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  • 1. Pankaj Sharma, Kamaljeet Bhambri / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 5, September- October 2012, pp.1594-1602 Multi-Response Optimization By Experimental Investigation Of Machining Parameters In CNC Turning By Taguchi Based Grey Relational Analysis Pankaj Sharma*, Kamaljeet Bhambri** *(Department of Mechanical Engg., M.M. University, Mullana, Ambala (Haryana) - India) ** (Department of Mechanical Engg., S.U.S.C.E.T., Tangori, Mohali (Punjab) - India)Abstract: As we know in actual machining there areThe AISI H13, a chromium based hot work tool many factors which affect the surface roughness andSteel has a wide variety of applications in material removal rates i.e. cutting conditions, toolaluminum casting and extrusion dies, forging variables and work piece variables. Cuttingdies, hot nut tools, hot header dies, extrusion conditions includes speed, feed and depth of cut. Themandrels, plastic molds, cores, die holder blocks, tool variables includes tool material, nose radius,hot press dies and specially hot work punches etc. rake angle, cutting edge geometry, tool vibration,In this study, the optimization of two response tool overhang, tool point angle etc. The work pieceparameters (Surface roughness and Material variables include hardness and mechanicalRemoval Rate) by three machining parameters properties of the material. It is very difficult to take(cutting speed, feed rate and depth of cut) is all the parameters that control the responseinvestigated in high speed turning of H13 in dry parameters for a particular manufacturing process. Inconditions. Taguchi’s L’18 orthogonal array and a turning operation, it is very difficult to select theanalysis of variance (ANOVA) are used for cutting parameters to achieve the high surface finishindividual optimization. The simultaneous with optimal material removal rate. This studyoptimization is done by Grey Relational Analysis would help the operator to select the cuttingapproach. The different levels of all machining parameters.parameters are used and experiments are done The hot work tool steels have the ability toon HMT STALLION-100 HS CNC lathe resist softening under hot working conditions andmachine. The optimum condition for combined after numerous exposures to elevated operatingeffects was found V5-F2-D3 and the optimal temperatures. Hot work material used for the presentvalue of the surface roughness (Ra) comes out to study is H13 steel. The nominal chemicalbe 1.0828 (μm) and of MRR is 554.0.4 (mm³/sec). compositions are {Chromium (5%), Vanadium (1%),The optimum results are also verified with the Molybdenum (1.5%), Carbon (0.4%), Maganesehelp of confirmation experiments. (0.35%)}. This H13 tool steel is suitable for forging dies, forging die inserts, hot gripper dies, hot nutKeywords: CNC Turning, Optimization, ANOVA, tools, hot header dies, brass forging and pressingMRR, Surface Roughness, Taguchi Method, Grey dies, aluminum base dies, aluminum casting andRelational Analysis, GRG. extrusion dies, zinc die casting dies, extrusion mandrels, plastic molds, cores, die holder blocks, hot1. Introduction: press dies and hot work punches etc. Quality plays a major role in today‟s This paper is about experimentallymanufacturing market. From Customer‟s viewpoint investigating and optimizing the machiningquality is very important because the quality of parameters for Material Removal Rate (MRR) andproducts affects the degree of satisfaction of the Surface Roughness in CNC turning by Taguchiconsumers during usage of the product. It also method and grey relational analysis approach..improves the goodwill of the company. Taguchi‟s