The goal of modern industry is to manufacture low cost, high quality products in short time. Optimum machining parameters are of great concern in manufacturing environments, where economy of machining operation plays a key role in competitiveness in the market. A number of researchers have dealt with the optimization of machining parameters, considering only turning operations and graphical methods to determine the optimum speed, feed and depth of cut. In this work, work pieces machined by Computer Numerical Control machine centre are evaluated according to Response Surface Methodology with an objective function of obtaining good surface finish using single tool operations. Optimum machining parameters resulting from this work are intended for use by Numerical Control machines in order to improve machining efficiencies, improve quality, and reduce rework and scrap.

Dhas, J.  Edwin Raja

Stalin, R.S.

MAT Journals

    1 Page 1-5 © MAT Journals 2018. All Rights Reserved   Journal of Mechanical and Mechanics Engineering  Volume 4 Issue 2  Modeling Of Metal Cutting Process Using Response Surface Methodology  J.  Edwin Raja Dhas1 and R.S. Stalin 2 1Department of Automobile Engineering Noorul Islam Centre for Higher Education Nagercoil, India. e-mail: edwinrajadhas@rediffmail.com  2Department of Mechanical Engineering Vins Christian College of Engineering, Nagercoil - 629807, Tamil Nadu. India. e-mail: stalin1977@gmail.com  Abstract The goal of modern industry is to manufacture low cost, high quality products in short time. Optimum machining parameters are of great concern in manufacturing environments, where economy of machining operation plays a key role in competitiveness in the market. A number of researchers have dealt with the optimization of machining parameters, considering only turning operations and graphical methods to determine the optimum speed, feed and depth of cut. In this work, work pieces machined by Computer Numerical Control machine centre are evaluated according to Response Surface Methodology with an objective function of obtaining good surface finish using single tool operations. Optimum machining parameters resulting from this work are intended for use by Numerical Control machines in order to improve machining efficiencies, improve quality, and reduce rework and scrap.   Keywords: Statistical Modeling, Metal cutting, cutting parameters  INTRODUCTION Metal cutting is one of the most significant manufacturing processes in material removal. Selection of cutting parameters is an important task to for achieving high cutting performance in a turning operation, it. Measuring surface roughness is a crucial concern for an immense range of industries and applications, from auto component wear to medical implant efficacy, from micro-electro-mechanical systems inspection to semiconductor thin-film uniformity. Many of the major advances in science and industry over the past half century would not have been possible without accurate surface roughness metrology Usually, the desired cutting parameters are determined based on experience, tables or by use of a handbook [1-3] .  However, this does not ensure that the selected cutting parameters have optimal or near optimal cutting performance for a particular machine and environment. To select the cutting parameters properly, several mathematical fashions [4–9] primarily based on statistical regression techniques or neural computing had been constructed to establish the relationship between the slicing overall performance and the slicing parameters. a few other researchers have tried to locate the affects of diverse machining parameters inclusive of reducing speed, feed charge, rake perspective and tool geometry on burrs [10-12] Taguchi method [13,14]is used to determine the desired cutting parameters more efficiency. Response Surface Methodology (RSM) [15, 16] is used to find a set of process variables that produce the desired surface roughness in metal cutting process.     2 Page 1-5 © MAT Journals 2018. All Rights Reserved   Journal of Mechanical and Mechanics Engineering  Volume 4 Issue 2  DEVELOPED METHODOLOGY  Following steps are employed to establish the effect of process parameters on surface roughness in machining process are  1. Identification of cutting parameters  2. Design Matrix using Response surface Methodology  3. Conducting the experiments  4. Developing the mathematical model 5. Conducting the confirmatory tests Identification of cutting parameters. The major parameters which affect the machining operations are identified as spindle speed, depth of cut, fees rate, tool nose radius and material properties [20].   