INVESTIGATING THE EFFECT OF MACHINING PARAMETERS ON SURFACE ROUGHNESS

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INVESTIGATING THE EFFECT OF MACHINING PARAMETERS ON SURFACE ROUGHNESS Powered By Docstoc
					 International Journal of JOURNAL OF MECHANICAL ENGINEERING
INTERNATIONALMechanical Engineering and Technology (IJMET), ISSN 0976 –
 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEME
                         AND TECHNOLOGY (IJMET)

ISSN 0976 – 6340 (Print)
ISSN 0976 – 6359 (Online)                                                    IJMET
Volume 4, Issue 2, March - April (2013), pp. 134-140
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   INVESTIGATING THE EFFECT OF MACHINING PARAMETERS ON
     SURFACE ROUGHNESS OF 6061 ALUMINIUM ALLOY IN END
                         MILLING

          U. D. Gulhane*, M.P.Bhagwat, M.S.Chavan ,S.A.Dhatkar, S.U.Mayekar
                            Department of Mechanical Engineering,
                  Finolex Academy of Management and Technology, Ratnagiri,
                                  Maharashtra 415612, India
         *Corresponding author- Associate Professor, Dept. of Mechanical Engineering,
       Finolex Academy of Management and Technology, P-60/61, MIDC, Mirjole Block,
                               Ratnagiri- (M.S.) 415639, India


  ABSTRACT

         Design of experiments is performed to analyse the effect of spindle speed, feed rate
  and depth of cut on the surface roughness of 6061 Aluminium alloy. The results of the
  machining experiments were used to characterise the main factors affecting surface
  roughness by the Analysis of Variance (ANOVA) method. The feed rate was found to be the
  most significant parameter influencing the surface roughness in the end milling process.

  Keywords: Surface roughness, DOE, ANOVA, 6061 Aluminium alloy.

  INTRODUCTION

          Milling process is one of the common metal cutting operations used for machining
  parts in manufacturing industry. It is usually performed at the final stage in manufacturing a
  product. The demand for high quality and fully automated production focuses attention on the
  surface condition of the product, especially the roughness of the machined surface, because
  of its effect on product appearance, function, and reliability. In the present work an
  experimental investigation of milling on aluminium 6061 with HSS tool is carried out and the
  effect of different cutting parameters on the surface roughness is studied.


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International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEME

       The material used for the analysis is 6061 Aluminium alloy which is widely used in
wings of the aeroplane, wheels of the automobiles.
        In this paper, L9 orthogonal array is employed to analyze experimental results of
machining obtained from 9 experiments by varying three process parameters viz. cutting
speed (A), depth of cut (B) and feed rate(C). ANOVA has been employed and compared with
Taguchi method.

METHODOLOGY

        DOE techniques enable designers to determine simultaneously the individuals and
interactive effects of many factors that could affect the output results in any design. There are
three input parameters and three level. Full factorial experimental design will give rise to
total 33=27 experiments which is time consuming and lengthy procedure.




                                 Fig 1: End- milling operation

             Taguchi found out new method of conducting the design of experiments which
are based on well defined guidelines. This method uses a special set of arrays called
orthogonal array. This standard array gives a way of conducting the minimum number of
experiments which could give the full information of all the factors that affect the response
parameter instead of doing all experiments.
        ANOVA was developed by Sir Ronald Fisher in 1930 and can be useful for
determining influence of any given input parameter for a series of experimental results by
design of experiments for machining process and it can be used to interpret experimental
data. ANOVA is statistical based objective decision making tool for detecting any differences
in average performance of groups of items tested. While performing ANOVA degrees of
freedom should also be considered together with each sum of squares. In ANOVA studies a
certain test error, error variance determination is very important. Obtained data are used to
estimate F value of Fisher Test (F-test). Variation observed (total) in an experimental
attributed to each significant factor or interaction is reflected in percent contribution (P),
which shows relative power of factor or interaction to reduce variation.



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International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEME

MATERIALS AND METHOD

       The Rectangular 25 X 25 X 100 mm 6061 Aluminium Alloy specimens were used for
experimentation. Table 1 and 2 shows properties and composition of 6061 Aluminium Alloy
used for the study. Milling operation was carried out on SINGER UNIVERSAL MILLING
MACHINE by using HSS tool.

                            Table 1 Properties of 6061 Al
                                  TENSILE            TEST                         BHN
                   Ultimate       Yield           Modulus Of                      For 500
                   Stress         Stress           Elasticity                     Kg
                   (N/mm2)        (N/mm2)            (GPa)

                   251.66             202.92                     56.1             79.57



                              Table 2 Composition of 6061 Al
  Elements    Al      Si        Fe    Mg       Ti           Ca       Cd       B        P        Na       Mn


    %        98.81   0.475    0.178   0.49   0.0135       0.0027    0.001   0.0015    0.0014   0.0043   0.0005



        Work piece was inserted in the jaw on the work bed and was tightened in the jaws
until they fixed the work piece such that top surface of the work piece will be perfectly
perpendicular to the tool axis. The milling was carried out for 9 different work pieces. For
each workpiece, all the three parameters, viz. cutting speed, depth of cut and feed rate, were
varied as shown in Table 3.