orthogonal arrays are highly fractional High speed turning operation is done on designs, used to estimate main effects using veryCNC lathe. The quality of the surface plays a very few experimental runs. These designs are not onlyimportant role in the performance of dry turning applicable for two level factorial experiments, butbecause a good quality turned surface surely also can investigate main effects when factors haveimproves fatigue strength, corrosion resistance and more than two levels. Designs are also available tocreep life. Surface roughness also affects on some investigate main effects for some mixed levelfunctional attributes of parts, such as, contact experiments where the factors included do not havecausing surface friction, wearing, light reflection, the same number of levels. For example, a four-levelability of distributing and also holding a lubricant, full factorial design with five factors requires 1024load bearing capacity, coating and resisting fatigue. runs while the Taguchi orthogonal array reduces the required number of runs to 16 only. 1594 | P a g e
  • 2. Pankaj Sharma, Kamaljeet Bhambri / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 5, September- October 2012, pp.1594-1602 Akhyar, G. et al. (2008) [1] has used the studied with minimum possible number ofapplication of taguchi method in optimization of experiments. Using Taguchi method, Appropriatecutting parameters for surface roughness in turning Orthogonal Array has been chosen and experimentsTi-6%Al-4%V extra low interstitial with various tool have been performed as per the set of experimentsgrades coated and uncoated cemented carbide tools designed in the orthogonal array. Signal to Noiseunder dry cutting condition and high cutting speed. ratios are also calculated for analyzing the effect ofThe analysis of results show that the optimal machining parameters more accurately. The resultscombination of parameters are at cutting speed of 75 of the experimentation are analyzed analytically andm/min, feed rate of 0.15 mm/min, depth of cut of graphically as well. ANOVA is used to determine0.10 mm and tool grade of KC9225. the percentage contribution of all factors upon each Chakradhar, D. and Venu Gopal, A. (2011) response individually.[3] has done the multi objective optimization ofelectrochemical machining of EN-31 steel by grey 3. Taguchi Method:relational analysis. The process parameters Traditional experimental design methodsconsidered are electrolyte concentration, feed rate are very complicated and difficult to use.and applied voltage and are optimized with Additionally, these methods also require a largeconsiderations of multiple performance number of experiments when the number of processcharacteristics including material removal rate, over parameters increases. In order to minimize thecut, cylindricity error and surface roughness. With number of tests required, Taguchi experimentalthe help of Analysis of variance (ANOVA) it was design method, a powerful tool for designing high-observed that feed rate is the significant process quality system, was developed by Taguchi. Taguchiparameter that affects the ECM robustness. method [1, 11, 18] uses a design of orthogonal Hassan, K. et al. (2012) [11] has done the arrays to study the entire parameter space with smallexperimental investigation of material removal rate number of experiments only. Taguchi recommends(MRR) in cnc turning of C34000 using taguchi analyzing the mean response for each run in themethod using L‟27 array. When the MRR is array, and he also suggests to analyze variation usingoptimized alone the MRR comes out to be 8.91. The an appropriately chosen signal-to-noise ratio (S/N).optimum levels