POSSIBILITIES OF USING MONTE CARLO METHOD FOR SOLVING MACHINING OPTIMIZATION PROBLEMS

Abstract Companies operating in today's machining environment are focused on improving their product quality and decreasing manufacturing cost and time. In their attempts to meet these objectives, the machining processes optimization is of prime importance. Among the traditional optimization methods, in recent years, modern meta-heuristic algorithms are being increasingly applied to solving machining optimization problems. Regardless of numerous capabilities of the Monte Carlo method, its application for solving machining optimization problems has been given less attention by researchers and practitioners. The aim of this paper is to investigate the Monte Carlo method applicability for solving single-objective machining optimization problems and to analyze its efficiency by comparing the optimization solutions to those obtained by the past researchers using meta-heuristic algorithms. For this purpose, five machining optimization case studies taken from the literature are considered and discussed. References Kramar, D., Sekulić, M., Kovač, P., Gostimirović, M. Kopač, J., 2012, The implementation of Taguchi method for quality improvement in high-pressure jet assisted turning process, Journal of Production Engineering, 15(2), pp. 23-26. Sivarao, Thiru, S., Kamaruzaman, J., Azizah, S., Yusoff, M., Jano, Z., Yaakub, Y., Hasoalan, Hadzley, M., Shah, I., Izan, N., Amran, M., Taufik, Sapto, W., Tan, C.F., Sivakumar, D., 2013, Modelling of CO2 laser materials processing by networked neuro-dimension fuzzy intelligent system, Australian Journal of Basic and Applied Sciences, 7(3), pp. 35-45. Nedić, B., Janković, P., Radovanović, M., Globočki Lakić, G., 2013, Quality of plasma cutting, Proc. of 13th Int. Conference on Tribology “SERBIATRIB-2013”, Kragujevac, Serbia, pp. 314-319. Quazi, T.Z., More, P., Sonawane, V., 2013, A case study of Taguchi method in the optimization of turning parameters, International Journal of Emerging Technologies and Advanced Engineering, 3(2), pp. 616-626. Yusup, N., Zain, A.M., Hashim, S.Z.M., 2012, Evolutionary techniques in optimizing machining parameters: Review and recent applications (2007-2011), Expert System with Applications, 39(10), pp. 9909-9927. Kroese, D., Taimre, T., Botev, Z., 2011, Handbook of Monte Carlo methods, Wiley, New York. Yang, X.-S., 2010, Engineering Optimization - An Introduction with Metaheuristic Applications, Wiley, New York. Pokorádi, L., Molnár, B., 2011, Monte-Carlo simulation of the pipeline system to investigate water temperature’s effects, U.P.B. Scientific Bulletin Series D: Mechanical Engineering, 73(4), pp. 223-226. Sharma, A.V.N.L., Sandeep Kumar, P., Gopichand, A., Mohan Rao, R., 2012, Optimal machining conditions for turning of Al/SiC MMC using PSO and regression analysis, International Journal of Engineering Research and Applications, 2(6), pp. 497-500. Sanjeev Kumar, M., Kaviarasan, V., Venkatesan, R., 2012, Machining parameter optimization of polytetrafluoroethylene (PTFE) using genetic algorithm, International Journal of Modern Engineering Research, 2(1), pp. 143-149. Saravanakumar, K., Pratheesh Kumar, M.R., Shaik Dawood, A.K., 2012, Optimization of CNC turning process parameters on Inconel 718 using genetic algorithm, IRACST Engineering Science and Techology: International Journal ESTIJ, 2(4), pp. 532-537. Bhushan, R.K., Kumar, S., Das, S., 2012, GA Approach for optimization of surface roughness parameters in machining of Al alloy SiC particle composite, Journal of Materials Engineering and Performance, 21(8), pp. 1676-1686. Poornima, Sukumar, 2012, Optimization of machining parameters in CNC turning of martensitic stainless steel using RSM and GA, International Journal of Modern Engineering Research, 2(2), pp. 539-542. Madić, M., Marković, D., Radovanović, M., 2013, Comparison of meta-heuristic algorithms for solving machining optimization problems, Facta Universitatis Series: Mechanical Engineering, 11(1), pp. 29-44.