                           Table 3: Machining parameters and levels:

                              Machining         Level 1          Level 2    Level 3
                              Parameters
                             Cutting speed
                                                     58            220        500
                              (Rev/min)
                             Depth of cut
                                                     0.4            0.8        1.2
                                 (mm)
                               Feed rate
                                                    15.31          41.84     104.56
                              (mm/min)

       The surface roughness of each specimen was tested on the surface roughness tester
(Mitutoyo Roughness tester SJ-400) for cut off value of 4.0 mm distance. The Ra value was
generated by the tester for each work piece.


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International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEME

RESULTS AND DISCUSSION

       Table 4 shows experimental design matrix and surface roughness value (Ra) for 6061
Al, S/N ratio is calculated using Lower the better characteristics.




where, n = No of measurements in a trial/row
     Yi = ith measured value in a run/row

                        Table 4 Experimental Design Matrix and Results
       EXPT
        NO.       MILLING        PARAMETERS
                               DEPTH     FEED               S/N
                   SPEED       OF CUT   RATE    SURFACE RATIO                     MEAN
                   (RPM)        (mm)   (mm/min)     ROUGHNESS
                                                  (µm)
          1           58         0.4     15.32    2.54    -8.0967                  2.54
          2           58         0.8     41.84    2.78    -8.8809                  2.78
          3           58         1.2    104.56    2.97    -9.4551                  2.97
          4          220         0.8     15.32    2.06    -6.2773                  2.06
          5          220         1.2     41.84    2.86    -9.1273                  2.86
          6          220         0.4    104.56     3.4   -10.6296                   3.4
          7          500         1.2     15.32    1.58    -3.9731                  1.58
          8          500         0.4     41.84    1.81    -5.1536                  1.81
          9          500         0.8    104.56    2.54    -8.0967                  2.54

        Responses for Signal to Noise Ratios of Smaller is better characteristics is shown in
Table 5. Significance of machining parameters (difference between max. and min. values)
indicates that feed is significantly contributing towards the machining performance as
difference gives higher values. Plot for S/N ratio shown in Figure 1 explains that there is less
variation for change in depth of cut where as there is significant variation for change in feed
rate.

              Table 5-Response Table for a) Signal to Noise Ratios and (b) means
                  (a)                                                (b)
  Level         A        B        C                 Level      A         B        C
    1         -8.811   -7.96    -6.116                1      2.763     2.583     2.06
    2         -8.678 -7.752 -7.721                    2      2.773      2.46    2.483
    3         -5.741 -7.519 -9.394                    3      1.977      2.47     2.97
  Delta        3.07    0.441    3.278               Delta    0.797     0.123     0.91
  Rank           2       3         1                Rank       2          3       1


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International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEME


                                                              M a in E ff e c ts P lo t f o r S N r a tio s
                                                                               D a ta M e a n s
                                                             Speed                                                Fe e d
                                    -6

                                    -7

                                    -8
                Mean of SN ratios


                                    -9

                                             58               220              500                1 5 .3 3        4 1 .9 2      1 0 4 .9 0
                                                             Depth
                                    -6

                                    -7

                                    -8

                                    -9

                                             0 .4             0 .8             1 .2
              S ig n a l - to - n o is e : S m a l le r is b e tte r




                                                                 M a in E f f e c t s P l o t f o r M e a n s
                                                                                Da ta M e a n s

                                                              Speed                                                Fe e d
                                    3 .0 0

                                    2 .7 5

                                    2 .5 0

                                    2 .2 5
                    Mean of Means




                                    2 .0 0
                                                  58            220              500               1 5 .3 3       4 1 .9 2      1 0 4 .9 0
                                                              De pth
                                    3 .0 0

                                    2 .7 5

                                    2 .5 0

                                    2 .2 5

                                    2 .0 0
                                                  0 .4          0 .8             1 .2




         Fig. 2 Effect of cutting speed, Depth of cut and Feed rate on surface finish

             Taguchi method cannot judge and determine effect of individual parameters on
entire process while percentage contribution of individual parameters can be well determined
using ANOVA. MINITAB software of ANOVA module was employed to investigate effect
of process parameters cutting speed, Depth of Cut and Feed rate.