of process parameters for There are 2 Signal-to-Noise ratios of commonsimultaneous optimization of MRR have been interest for optimization of static problems used inidentified. Optimal results were verified through present study as are:confirmation experiments. It was concluded thatMRR is mainly affected by cutting speed and feed (I) SMALLER-THE-BETTER:rate. Jaharah, A.G. et al (2009) [13] has studiedthe effect of uncoated carbide tool geometries inturning AISI 1045. This paper presents theapplication of Finite element method (FEM) insimulating the effect of cutting tool geometries on (II) LARGER-THE-BETTER:the effective stress and temperature increased inturning. The tool geometries studied were variousrake (α) and clearance (β) in the different ranges.The minimum effective stress of 1700MPa isachieved using rake and clearance angles of 5° and5° respectively with cutting speed of 300mm/min, Where, ƞ - Signal to Noise (S/N) Ratio,and feed rate of 0.25mm/rev. Yi - ith observed value of the response, Ojel, T. et al. (2005) [23] has studied the n - Number of observations in a trial,effects of cutting edge geometry, work piece y - Average of observed values (responses)hardness, feed rate and cutting speed on surface s - Variance.roughness and resultant forces in the finish hardturning of AISI H13 steel. Cubic Boron Nitrite Regardless of the category of theinserts with two distinct edge preparations performance characteristics, the higher S/N ratio(chamfered and honed) and through hardened AISI corresponds to a better performance. Therefore, theH13 steel bars were used. The honed Edge geometry optimal level of the process parameters is the leveland lower work piece surface hardness resulted in with the highest S/N value. The statistical analysis ofbetter surface roughness. the data is performed by analysis of variance (ANOVA) [3, 12] to study the contribution of the2. Design of Experiment: various factors and interactions and to explore the Experiments were designed using Taguchi effects of each process on the observed values.method so that effect of all the parameters could be 1595 | P a g e
  • 3. Pankaj Sharma, Kamaljeet Bhambri / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 5, September- October 2012, pp.1594-1602 4. Experimental Plan and details: which design parameter significantly affects the In this study, three machining parameters surface roughness. Based on the ANOVA, the were selected with different levels as given in Table relative importance of the machining parameters 1. The experimental design was according to an with respect to material removal rate and surface L‟18 array based on Taguchi method. A set of roughness was investigated to determine the experiments designed and conducted to investigate optimum combination of the machining parameters. the relation between the process parameters and The Material Removal Rate calculations response factor. Minitab 16 software is used for and experimental results of the surface roughness for optimization and graphical analysis of obtained data. turning of H13 with different turning parameters are shown in Table 3. Table 1: Turning parameters and levels Table 3: Design of experiment and calculations Levels CS F DOC (m/sec) (mm/rev) (mm) S. Weight Weight Machining Means of 1 150 0.1 0.5 No. before after Time MRR turning turning (sec.) (mm³/sec) 2 180 0.2 1 (gm) (gm) 3 210 0.3 1.5 1 454.00 434.70 7.3 304.99 4 240 2 434.70 421.62 3.9 424.54 5 270 3 421.62 414.97 2.76 334.66 6 300 4 456.00 437.05 6.1 319.12 5 437.05 423.98 3.33 496.83 The work material selected for this 6 423.98 417.98 2.38 393.24 experiment is H13 of Ø 32 mm, length 70 mm in the present study. The turning operation is performed in 7 455.00 436.5 5.36 535.71 3 steps of 17 mm length each over the total length of 8 436.5 424.1 2.93 436.90 varying depth of cut. The chemical composition of 9 424.1 417.9 2.12 370.19 H13 sample can be seen in Tables 2. 