Mechanical Engineering & Technology (IJMET), Volume 4, Issue 3, 2013, pp. 203 - 211,
ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359.




                                             198
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN
0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 4, May – June (2013), © IAEME


                                      APPENDIX




                       Fig A photograph showing machined parts




                                           199

				
DOCUMENT INFO
Shared By:
Categories:
Tags:
Stats:
views:0
posted:6/21/2013
language:
pages:9