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Improvement in surface quality with high production rate using taguchi method and gra

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Improvement in surface quality with high production rate using taguchi method and gra

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Improvement in surface quality with high production rate using taguchi method and gra

  1. 1. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 10, October (2014), pp. 138-147 © IAEME 138 IMPROVEMENT IN SURFACE QUALITY WITH HIGH PRODUCTION RATE USING TAGUCHI METHOD & GRA NEERAJ KUMAR1 , VIJAY KUMAR2 1 Assistant Professor, Dept. of Mechanical Engg., SBTC Jaipur, India, 2 Assistant Professor, Dept. of Mechanical Engg., VIER, Vadodara, India ABSTRACT Metal matrix composites possess significantly improved properties including high specific strength, damping capacity, specific modulus and wear resistance compared to unreinforced alloys. There has been an increasing interest in composites containing low density and low cost reinforcements with good properties. Among these reinforcements SiC is found to be chemically compatible with aluminum forming a sufficiently strong bond with the matrix. There are several methods of manufacture of MMCs. Of these, the stir casting method is very popular due to its unique mixing property. In the present investigation an attempt is made to evaluate the effect of certain cutting variables on surface roughness and material removal rate in end milling of aluminium alloy (Al 6061) – SiC metal matrix composites. The experiments are conducted based on four factors, three levels. The results are analyzed by Taguchi design method. The equation to the response surface is developed by software package MINITAB-14. The goodness of fit is examined using the Analysis of Variance (ANOVA). The graphs of S/N ratios shows that the best surface finish is associated with the lowest level of SiC percentage in the metal matrix followed by the lowest level of speed, medium level of feed and medium level of depth of cut. Also maximum material removal rate is obtained at highest level of depth of cut followed by medium level of feed rate and SiC percentage and low level of speed. Gray relation results shows the best parameters for combination of high surface finish and high material removal rate are medium level of SiC followed by low level of speed medium level for feed rate and high level of depth of cut. Key words: ANOVA, Composite Material, Taguchi Method, S/N Ratio. INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING AND TECHNOLOGY (IJMET) ISSN 0976 – 6340 (Print) ISSN 0976 – 6359 (Online) Volume 5, Issue 10, October (2014), pp. 138-147 © IAEME: www.iaeme.com/IJMET.asp Journal Impact Factor (2014): 7.5377 (Calculated by GISI) www.jifactor.com IJMET © I A E M E
  2. 2. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 10, October (2014), pp. 138-147 © IAEME 139 1. INTRODUCTION Aim of end milling process is to removing material by two continuous motions. Those of the tool and work pieces basically the tool has rotating motion (spindle speed) and the work pieces are linear ones. For this study SiC percentage, spindle speed, feed rate and depth of cut taken as input parameters and surface roughness and material removal rate as response parameters. J.A. Ghani et al. [1] applied Taguchi method to optimize cutting parameters in end milling when machining hardened steel AISI H13 with TiN coated P10 carbide insert tool. They take speed, feed rate and depth of cut as cutting parameter and use an orthogonal array, signal-to-noise (S/N) ratio and analysis of variance (ANOVA) to analyze the effect of these milling parameters. They concluded that the optimal combination for low resultant cutting force and good surface finish are high cutting speed, low feed rate and low depth of cut. E. Kılıckap et al. [2] Studied on tool wear and surface roughness in machining of homogenized SiCp reinforced aluminium metal matrix composite. They select SiCp aluminium MMC as working material and Two types of K10 cutting tool (uncoated and TiN-coated). They show that in dry turning condition, tool wear is mainly affected by cutting speed and tool wear is proportional to cutting speed. Tool wear was lower when coated cutting tool was used in comparison to uncoated one. According to their analysis higher cutting speeds and lower feed rates produced better surface quality. T.Tamizharasan et al. [3] performed experiment for effects of geometrical parameters of cutting tool insert and analysis of output responses such as surface roughness and Material Removal Rate (MRR) during machining AISI 1045 steel. They use Analysis of Variance (ANOVA) to study the contribution of individual parameter on the output quality characteristics. Amit Joshi et al. [4] performed experiment on CNC Vertical End Milling machine. They take spindle speed, depth of cut, feed rate as cutting parameter and Material Removal Rate as response. Experimental plan is performed by a Standard Orthogonal Array. The results of analysis of variance (ANOVA) indicate that the proposed mathematical model can be adequately describing the performance within the limit of factors being studied. The optimal set of process parameters has also been predicted to maximize the MRR. They concluded that High feed rate, high depth of cut and high spindle speed lead to higher value of resultant Material removal rate for the specific test range, The depth of cut is the most dominant factor for material removal rate out of others two factors i.e., spindle speed & feed rate. 2. MATERIAL SELECTION The work piece material chosen for this experimental study is aluminium silicon carbide composite material. The main properties of composite materials are higher strength-to-density ratios, higher stiffness-to-density ratios, better fatigue resistance, better elevated temperature properties, better wear resistance, reduced density, higher strength and lower expansion ratio [5]. Among all the available manufacturing processes for composite casting stir casting is generally accepted as a particularly promising route because of its simplicity, flexibility and applicability to large quantity production [6]. In a stir casting process, the reinforcing particles are distributed into molten matrix by mechanical stirring. Stir casting is suitable for manufacturing composites with up to 30% volume fractions of reinforcement [7]. So for this research material is casted by stirring casting process. The casting setup for the casting is as shown in fig 1 & 2 below.
