How to cite this paper
Madić, M & Radovanović, M. (2014). Optimization of machining processes using pattern search algorithm.International Journal of Industrial Engineering Computations , 5(2), 223-234.
Refrences
Al-Sumait, J.S., Al-Othman, A.K., & Sykulski, J.K. (2007). Application of pattern search method to power system valve-point economic load dispatch. Electrical Power and Energy Systems, 29, 720–730.
Armarego, E.J.A., & Brown, R.H. (1969). The machining of metals. Englewood Cliffs, NJ: Prentice Hall.
Audet, C., & Dennis, J.E. (2006). Mesh adaptive direct search algorithms for constrained optimization. SIAM Journal on Optimization, 17, 188–217.
Bhattacharya, A., Faria-Gonzalez, R., & Inyong, H. (1970). Regression analysis for predicting surface finish and its application in the determination of optimum machining conditions. Journal of Engineering for Industry, 92, 711–714.
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, 1676–1686.
Cayda?, U., & Hasçalik, A. (2008). A study on surface roughness in abrasive waterjet machining process using artificial neural networks and regression analysis method. Journal of Materials Processing Technology, 202, 574–582.
Chen, M.C., & Tsai, D.M. (1996). A simulated annealing approach for optimization of multi-pass turning operations. International Journal of Production Research, 34, 2803–2825.
Debroy, A., & Chakraborty, S. (2013). Non-conventional optimization techniques in optimizing non-traditional machining processes: a review. Management Science Letters, 3, 23–38.
Dixit, P.M., & Dixit, U.S. (2008). Modeling of metal forming and machining processes: by finite element and soft computing methods. Springer.
El-Gizawy, A.S., & El-Sayed, J.J. (2002). A multiple objective based strategy for process design of machining operations. International Journal of Computer Integrated Manufacturing, 15, 353–360.
Ermer, D.S. (1971). Optimization of constrained machining economics problem by geometric programming, Journal of Engineering for Industry, 93, 1067–1072.
Ermer, D.S., & Patel, D.C. (1974). Maximization of the production rate with constraints by linear programming and sensitivity analysis. Proceedings NAMRC, pp. 436–449.
Gilbert, W.W. (1950). Economics of machining. In: Machining Theory and Practice. American Society of Metals, pp. 465–485.
Goswami, D., & Chakraborty, S. (2014). Differential search algorithm-based parametric optimization of electrochemical micromachining processes. International Journal of Industrial Engineering Computations, 5, 1–14.
Gupta, R., Batra, J.L., & Lal, G.K. (1995). Determination of optimal subdivision of depth of cut in multi-pass turning with constraints. International Journal of Production Research, 33, 2555–2565.
Hayers, G.M., & Davis, R.P. (1979). A discrete variable approach to machine parameter optimization. AIIE Transactions, 11, 155–159.
Kilickap, E., Huseyinoglu, M., & Yardimeden, A. (2011). Optimization of drilling parameters on surface roughness in drilling of AISI 1045 using response surface methodology and genetic algorithm. International Journal of Advanced Manufacturing Technology, 52, 79–88.
Kolda, T.G., Lewis, R.M, & Torczon, V (2003). Optimization by direct search: new perspectives on some classical and modern methods. SIAM Review, 45, 385–482.
Kova?evi?, M., Madi?, M., & Radovanovi?, M. (2013). Software prototype for validation of machining optimization solutions obtained with meta-heuristic algorithms. Expert Systems with Applications, 40, 6985–6996.
Lambert, P.K., & Walvekar, A.G. (1978). Optimization of multi-pass machining operations. International Journal of Production Research, 16, 247–259.
Lewis, R.M, Torczon, V., & Trosset, M.W. (2000). Direct search methods: then and now. Journal of Computational and Applied Mathematics, 124, 191–207.
Lewis, R.M, & Torczon, V. (2011). Direct search methods, in Wiley Encyclopedia of Operations Research and Management Science.
Maji, K., & Pratihar, D. K. (2011). Modeling of electrical discharge machining process using conventional regression analysis and genetic algorithms. Journal of Materials Engineering and Performance, 20, 1121–1127.
Macklem, M. (2006). Low-dimensional curvature methods in derivative-free optimization on shared computing networks. PhD Thesis, Dalhousie University.
Markos, S., Viharos, Zs.J., & Monostori, L. (1998). Quality-oriented, comprehensive modelling of machining processes. Sixth ISMQC IMEKO symposium on metrology for quality control in production, pp. 67–74.
MathWorks Inc. Global Optimization Toolbox User’s Guide, 2012. Natick, MA: MathWorks, Inc.
Mukherjee, I., & Ray, P. K. (2006). A review of optimization techniques in metal cutting processes. Computers and Industrial Engineering, 50, 15–34.