                                               Table 6-Analysis of Variance for S/N ratios

                                                                                                              Adj
            Source                                  DF         Seq SS Adj SS                                   MS             F                P
              A                                      2         18.0668 18.0668                               9.0334          5.46            0.155
               B                                     2         0.2926 0.2926                                 0.1463          0.09            0.919
               C                                     2         16.121 16.121                                 8.0605          4.87            0.17
           Residual
             error                                       2     3.3098                 3.3098                 1.6549
             Total                                       8     37.7902



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International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEME

                          Table 7-Analysis of Variance for Means

            Source         DF     Seq SS Adj SS Adj MS                F        P
            Speed           2     1.25362 1.25362 0.62681            5.4     0.156
             Feed           2     1.24416 1.24416 0.62208           5.36     0.157
            Depth           2     0.02816 0.02816 0.01408           0.12     0.892
           Residual
             Error          2     0.23209 0.23209 0.11604
             Total          8     2.75802

Table 6 and 7 shows Analysis of variance for S/N ratio and mean. F value (5.46) of parameter
indicates that feed rate is significantly contributing towards machining performance. F value
(0.09) of parameter indicates that depth of cut is contributing less towards surface finish. It
can be observed rough surface for the specimen No. 6 (cutting speed, 220 rev/min; depth of
cut, 0.4 mm; feed, 104.90 mm/min.) and smooth surface for the specimen No. 7 (cutting
speed, 500 rev/min; depth of cut, 1.2 mm; feed, 15.33 mm/min.)




  Fig3: Surface texture for the test (cutting Fig 4: Surface texture for the test (cutting
  speed 500 rev/min, depth of cut 1.2 mm, speed 220 rev/min, depth of cut 0.4 mm,
  feed 15.33 mm/min)                          feed 104.90 mm/min)




                      Fig5: Surface Roughness Profile For specimen 1
                          Cut off length = 4.0 mm, Ra= 2.54 µm

        The pattern of impressions left by tool after the machining of workpiece is called as a
‘lay pattern’ and it is circular in end milling process. When the feed is high the pattern is
more prominent as the time available for traversing is less. When the feed is low the lay
pattern is not much emphasized as more time available for traversing. Hence we observed in
our experimentation that the contribution of feed is dominant amongst all three parameters in
surface roughness.




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International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEME

CONCLUSION

       Taguchi method of experimental design has been applied for investigating the effect
of machining parameters on surface roughness. Results obtained from Taguchi method
closely matches with ANOVA. Best parameters found for finish machining are: cutting
speed, 500 rev/min; depth of cut, 1.2 mm; feed, 15.33 mm/rev. The parameters found for
rough machining are cutting speed, 220 rev/min; depth of cut, 0.4 mm; feed, 104.90 mm/min.
Feed is most influencing parameters corresponding to the quality characteristics of surface
roughness.

ACKNOWLEDGEMENT

      Quality control department of Adler Mediequit PVT.LTD, Ratnagiri are gratefully
acknowledged.

REFERENCES

1. Gulhane U. D., et. al.(2012),” Improvement in surface roughness of 316 L Stainless Steel and
Ti-6Al-4V: DOE Appproach” International Journal of Mechanical Engineering and Technology,
2012, Volume 3, Issue 1, pp. 150 - 160, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359.
2. Patel K.P. (2012) “Experimental analysis on surface roughness of CNC end milling process
using taguchi design method” International Journal Of Engineering Science And Technology
(IJEST) Vol-4 No.02 ISSN:0975-5462.
3. Mohammed T., et. al. (2007) “A Study of the effects of machining parameters on the surface
roughness in the end milling process.”, Jordan Journal Of Mechanical And Industrial
Engineering.(JJMIE) Vol-1No-1. ISSN-1995-665.
4. Kuttolamadom M. A., et. al (2010), “ Effect Of machining feed on surface roughness in
cutting 6061 aluminum” 2010-01-0218.
5. Kakati A., et. al. (2011), “ Prediction of optimum cutting parameters to obtain desired surface
in finish pass end milling of aluminium alloy with carbide tool using ANN” World Academy Of
Science And Engineering and Technology 81.
6. Gopalsamy B. M., et. al. (2009), ”taguchi method and Anova : An Approach for process
parameters optimisation of hard machining while machining hardened steel” Journal of Scientific
and Industrial research, vol.68, pp.686-695.
7. Julian J. Faraway, Practical Regression and ANOVA using R, July 2002, pp 168-200.
8. Phillip J. Ross “ Taguchi Techniques for Quality Engineering” Printed and bounded by R.R.
Donnelley and son’s company 2nd edition.
9. Gulhane U. D. , et. al. (2012), “Optimization of process parameters for 316L stainless steel
by using Taguchi method and ANOVA” International Journal of Mechanical Engineering and
Technology(IJMET). Volume 3, Issue 2, pp. 67-72, ISSN Print: 0976 – 6340, ISSN Online:
0976 – 6359.
10. P.C. Sharma “A text book of production engineering” ISBN: 81-219-0111, Code:10A 038A.
11. N.B.Doddapattar and N Lakshmana swamy, “An Optimization of Machinability of
Aluminium Alloy 7075 and Cutting Tool Parameters by using Taguchi Technique” International
Journal of Mechanical Engineering & Technology (IJMET), Volume 3, Issue 2, 2012,
pp. 480 - 493, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359



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