  3. 3. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 10, October (2014), pp. 138-147 © IAEME 140 Fig.1: Stir casting setup Fig.2: Stirrer in pit furnace Table 1: Chemical Composition of Al 6061 alloy Component Al Mg Si Fe Cu Zn Ti Mn Cr Oth. Amount (%) 96.35 1.2 0.8 0.7 0.15 0.25 0.15 0.15 0.35 0.05 3. EXPERIMANTAL SETUP AND METHODOLOGY The experiments are conducted on a CNC Vertical machine centre as shown in fig 3. Which have Spindle speed 8000 rpm, cutting feed rate 5-10000 mm/min, and main motor power of 7.5 kW, Table dimension 700*430 mm, Maximum loading capacity 500 kg and Maximum tool diameter 125 mm. After performing the machining process, surface roughness values of the machined surfaces are recorded by using a Hommel surface roughness tester as shown in fig 4. Material removal rate is determined by noting the time, length, width and depth of cut of work piece. Fig.3: Cosmos V60 Vertical Machine Centre Fig.4: Hommel surface roughness tester 3.1 TAGUCHI TECHNIQUES Dr. Taguchi of Nippon Telephones and Telegraph Company, Japan has developed a method based on “ORTHOGONAL ARRAY” experiments which gives much reduced “variance” for the experiment with “optimum settings” of control parameters. Thus the combination of Design of Experiments with optimization of control parameters to obtain BEST results is achieved in the Taguchi Method. "Orthogonal Arrays" reduce the number of experiments and Signal-to-Noise ratios (S/N) serve as objective functions for optimization [8].The Taguchi method is best used when there are an intermediate number of variables (3 to 50) control parameter and their levels are as shown in table 2.
  4. 4. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 ISSN 0976 – 6359(Online), Volume 5, Issue 10, October (2014), pp. Table The selection of L27 array is beca The L27 orthogonal array is shown in Table 3. 3.2 ANNOVA METHOD ANOVA is a statistically based, objective de the average performance of groups of items tested. ANOVA helps in formally testing the significance of all main factors and their interactions by comparing the mean square against an estimate of the experimental errors at specific confi 4. RESULTS AND DISCUSSION For this experiment surface roughness and material removal rate are taken as output variable and their values determines by experiment are as shown below Mechanical Engineering and Technology (IJMET), ISSN 0976 6359(Online), Volume 5, Issue 10, October (2014), pp. 138-147 © IAEME 141 Table-2: Control parameters and their levels array is because of its suitability for four factors array is shown in Table 3. Table-3: L27 Orthogonal Array ANOVA is a statistically based, objective decision-making tool for finding the average performance of groups of items tested. ANOVA helps in formally testing the significance of all main factors and their interactions by comparing the mean square against an estimate of the experimental errors at specific confidence levels [10]. RESULTS AND DISCUSSION For this experiment surface roughness and material removal rate are taken as output variable and their values determines by experiment are as shown below. Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), © IAEME use of its suitability for four factors with three Levels [9]. making tool for finding any differences in the average performance of groups of items tested. ANOVA helps in formally testing the significance of all main factors and their interactions by comparing the mean square against an For this experiment surface roughness and material removal rate are taken as output variable
  5. 5. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 ISSN 0976 – 6359(Online), Volume 5, Issue 10, October (2014), pp. 4.1 ANALYSIS OF SURFACE ROUGHNESS The surface roughness value for any product should be low so smaller the better condition is used to determine the Taguchi S/N ratio. S/N ratio ൌ െ10logଵ଴ሺ ଵ ୬ ∑ y୧ ଶ ሻ. Where i=1 to n & n = number of replications The values of S/N ratio for surface roughness are as shown in table 4. Table The optimum level of parameters are decided by calculating average value of S/N ratio corresponding to each level of parameters of surface r Table-5: Response table of S/N ratio From the S/N ratio response identified by selecting the highest difference value from each factor. In this case, the most significant factor that has an effect on surface roughness are SiC % (A) Mechanical Engineering and Technology (IJMET), ISSN 0976 6359(Online), Volume 5, Issue 10, October (2014), pp. 138-147 © IAEME 142 4.1 