The material properties of the work piece used, AA 6351 Aluminum Alloy is given below in Table 1.   Table 1 Material Properties of used AA 6351 Aluminum Alloy Properties Values Density 2.6-2.8 ×1000 kg/m3 Poisson's Ratio 0.33 Elastic Modulus 70-80 GPa Tensile Strength 250 Mpa Yield Strength  150 Mpa Elongation  20 % Hardness  95  HB500 Shear Strength  200  Mpa Fatigue Strength 90 Mpa  Experimental Design using Response surface Methodology In statistics, response surface methodology explores the relationships between several explanatory variables and one or more response variables. The method was introduced by G. E. P. Box and K. B. Wilson in 1951. The main idea of RSM is to use a sequence of designed experiments to obtain an optimal response. The response surface methodology became composed of an experimental layout to discover an approximate model between composite design to attain a second-order regression version and the experimental factors. desk 2 indicates the crucial composite layout employed in this paintings for 4 elements and 3 ranges.  Conducting the experiments  Thirty experiments were performed under cutting environment. For each experimental trial, a new cutting edge was used. Each trial was repeated twice taking into consideration the cost of inserts, machining, cryogenic cutting environment and high repeatability of CNC machines. Cutting conditions were selected based on some preliminary investigations and industrial survey.  The responses investigated were the surface roughness. Experiment was done using an EXPERT didactic PC lathe an advanced unit of a PC programmable lathe and possesses a range of accessories that makes it usable as a PC-driven lathe, aimed at didactic use and an exclusive property of ALECOP. AA 6351, Aluminum alloy bars (24mm diameter and 60mm length) were used for the experimentation. The average temperature of the environment was maintained at 25 ±3 ◦C during machining. A portable high-performance surface roughness and waviness measuring instrument, characterized by skid less measurement SE1200 was used to measure the Ra value of surface roughness in microns and the responses were recorded. Experimental set up for conducting experiments are shown in Figure 1. The ranges of parameters were spindle speed (X1) from 800 to 1200 rpm, feed rate (X2)  40 to 60 mm/min, depth of cut (X3)  from     3 Page 1-5 © MAT Journals 2018. All Rights Reserved   Journal of Mechanical and Mechanics Engineering  Volume 4 Issue 2  0.30 to 0 .6 mm and tool nose radius (X4) from 0.4 to 0.8 mm.   Fig.1. Experimental set up  Developing the mathematical model Mathematical models are useful for selecting correct process parameters to achieve the desired surface finish. In order to predict the surface roughness using the experimental data, the statistical method of Multiple Regression Analysis is used. Surface roughness (X5), micron = 1.5966 - (0.005X1) + (.0016 X2) - (.2263) – (0.1234 X4). From the above equation, new values of surface roughness are predicted.  Table 2 Design matrix for central composite design Trial Numbers Coded units Response X1 X2 X3 X4 X5 1 -1 -1 -1 -1 1.013 2 +1 -1 -1 -1 1.008 3 -1 1 -1 -1 1.169 4 +1 1 -1 -1 1.148 5 -1 -1 +1 -1 1.079 6 +1 -1 +1 -1 1.013 7 -1 1 +1 -1 1.157 8 +1 1 +1 -1 0.827 9 -1 -1 -1 +1 1.002 10 +1 -1 -1 +1 0.95 11 -1 1 -1 +1 1.174 12 +1 1 -1 +1 1.024 13 -1 -1 +1 +1 1.229 14 +1 -1 +1 +1 0.744 15 -1 1 +1 +1 1.081 16 +1 1 +1 +1 0.815 17 -1 0 0 0 1.229 18 +1 0 0 0 0.952     4 Page 1-5 © MAT Journals 2018. All Rights Reserved   Journal of Mechanical and Mechanics Engineering  Volume 4 Issue 2  19 0 -1 0 0 1.131 20 0 1 0 0 1.084 21 0 0 -1 0 1.166 22 0 0 +1 0 1.298 23 0 0 0 -1 0.967 24 0 0 0 +1 1.008 25 0 0 0 0 1.196 26 0 0 0 0 1.156 27 0 0 0 0 1.156 28 0 0 0 0 0.977 29 0 0 0 0 1.048 30 0 0 0 0 1.055  Conducting the confirmatory Tests To determine the accuracy of the developed models developed, confirmatory runs are conducted with the same experimental set up. In the confirmatory tests, the process variables are assigned some intermediate values and responses are measured. Input values and results from the confirmatory experiments are given in Table 3. The results show that the accuracy of the model is above 95%.The percentage of error is calculated by [{Actual value- predicted value}/predicted value] x 100 Table 3 Comparison of results spindle speed   feed rate depth of cut tool nose radius Experimental values Predicted  values % error  850 44 0.36 0.4 1.013 1.111 - 9.13 950 48 0.42 0.6 1.135 1.029 9.34 1050 52 