10 460.00 441.25 4.75 499.67 11 441.25 428.63 2.63 403.48 Table 2: Chemical composition of H13 sample 12 428.63 422.51 1.92 607.40Ele- C Cr V Mo SI S P 13 450.00 431.51 4.28 767.37ments 14 431.51 418.84 2.09 546.85% 0.36 4.9 0.9 1.4 1.04 0.03 0.03 15 418.84 412.6 1.76 448.79 16 455.00 436.18 3.9 610.84 The turning tests were carried out to 17 436.18 423.51 2.2 460.02 determine the Material Removal Rate and Surface 18 423.51 417.55 1.64 728.99 Roughness under various turning parameters. A HMT STALLION-100 HS CNC lathe machine is Table 4: Design of experiment and calculations used for experimentation. Ex. CS F D MRR Ra Roughness is measured using stylus type No. (m/ ( mm/ (mm (mm³/ (μm) surface roughness tester „Surftest SJ-201‟ made of min) rev) ) sec) Mitutoyo, Japan. The cut-off length (λ) was chosen 1 150 0.1 0.5 304.99 0.54 as 0.8 for each roughness measurement. An average 2 150 0.2 1.0 424.54 0.96 of 5 measurements of the surface roughness was 3 150 0.3 1.5 334.66 1.29 taken to use in the multi-criteria optimization. 4 180 0.1 0.5 319.12 0.60 The Material Removal Rate, MRR (mm³/ 5 180 0.2 1.0 496.83 0.96 min) was calculated using formula: 6 180 0.3 1.5 393.24 1.09 7 210 0.1 1.0 535.71 0.48 8 210 0.2 1.5 436.90 1.10 9 210 0.3 0.5 370.19 2.19 Where, Wi = Initial weight of work piece in gm 10 240 0.1 1.5 499.67 1.08 Wf= Final weight of work piece in gm 11 240 0.2 0.5 403.48 1.03 t = Machining time in seconds 12 240 0.3 1.0 607.40 2.13 ρs= Density of mild steel 13 270 0.1 1.0 767.37 1.02 = (7.9 x 10ˉ³ gm/mm³). 14 270 0.2 1.5 546.85 1.06 15 270 0.3 0.5 448.79 2.06 5. Experimentation and Calculations: 16 300 0.1 1.5 610.84 0.90 In high speed turning operation, surface 17 300 0.2 0.5 460.02 1.02 roughness is an important criterion. The purpose of 18 300 0.3 1.0 728.99 2.14 the analysis of variance (ANOVA) is to investigate 1596 | P a g e
  • 4. Pankaj Sharma, Kamaljeet Bhambri / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 5, September- October 2012, pp.1594-1602Table 5: ANOVA Table for means of MRR 6. Grey Relational Analysis:V D SS MS F P C In the Grey relational analysis [3, 25] the F quality characteristics are first normalized, rangingC 5 14745 2949 25.07 0.001 49.91 from zero to one. This process is known as GreyS 0 0 *** Relational Generation. Then the Grey RelationalF 2 6078 3039 2.58 0.136 2.06* Coefficient based on normalized experimental dataD 2 13247 6623 56.31 0.002 44.84 is calculated to represent the correlation between the 8 9 ** desired and the actual experimental data. ThenE 8 9410 1176 3.19 overall Grey Relational Grade (GRG) is determinedT 17 29541 100 by averaging the Grey relational coefficient 6 corresponding to selected responses. The overall performance characteristic ofTable 6: Response Table for MRR the multiple response process depends on theLevel CS F DOC calculated GRG. This Grey relational approach 1 354.7 506.3 384.4 converts a multiple response process optimization 2 403.1 461.4 593.5 problem into a single response optimization 3 447.6 480.5 470.4 problem. The optimal parametric combination is 4 503.5 then evaluated, which would result in the highest 5 587.7 Grey relational grade. The optimal factor setting for 6 600.0 maximizing the overall Grey relational grade can be performed using the Taguchi method.Delta 245.2 44.8 209.0 In Grey relational generation, theRank 1 3 2 normalized MRR should follow the larger-the-better (LB) criterion, which can be expressed as: Table 7: ANOVA