Petropoulos, P.G. (1973), Optimal selection of machining rate variables by geometric programming. International Journal of Production Research, 11, 305–314.
Philipson, R.H., & Ravindran, A. (1978). Application of goal programming to machinability data optimization. Journal of Mechanical Design, 100, 286–291.
Rao, R.V., & Pawar, P.J. (2009). Modelling and optimization of process parameters of wire electrical discharge machining. Proceedings of the Institution of Mechanical Engineers, Journal of Engineering Manufacture 223, 1431–1440.
Rao, R.V., & Pawar, P.J. (2010). Parameter optimization of a multi-pass milling process using non-traditional optimization algorithms. Applied Soft Computing, 10, 445–456.
Rao, R.V. (2011). Advanced modeling and optimization of manufacturing processes: international research and development. London: Springer-Verlag.
Rao, R.V., & Kalyankar, V.D. (2013). Parameter optimization of modern machining processes using teaching–learning-based optimization algorithm. Engineering Applications of Artificial Intelligence, 26, 524–531.
Samanta, S., & Chakraborty, S. (2011). Parametric optimization of some non-traditional machining processes using artificial bee colony algorithm. Engineering Applications of Artificial Intelligence, 24, 946–957.
Savas, V., & Ozay, C. (2008). The optimization of the surface roughness in the process of tangential turn-milling using genetic algorithm. International Journal of Advanced Manufacturing Technology, 37, 335–340.
Sekhon, G.S. (1982). Application of dynamic programming to multi-stage batch machining. Computer-Aided Design, 14, 157–159.
Shin, Y.C., & Joo, Y.S. (1992). Optimization of machining condition with practical constraints. International Journal of Production Research, 30, 2907–2919.
S?nmez, A.I., Baykasoglu, A., Dereli, T., & Filiz, I.H. (1999). Dynamic optimization of multipass milling operation via geometric programming. International Journal of Machine Tools and Manufacture, 39, 297–320.
Sundaram, R.M. (1978). An application of goal programming technique in metal cutting. International Journal of Production Research, 16, 375–382.
Tan, F.P., & Creese, R.C. (1995). A generalized multi-pass machining model for machining parameter selection in turning. International Journal of Production Research, 33, 1467–1487.
Torczon, V. (1989). Multi-directional search: a direct search algorithm for parallel machines. PhD thesis, Rice University.
Torczon, V. (1997). On the convergence of pattern search algorithms. SIAM Journal on Optimization, 7, 1–25.
Wang, Z.G., Wong, Y.S., & Rahman, M. (2004). Optimisation of multi-pass milling using genetic algorithm and genetic simulated annealing. International Journal Advanced Manufacturing Technology, 24, 727–732.
Yildiz, A.R. (2009). An effective hybrid immune-hill climbing optimization approach for solving design and manufacturing optimization problems in industry. Journal of Materials Processing Technology, 209, 2773–2780.
Yildiz, A.R., & Ozturk, F. (2006). Hybrid enhanced genetic algorithm to select optimal machining parameters in turning operation. Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture, 220, 2041–2053.
Yusup, N., Sarkheyli, A., Zain, A.M., Hashim, S.Z.M., & Ithnin, N. (2013). Estimation of optimal machining control parameters using artificial bee colony. Journal of Intelligent Manufacturing, DOI 10.1007/s10845-013-0753-y.
Zain, A.M., Haron, H., & Sharif, S. (2011). Genetic algorithm and simulated annealing to estimate optimal process parameters of the abrasive waterjet machining. Engineering with Computers, 27, 251–259.
Zhang, J.Y., Liang, S.Y., Yao, J., Chen, J.M., & Huang, J.L. (2006). Evolutionary optimization of machining processes. Journal of Intelligent Manufacturing, 17, 203–215.
Armarego, E.J.A., & Brown, R.H. (1969). The machining of metals. Englewood Cliffs, NJ: Prentice Hall.
Audet, C., & Dennis, J.E. (2006). Mesh adaptive direct search algorithms for constrained optimization. SIAM Journal on Optimization, 17, 188–217.
Bhattacharya, A., Faria-Gonzalez, R., & Inyong, H. (1970). Regression analysis for predicting surface finish and its application in the determination of optimum machining conditions. Journal of Engineering for Industry, 92, 711–714.
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, 1676–1686.
Cayda?, U., & Hasçalik, A. (2008). A study on surface roughness in abrasive waterjet machining process using artificial neural networks and regression analysis method. Journal of Materials Processing Technology, 202, 574–582.
Chen, M.C., & Tsai, D.M. (1996). A simulated annealing approach for optimization of multi-pass turning operations. International Journal of Production Research, 34, 2803–2825.
Debroy, A., & Chakraborty, S. (2013). Non-conventional optimization techniques in optimizing non-traditional machining processes: a review. Management Science Letters, 3, 23–38.