ANALYSIS OF SURFACE ROUGHNESS value for any product should be low so smaller the better condition is used to determine the Taguchi S/N ratio. n = number of replications /N ratio for surface roughness are as shown in table 4. able-4: S/N Ratio for surface roughness The optimum level of parameters are decided by calculating average value of S/N ratio corresponding to each level of parameters of surface roughness which are as shown in T Response table of S/N ratio for each level of each factor From the S/N ratio response as shown in Table, the best combination of parameters can be identified by selecting the highest difference value from each factor. In this case, the most significant surface roughness are SiC % (A) followed by speed (B) feed rate C) and Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), © IAEME value for any product should be low so smaller the better condition is The optimum level of parameters are decided by calculating average value of S/N ratio oughness which are as shown in Table 5. for each level of each factor , the best combination of parameters can be identified by selecting the highest difference value from each factor. In this case, the most significant speed (B) feed rate C) and
  6. 6. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 ISSN 0976 – 6359(Online), Volume 5, Issue 10, October (2014), pp. depth of cut (D). Hence optimum condition can be represented by A surface roughness main effect plot is dr fig 5. Fig.5 To determining the contribution of parameters on surface roughness analysis of variance is performed and outcomes are tabulated in T Table-6: This results shows that the SiC % spindle speed by 13.67%, feed rate significant parameter contributes to improve surface finish in the rate and depth of cut only have small effects towards surface finish. 4.2 ANALYSIS OF MATERIAL REMOVAL RATE For better productivity the material removal rate should be maximum so Taguchi larger the better condition is used for this analysis S/ Where i=1 to n & n = number of replications Mechanical Engineering and Technology (IJMET), ISSN 0976 6359(Online), Volume 5, Issue 10, October (2014), pp. 138-147 © IAEME 143 depth of cut (D). Hence optimum condition can be represented by A1B1C2D2 surface roughness main effect plot is drawn by using MINITAB14 software which is as shown Fig.5: Main effect plot for S/N ratio To determining the contribution of parameters on surface roughness analysis of variance is and outcomes are tabulated in Table 6. : Analysis of variance for surface roughness This results shows that the SiC % contribution the most by 59.79% and this is followed by %, feed rate 6.73% and depth of cut 3.44%. This proves that SiC% is the most significant parameter contributes to improve surface finish in the process while spindle speed, feed rate and depth of cut only have small effects towards surface finish. 4.2 ANALYSIS OF MATERIAL REMOVAL RATE For better productivity the material removal rate should be maximum so Taguchi larger the d for this analysis. /N ratioሺηሻ ൌ െ10logଵ଴ሺ 1 n ෍ 1 y୧ ଶሻ n = number of replications Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), © IAEME 2.From the response of awn by using MINITAB14 software which is as shown in To determining the contribution of parameters on surface roughness analysis of variance is % and this is followed by %. This proves that SiC% is the most process while spindle speed, feed For better productivity the material removal rate should be maximum so Taguchi larger the
  7. 7. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 ISSN 0976 – 6359(Online), Volume 5, Issue 10, October (2014), pp. The values of S/N ratio for surface roughness are as shown in T Table 7: The optimum level of parameters are decided by calculating average value of S/N ratio corresponding to each level of parameters of surface roughness which are as shown in Table-8: Response table of S/N ratio for each level of each factor From the S/N ratio response as shown in Table, the best combination of parameters can be identified by selecting the highest difference value from each factor. In this case, the most significant factor that has an effect on material removal rate are depth SiC% (A) and speed (B). Hence optimum condition can be represented by A response of surface roughness main effect plot is drawn by using MINITAB14 fig.6. Mechanical Engineering and Technology (IJMET), ISSN 0976 6359(Online), Volume 5, Issue 10, October (2014), pp. 138-147 © IAEME 144 face roughness are as shown in Table 7. : S/N