Table for means of Surface Roughness xj(k) = yi(k) – min yi(k)V D SS MS F P C max yi(k) – min yi(k) FC 5 0.8333 0.166 2.21 0.153 16.20 The normalized Ra should follow the smaller-the-S 66 ** better (SB) criterion which can be expressed as:F 2 3.5817 1.790 23.74 0.001 69.61 87 *** xj(k) = max yi(k) – yi(k)D 2 0.1265 0.063 0.84 0.467 2.46* max yi(k) – min yi(k) 27E 8 0.6036 0.075 11.73 where, xi(k) and xj(k) are the value after Grey 45 Relational Generation for LB and SB criteria. MaxT 17 5.1452 100 yi(k) is the largest value of yi(k) for kth response and min yi(k) is the minimum value of yi(k) for the kthTable 8: Response Table for Surface Roughness response. Level CS F DOC The Grey relational coefficient ξi(k) can be 1 0.9300 0.7700 1.2400 calculated as: 2 0.8833 1.0217 1.2817 ξi(k) = Δmin – Ψ Δmax 3 1.2567 1.8167 1.0867 Δ oi(k) + Ψ Δmax 4 1.4133 5 1.3800 And Δ oi = ║ xo(k) – xi(k) ║ 6 1.3533 Delta 0.5300 1.0467 0.1950 Where Δ oi is the difference between absolute value Rank 2 1 3 xo(k) and xi(k). Ψ is the distinguishing coefficient 0 ≤ Ψ ≥ 1. Δ min and Δ max are the minimum andWhere, maximum value among the Δ oi for correspondingV-Variable, CS-Cutting Speed, kth response.F-Feed Rate, D-Depth Of Cut,SR-Surface Roughness, E-Error, Now the Grey Relational Grade (GRG) can beT-Total, DF-Degree of Freedom, calculated as :SS-Sum of Squares, MS-Mean of Squares,F-a statistical parameter, P-Percentage, γi =C-% Contribution. Where n is the number of process responses.Here *** & ** represents most significant andsignificant parameters and * as less significant. 1597 | P a g e
  • 5. Pankaj Sharma, Kamaljeet Bhambri / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 5, September- October 2012, pp.1594-1602 The higher value of the GRG corresponds to a Where, relational degree between the Reference Sequence GRGC- Grey Relational Generation Calculation, xo(k) and the given sequence xi(k). The Reference RSDC- Reference Sequence Definition Sequence xo(k) represents the best process sequence. Calculation, Therefore, a higher GRG means that the GRCC- Grey Relational Coefficient Calculation corresponding parameter combination is closer to the optimal. The mean response for the GRG and the Table 11: Grey Relational Grade (GRG) main effect plot of the GRG are very important calculation because the optimal process condition can be S. No. GRG Rank evaluated from this plot. Mean S/N Ratio 1 0.468 -6.5950 12 Table 9: S/N Ratio Calculation for MRR and Ra 2 0.522 -5.6465 11 S. No. Mean Values S/N Ratios 3 0.423 -7.4731 14 Xo MRR Ra MRR Ra 4 0.630 -4.0131 4 1 304.99 0.54 49.6857 5.352 5 0.551 -5.1769 7 2 424.54 0.96 52.5584 0.355 6 0.462 -6.7071 13 3 334.66 1.29 50.4921 -2.212 7 0.706 -3.0239 3 4 319.12 0.60 50.0791 4.437 8 0.539 -5.3682 9 5 496.83 0.96 53.9242 0.355 9 0.351 -9.0938 18 6 393.24 1.09 51.8932 -0.749 10 0.526 -5.5802 10 7 535.71 0.48 54.5786 6.375 11 0.600 -4.4369 5 8 436.90 1.10 52.8076 -0.828 12 0.365 -8.7541 17 9 370.19 2.19 51.3685 -6.809 13 0.563 -4.9898 6 10 499.67 1.08 53.9737 -0.668 14 0.798 -1.9599 2 11 403.48 1.03 52.1164 -0.257 15 0.386 -8.2682 15 12 607.40 2.13 55.6695 -6.568 16 0.547 -5.2402 8 13 767.37 1.02 57.7001 -0.172 17 0.801 -1.9273 1 14 546.85 1.06 54.7574 -0.506 18 0.385 -8.2907 16 15 448.79 2.06 53.0409 -6.277 16 610.84 0.90 55.7185 0.915 Table 12: ANOVA Table for means of GRG 17 460.02 1.02 53.2555 -0.172 V D SS MS F P C 18 728.99 2.14 57.2544 -6.608 F Max. 304.99 0.48 49.6857 -6.809 C 5 0.0293 0.0058 0.50 0.772 9.35** Min. 767.37 2.19 57.7001 6.375 S 08 62 F 2 0.1860 0.0930 7.86 0.013 59.31 Table 10: Grey Relational Analysis Calculations 55 27 ***S. GRGC RSDC GRCC D 2 0.0036 0.0018 0.15 0.860 1.16*N. MR Ra MR Ra MR Ra 35 17 R R