0.48 0.8 0.851 0.947 -11.28 1150 56 0.54 0.6 1.041 0.915 12.10  CONCLUSIONS The plan of investigation and analysis of cutting parameters is elaborated. Significant process parameters affecting the cutting process are identified as spindle speed, feed rate, depth of cut   and tool nose radius. Indicators to predict the machining quality are identified in terms of surface roughness Experiments were conducted using Central composite design orthogonal array and the responses are recorded. The performance characteristics of the cutting parameters are evaluated using regression analysis. Mathematical models are developed from the responses obtained and are validated. This work can be extended to other manufacturing process also. REFERENCES  1. Bralla  J. G, (1998), Design for Manufacturability Handbook. 2nd edition, New York: McGraw-Hill. 2. Chern G, (1993), Analysis of burr formation and Breakout in metal cutting. Ph.D. dissertation, University of California—Berkeley. 3. Kalpakjian S. and Schmid S, (2000), Manufacturing Engineering and Technology. 4th edn, Upper Saddle River, NJ: Prentice Hall. 4. Oxley P.L.B. (1988), ―Modelling machining processes with a view to their optimization and the adaptive control of metal cutting machine tools‖ Robot. Comput.-Integrated Manuf, Vol 4, pp103–119.     5 Page 1-5 © MAT Journals 2018. All Rights Reserved   Journal of Mechanical and Mechanics Engineering  Volume 4 Issue 2  5. Chryssolouris G and Guillot M, 1990) A comparison of statistical and AI approaches to the selection of process parameters in intelligent machining, ASME J. Eng. Ind. Vol  112,  pp122–131. 6. Abuelnaga, A.M and  El-Dardiry M.A, (1984) Optimization methods for metal cutting, Int. J. Mach. Tool Des. Res. Vol  24 No1, pp 1–18. 7. Zhou C and Wysk R.A, (1992), ―An integrated system for selecting optimum cutting speeds and tool replacement times‖ International Journal of Machine Tools and Manufacture,  Vol  32, pp 695–707. 8. Yao Y, and Fang X.D, (1992), ―Modelling of multivariate time series for tool wear estimation in finish turning‖ Int. J. Mach. Tools Manuf. Vol 32, No 4,  pp  495–508. 9. Hashimura, M. Hassamontr. J and Dornfeld. D, (1999), ―Effect of in-plane exit angle and rake angles on burr height and thickness in face milling operation‖ ASME, Journal of Manufacturing Science & Engineering, Vol ,121, pp 13–18. 10. Hassamontr. J, (1998), Edge finishing planning in milling. Ph.D. dissertation, University of California—Berkeley 11. Olvera, O and Barrow. G, (1996), ―An experimental study of burr formation in square shoulder face milling. International Journal of Machine Tools Manufacturing, Vol  36, No 9 ,pp 1005–1020. 12. Olvera, O and Barrow. G, (1998), ―Influence of exit angle and tool nose geometry on burr formation in face milling operations‖ Proceedings of the Institute of Mechanical Engineers, Vol 212 (Part B),  pp. 59–72 13. Manna, A and Bhattacharyya. B, (2004), ―Investigation for optimal parametric combination for achieving better surface finish during turning of Al/SiC-MMC‖ Int. J. Adv. Manuf. Technol. Vol 23, pp 658–665. 14. Ross, P.J. (1988) Taguchi Techniques for Quality Engineering, Mc- Graw-Hill, New York. 15. Myers, R. H and Montgomery, D. C, (1995) Response Surface Methodology: Process and Product Optimization Using Designed Experiments (Wiley). 16. Box G. E. P and Draper N. R. (1987), "Empirical Model Building and Response Surfaces," John Wiley and Sons, New York. 17. Rajesh Prabha N and  J. Edwin Raja Dhas (2017) Design optimization of surface roughness by turning process using response surface methodology and grey relational analysis International Journal of Mechanical Engineering and Technology Volume 8, Issue 8, pp. 810–810. 18. Ramanan G and Edwin Raja Dhas J (2017) Multi Objective Optimization of Machining Parameters for AA7075 Metal Matrix Composite Using Grey - Fuzzy Technique International Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 8 pp. 1729-1735. 19. J. Edwin Raja Dhas and P.Pradeep ,(2015) Application of RSM and ANFIS models for machining Quality Prediction, Alloy Journal of Soft Computing and Applications Volume 3, Issue 1 pp.5-13. 20. J.Edwin Raja Dhas,   R.S. Stalin and J. Rajeesh  (2013) RBF neural network model for machining quality prediction  International Journal of Modeling Identification and Control  Vol 20, No 2, 174-180. 

Modeling Of Metal Cutting Process Using Response Surface Methodology

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Modeling Of Metal Cutting Process Using Response Surface Methodology

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