ratio for Material removal rate value The optimum level of parameters are decided by calculating average value of S/N ratio corresponding to each level of parameters of surface roughness which are as shown in Response table of S/N ratio for each level of each factor From the S/N ratio response as shown in Table, the best combination of parameters can be identified by selecting the highest difference value from each factor. In this case, the most significant factor that has an effect on material removal rate are depth of cut (D) followed by fee Hence optimum condition can be represented by A response of surface roughness main effect plot is drawn by using MINITAB14 Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), © IAEME The optimum level of parameters are decided by calculating average value of S/N ratio corresponding to each level of parameters of surface roughness which are as shown in Table 8. Response table of S/N ratio for each level of each factor From the S/N ratio response as shown in Table, the best combination of parameters can be identified by selecting the highest difference value from each factor. In this case, the most significant D) followed by feed rate (C), Hence optimum condition can be represented by A2B1C3D3.From the response of surface roughness main effect plot is drawn by using MINITAB14 software as shown in
  8. 8. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 ISSN 0976 – 6359(Online), Volume 5, Issue 10, October (2014), pp. Fig. To determining the contribution of parameters on material removal rate analysis of variance is performed and outcomes are tabulated in T Table-9: Analysis of variance for This results shows that the by feed rate by 20.89%, SiC percentage most significant parameter contributes to improve rate, SiC percentage and spindle speed, 5. GRAY RELATION ANALYSIS GRA is effective tool for solving the complicated interrelationship among the designated performance characteristics. It also problems. In gray relational analysis the complex multiple response optimization problem can be simplified into optimization of single Mechanical Engineering and Technology (IJMET), ISSN 0976 6359(Online), Volume 5, Issue 10, October (2014), pp. 138-147 © IAEME 145 Fig.6: Main effect plot for S/N ratio of MRR To determining the contribution of parameters on material removal rate analysis of variance and outcomes are tabulated in Table 9 below. Analysis of variance for material removal rate the Depth of cut contribute the most by 72.17 percentage 2.36% and speed 1.69%. This proves that most significant parameter contributes to improve material removal rate in the process while percentage and spindle speed, only have small effects towards material removal rate 5. GRAY RELATION ANALYSIS GRA is effective tool for solving the complicated interrelationship among the designated performance characteristics. It also provides an efficient solution to multi-input and discrete data problems. In gray relational analysis the complex multiple response optimization problem can be simplified into optimization of single response gray relational grade [11]. Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), © IAEME To determining the contribution of parameters on material removal rate analysis of variance material removal rate .17% and this is followed . This proves that depth of cut is the in the process while feed material removal rate. GRA is effective tool for solving the complicated interrelationship among the designated input and discrete data problems. In gray relational analysis the complex multiple response optimization problem can be
  9. 9. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 ISSN 0976 – 6359(Online), Volume 5, Issue 10, October (2014), pp. Fig.7: Graphical representations of steps to determine GRG For a product good surface finish and high material removal rate required hence, equal weights are used here for determining weighted gray relational grades for MRR and roughness. Based on this combination of weights gray relational coefficient of the individual quality characteristics and ranks of the experiments are determined. The gray relational grades found for various experiments are listed in Table Table From Table 10, it is found that input parameters used for experiment number 12 gives optimal result among all input parameters used for experiments. Mechanical Engineering and Technology (IJMET), ISSN 0976 6359(Online), Volume 5, Issue 10, October (2014), pp. 138-147 © IAEME 146 Graphical representations of steps to determine GRG good surface finish and high material removal rate required hence, equal weights are used here for determining weighted gray relational grades for MRR and roughness. on of weights gray relational coefficient of the individual quality characteristics and ranks of the experiments are determined. The gray relational grades found for various experiments are listed in Table 10. Table-10: Gray relational grades for MRR and Ra , it is found that input parameters used for experiment number 12 gives optimal result among all input parameters used for experiments. Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), © IAEME Graphical representations of steps to determine GRG good surface finish and high material removal rate required hence, equal weights are used here for determining weighted gray relational grades for MRR and roughness. on of weights gray relational coefficient of the individual quality characteristics and ranks of the experiments are determined. The gray relational grades found for MRR and Ra , it is found that input parameters used for experiment number 12 gives
  10. 10. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 10, October (2014), pp. 138-147 © IAEME 147 6. CONCLUSION 1. For surface roughness, the best level of input parameters are percentage of SiC5%, speed 2500 rpm, and feed rate 75 mm/min depth of cut 1 mm. 2. The best-input parameters for MRR are percentage of SiC 10%, speed 2500 rpm, feed rate 100 mm/min and depth of cut 1.5 mm. 3. The most significant factor that has an effect on surface roughness are SiC % (59.79%) followed by speed (13.67%), feed rate (6.73%) and depth of cut (3.44%). 4. The most significant factor that has an effect on material removal rate are depth of cut (72.17%) followed by feed rate (20.89%), SiC% (2.36%) and speed (1.69%). 5. Optimum parameters according to GRA are 10% SiC, speed 2500 rpm, feed rate, 100 mm/min depth of cut, 1.5 mm REFERENCE [1] J.A. Ghani, I.A. Choudhury, H.H. Hassan, Application of Taguchi method in the optimization of end milling parameter, Journal of Materials Processing Technology 145 (2004) 84–92. [2] E. Kılıc¸kap, O. C¸ akır , M. Aksoy , A. ˙Inan,Study of tool wear and surface roughness in machining of homogenized SiC-p reinforced aluminum metal matrix composite, Journal of Materials Processing Technology 164–165 (2005) 862–867 [3] T.Tamizharasan, N.Senthil Kumar,Analysis of Surface Roughness and Material Removal Rate in Turning Using Taguchi's Technique, IEEE-International Conference On Advances In Engineering, Science And Management (lCAESM -2012) March 30, 31, 2012 231. [4] Amit Joshi, Pradeep Kothiya, Ruby Pant, Experimental Investigation of Machining Parameters of CNC Milling On MRR By Taguchi Method, International Journal of Applied Engineering Research, ISSN 0973-4562 Vol.7 No.11 (2012) [5] P.K. Rohatgi, JOM 46 (11) (1994) 55–59. [6] M.N.Wahab, A.R. Daud and M. J. Ghazali, preparation and characterization of stir cast- aluminum nitride reinforced aluminum metal matrix composites, International Journal of Mechanical and Materials Engineering (IJMME), Vol. 4, No. 2, 115-117, (2009) [7] PradeepSharma, Gulshan Chauhan, Neeraj Sharma, production of amc by stir casting- an overview, International Journal of Contemporary Practises Vol.2 Issue1. [8] https://www.ee.iitb.ac.in/~apte/CV_PRA_TAGUCHI_INTRO.htm [9] Phillips.J.Ross, Taguchi Techniques for Quality Engineering, Mc Graw Hill Publication, ISBN 0070539588, (1996). [10] Rudolf N. Cardinal, ANOVA in practice and complex ANOVA designs, vol 2, may 2004 [11] Deng, J., Control problems of gray systems, System and Control Letters 1, 1982, pp288-294. [12] K.R. Padmavathi and Dr.R. Ramakrishnan, “Aluminium Metal Matrix Composite with Dual Reinforcement”, International Journal of Mechanical Engineering & Technology (IJMET), Volume 5, Issue 5, 2014, pp. 151 - 156, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359. [13] V.Suresh, Dr. R.Sivasubramanian and R.Maguteeswaran, “Study and Investigation of Analysis of Metal Matrix Composite”, International Journal of Mechanical Engineering & Technology (IJMET), Volume 3, Issue 2, 2012, pp. 171 - 188, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359. [14] Amol D. Sable and Dr. S. D. Deshmukh, “Preparation of Metal-Matrix Composites by Stircasting Method”, International Journal of Mechanical Engineering & Technology (IJMET), Volume 3, Issue 3, 2012, pp. 404 - 411, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359.

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