R E 8 0.0946 0.0118 30.18xo 1.000 1.000 1.000 1.000 1.000 1.000 84 361 0.064 0.965 0.936 0.035 0.348 0.588 T 17 0.3136 1002 0.259 0.719 0.741 0.281 0.403 0.640 823 0.000 0.526 1.000 0.474 0.333 0.5134 0.191 0.930 0.809 0.070 0.382 0.877 Table 13: ANOVA Table for S/N Ratio of GRG5 0.415 0.719 0.585 0.281 0.461 0.640 V D SS MS F P C6 0.031 0.643 0.969 0.357 0.340 0.583 F7 0.285 1.000 0.715 0.000 0.412 1.000 C 5 5.821 1.164 0.48 0.780 7.278 0.498 0.637 0.502 0.363 0.499 0.579 S 2 **9 0.141 0.000 0.859 1.000 0.368 0.333 F 2 53.90 26.95 11.19 0.005 67.3310 0.421 0.649 0.579 0.351 0.463 0.588 1 07 ***11 0.654 0.678 0.346 0.322 0.591 0.608 D 2 1.058 0.529 0.22 0.808 1.3212 0.213 0.035 0.787 0.965 0.389 0.341 0 *13 0.523 0.684 0.477 0.316 0.512 0.613 E 8 19.27 2.409 24.0814 1.000 0.660 0.000 0.340 1.000 0.595 3 115 0.311 0.076 0.689 0.924 0.421 0.351 T 17 80.05 10016 0.321 0.754 0.679 0.246 0.424 0.670 317 0.994 0.684 0.006 0.316 0.988 0.61318 0.335 0.029 0.665 0.971 0.429 0.340 1598 | P a g e
  • 6. Pankaj Sharma, Kamaljeet Bhambri / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 5, September- October 2012, pp.1594-1602 Table 14: Response Table for means of GRG Residual Plots for MeansLevel CS F DOC Normal Probability Plot Versus Fits1 0.4710 0.5733 0.5393 99 0.102 0.5477 0.6352 0.5153 90 0.05 Residual Percent3 0.5320 0.3953 0.5492 50 0.004 0.4970 10 -0.055 0.5823 1 -0.106 0.5777 -0.1 0.0 0.1 0.4 0.5 0.6 0.7 0.8 Residual Fitted ValueDelta 0.1113 0.2398Rank 2 1 3 Histogram Versus Order 4 0.10 Table 15; Response Table for S/N Ratio of GRG 3 0.05 Frequency ResidualLevel CS F DOC 2 0.001 -6.572 -4.907 -5.722 1 -0.052 -5.299 -4.086 -5.980 -0.10 03 -5.829 -8.098 -5.388 -0.10 -0.05 0.00 0.05 0.10 2 4 6 8 10 12 14 16 18 Residual Observation Order4 -6.2575 -5.073 Fig 3: Residual plot for means of GRG6 -5.153Delta 1.499 4.012 0.592 Interaction Plot for GRGRank 2 1 3 Data Means 0.1 0.2 0.3 0.8 cs Main Effects Plot for Means 150 180 Data Means 0.6 210 cs f cs 240 270 300 0.6 0.4 0.8 f 0.1 0.5 0.2 0.6 0.3 Mean of Means f 0.4 150 180 210 240 270 300 0.1 0.2 0.3 0.4 doc 0.8 doc 0.5 0.6 1.0 0.6 1.5 doc 0.5 0.4 0.4 150 180 210 240 270 300 0.5 1.0 1.5 0.5 1.0 1.5 Fig 4: Interaction plot for means of GRG Fig 1: Main effect plot for means of GRG 7. Determining Optimum Condition: Main Effects Plot for SN ratios Both the response and S/N ratio are used to Data Means derive the optimum conditions. Since for quality cs f characteristic, Grey Relational Grade larger the -4 -5 better approach is desirable, the largest is the ideal -6 level for a parameter. The S/N ratio is always highest at the optimum condition. The graphs of Mean of SN ratios -7 -8 Figures 2 and 3 are used to determine the optimum 150 180 210 240 270 300 0.1 0.2 0.3 process parameters combination. The optimum doc combination is therefore V5-F2-D3. -4 -5 7.1 Predictive Equation and Verification: -6 The predicted values of GRG, MRR and Ra -7 at the optimal levels are calculated by using the -8 relation: 0.5 1.0 1.5 Signal-to-noise: Larger is better Fig2: Main effect plot for S/N Ratio of GRG 1599 | P a g e
  • 7. Pankaj Sharma, Kamaljeet Bhambri / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 5, September- October 2012, pp.1594-1602Where, The purpose of the ANOVA in this study is toñ - Predicted response value after optimization, identify the important turning parameters in - Total mean value of quality characteristic, prediction of Material Removal Rate and Surface Roughness. Some important results come from - Mean value of quality characteristic at ANOVA and plots are given as below.optimum level of each parameter ando - Number of main machining parameters that Table 16 shown below shows that optimal values ofaffect the response parameter. surface roughness and material removal rate that lie between the optimal ranges.Applying this relation, predicted values of GRG,MRR and Ra at the optimum conditions are Table 16: Optimal values of machining andcalculated as: response parameters CP OV OL POV EOV OR1. ñGRG = 0.5346 + [ (0.5823 - 0.5346) + (0.6352 – 0.5346) + (0.5492 - 0.5346) ] CS 270 V5- MRR= MRR= 544.20 = 0.6975 554.04 544.20 <MRR> F 0.2 F2- 554.042. ñMRR = 482.73 + [ (587.7 – 482.73) + (461.4 – 482.73) + (470.4 – 482.73) ] D 1.5 D3 Ra= Ra= 1.06 = 554.04 mm³/sec 1.0828 1.06 < Ra > 1.08283. ñRa = 1.2028 + [ (1.3800 – 1.2028) + (1.0217 – 1.2028) + (1.0867 – 1.2028 ] Where, = 1.0828 µm CP-Cutting Parameters OV-Optimal Values of Parameters The robustness of this parameter OL-Optimum Levels of Parametersoptimization is verified experimentally. This POV-Predicted Optimum valuerequires the confirmation run at the predicted EOV-Experimental Optimum Valueoptimum conditions. The experiment is conducted OR-Optimum Range of MRR and Surfacetwice at the predicted optimum conditions. RoughnessVerifications: 9. Conclusion:1. For Material Removal Rate (MRR): In this study, the Grey relational based The calculated value of MRR at the Taguchi method was applied for the multipleoptimum condition (V5-F2-D3) is 554.04 mm³/sec. performance characteristics of turning operations. AThe error in the predicted optimum value (554.04) grey relational analysis of the Material removal rateand the calculated value (544.20) is only 1.77%. and the surface roughness obtained from the Taguchi method reduced from the multiple performance2. For Surface Roughness (Ra): characteristics to a single performance characteristic The average of two measured values (1.04, which is called the grey relational grade. Therefore,1.08) of the response at the optimum condition (V5- the optimization of the complicated multipleF2-D3) is 1.06 μm. The error in the predicted performance characteristics of the processes can beoptimum value (1.0828) and experimental value greatly simplified using the Grey relational based(1.06) is only 2.11%. Taguchi method. It is also shown that the performance characteristics of the turning Hence, so good agreement between the operations, such as the material removal rate and theactual and the predicted results is observed. Since surface roughness are greatly enhanced by using thisthe percentage error is less than 5%, it confirms method.excellent reproducibility of the results. The resultsshow that using the optimal parameter setting (V5-F2-D3) a higher material removal rate is achievedwith lower surface roughness.8. Results: The effect of three machining parametersi.e. Cutting speed, feed rate and depth of cut andtheir interactions are evaluated using ANOVA andwith the help of MINITAB 16 statistical software. 1600 | P a g e
  • 8. Pankaj Sharma, Kamaljeet Bhambri / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 5, September- October 2012, pp.1594-1602References: [11] Hassan, K. et al. (2012) “Experimental[1] Akhyar, G. et al. (2008), “Application of investigation of Material removal rate in Taguchi method in the optimization of CNC turning using Taguchi method” turning parameters for surface roughness”, International Journal of Engineering International Journal of Science Research and application, vol. 2, no. 2, pp. Engineering and Technology, vol.1, no.3, 1581-1590 pp. 60-66. [12] Hascalik, A. and Caydas, U. (2008),[2] Adesta, E.Y.T. et al. (2009), “Tool wear “Optimization of Turning parameters for and surface finish investigation in high surface roughness and tool life based on the speed turning using cermet insert by taguchi method” International Journal of applying negative rake angles” European advance manufacturing technology, vol. 38, Journal of Scientific Research, vol. 38, no. pp. 38:896-903. 2, pp. 180-188. [13] Jaharah, A.G. et al. (2009), “The effect of[3] Chakradhar, D. and Venu Gopal, A. (2011), uncoated carbide tool geometries in turning “Multi-objective optimization of AISI 1045 using finite element analysis” electrochemical machining of en31 steel by European Journal of Scientific research, grey relational analysis”, International vol. 28, no.2, pp. 271-277. Journal of Modeling and optimization, vol. [14] Jaharah, A.G. et al. (2009), “Surface 1, no.2, pp. 113-117. integrity of AISI H13 tool steel in end[4] David Pye et al. “Chromiun Steel Article milling process” International Journal of for AISI H13 Hot Work Tool Steel by Pye Mechanical and Materials Engineering Metallurgical consulting” at (IJMME), vol. 4, no. 1, pp. 88 -92. http://www.heattreatonline.com/ [15] Kirby, E.D. (2006), “A parameter design ChromiumSteelArticle.pdf study in a turning operation using the[5] Datta, R. and Majumdar, A. (2010), taguchi method”, The technology “Optimization of turning process interface/Fall 2006, pp. 1-14. parameters using multi-objective [16] Lan, T.S. et al. (2010), “Fuzzy Linguistic evolutionary algorithm” Institute of optimization on surface roughness for cnc electrical and electronics engineers (IEEE), turning” Hindawi publishing corporation vol. 2010, article id 978-1-4244-8126-2/10. Mathematical problems in engineering, vol.[6] Dhanabalan, S. et al. (2011), “Optimization 10, article id 572506. of EDM process parameters with multiple [17] Lan, T.S. et al. (2010), “Tool wear performance characteristics for titanium optimization for general cnc turning using grades” European Journal of Scientific fuzzy deduction” Scientific research research, vol. 68, no. 3, pp. 297-305. engineering, vol. 2, pp. 1019-1025.[7] Dhavamani, C. and Alwarsamy, T. (2011), [18] Lan, T.S. (2011), “Fuzzy parametric “Reviw on optimization of machining deduction for material removal rate operation” International Journal of optimization” Journal of mathematics and academic research, vol. 3, no.3, pp. 4776- statistics, vol.2011, 7-(1), pp.51-56 4785. [19] Lalwani, D.I. et al. (2008), “Experimental[8] Fnides, B. et al. (2011), “Application of Investigation of cutting parameters response surface methodology for influencing on cutting forces and Surface determining cutting force model in turning roughness in finish hard turning of MDN hardened AISI H11 hot work tool steel” 250 steel”, Journal of Materials processing Indian Academy of Sciences, vol. 36, part technology, vol. 206, pp. 167-179 1, pp. 109–123. [20] Mahapatra, S.S. et al. (2006), “Parametric[9] Gonzalez, L.W.H. et al. (2011), “Study of analysis and optimization of cutting performance of high speed turning of parameters for turning operations based on AISI1045 using the volumetric dimension taguchi method” Proceedings of the of cutting tool life” International journal of International Conference on Global advances in machining and forming Manufacturing and Innovation, - July 27- operations, vol. 3, no.1, pp.19-32. 29/2006, pp. 1-9.[10] Ghani, M.U. et al. (2007), “An [21] Modh, N.R. et al. (2009), “An experimental investigation of heat partition and tool wear investigation to optimize the process in hard turning of H13 tool steel with CBN parameters of AISI 52100 steel in hot cutting tools” International Journal of machining” International Journal of Advance Manufacturing and Technology, Engineering Research and Applications, article id 170-007-1282-7. vol. 1, issue 3, pp.483-489. 1601 | P a g e
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