Dixit, P.M., & Dixit, U.S. (2008). Modeling of metal forming and machining processes: by finite element and soft computing methods. Springer.
El-Gizawy, A.S., & El-Sayed, J.J. (2002). A multiple objective based strategy for process design of machining operations. International Journal of Computer Integrated Manufacturing, 15, 353–360.
Ermer, D.S. (1971). Optimization of constrained machining economics problem by geometric programming, Journal of Engineering for Industry, 93, 1067–1072.
Ermer, D.S., & Patel, D.C. (1974). Maximization of the production rate with constraints by linear programming and sensitivity analysis. Proceedings NAMRC, pp. 436–449.
Gilbert, W.W. (1950). Economics of machining. In: Machining Theory and Practice. American Society of Metals, pp. 465–485.
Goswami, D., & Chakraborty, S. (2014). Differential search algorithm-based parametric optimization of electrochemical micromachining processes. International Journal of Industrial Engineering Computations, 5, 1–14.
Gupta, R., Batra, J.L., & Lal, G.K. (1995). Determination of optimal subdivision of depth of cut in multi-pass turning with constraints. International Journal of Production Research, 33, 2555–2565.
Hayers, G.M., & Davis, R.P. (1979). A discrete variable approach to machine parameter optimization. AIIE Transactions, 11, 155–159.
Kilickap, E., Huseyinoglu, M., & Yardimeden, A. (2011). Optimization of drilling parameters on surface roughness in drilling of AISI 1045 using response surface methodology and genetic algorithm. International Journal of Advanced Manufacturing Technology, 52, 79–88.
Kolda, T.G., Lewis, R.M, & Torczon, V (2003). Optimization by direct search: new perspectives on some classical and modern methods. SIAM Review, 45, 385–482.
Kova?evi?, M., Madi?, M., & Radovanovi?, M. (2013). Software prototype for validation of machining optimization solutions obtained with meta-heuristic algorithms. Expert Systems with Applications, 40, 6985–6996.
Lambert, P.K., & Walvekar, A.G. (1978). Optimization of multi-pass machining operations. International Journal of Production Research, 16, 247–259.
Lewis, R.M, Torczon, V., & Trosset, M.W. (2000). Direct search methods: then and now. Journal of Computational and Applied Mathematics, 124, 191–207.
Lewis, R.M, & Torczon, V. (2011). Direct search methods, in Wiley Encyclopedia of Operations Research and Management Science.
Maji, K., & Pratihar, D. K. (2011). Modeling of electrical discharge machining process using conventional regression analysis and genetic algorithms. Journal of Materials Engineering and Performance, 20, 1121–1127.
Macklem, M. (2006). Low-dimensional curvature methods in derivative-free optimization on shared computing networks. PhD Thesis, Dalhousie University.
Markos, S., Viharos, Zs.J., & Monostori, L. (1998). Quality-oriented, comprehensive modelling of machining processes. Sixth ISMQC IMEKO symposium on metrology for quality control in production, pp. 67–74.
MathWorks Inc. Global Optimization Toolbox User’s Guide, 2012. Natick, MA: MathWorks, Inc.
Mukherjee, I., & Ray, P. K. (2006). A review of optimization techniques in metal cutting processes. Computers and Industrial Engineering, 50, 15–34.
Petropoulos, P.G. (1973), Optimal selection of machining rate variables by geometric programming. International Journal of Production Research, 11, 305–314.
Philipson, R.H., & Ravindran, A. (1978). Application of goal programming to machinability data optimization. Journal of Mechanical Design, 100, 286–291.
Rao, R.V., & Pawar, P.J. (2009). Modelling and optimization of process parameters of wire electrical discharge machining. Proceedings of the Institution of Mechanical Engineers, Journal of Engineering Manufacture 223, 1431–1440.
Rao, R.V., & Pawar, P.J. (2010). Parameter optimization of a multi-pass milling process using non-traditional optimization algorithms. Applied Soft Computing, 10, 445–456.
Rao, R.V. (2011). Advanced modeling and optimization of manufacturing processes: international research and development. London: Springer-Verlag.
Rao, R.V., & Kalyankar, V.D. (2013). Parameter optimization of modern machining processes using teaching–learning-based optimization algorithm. Engineering Applications of Artificial Intelligence, 26, 524–531.
Samanta, S., & Chakraborty, S. (2011). Parametric optimization of some non-traditional machining processes using artificial bee colony algorithm. Engineering Applications of Artificial Intelligence, 24, 946–957.
Savas, V., & Ozay, C. (2008). The optimization of the surface roughness in the process of tangential turn-milling using genetic algorithm. International Journal of Advanced Manufacturing Technology, 37, 335–340.
Sekhon, G.S. (1982). Application of dynamic programming to multi-stage batch machining. Computer-Aided Design, 14, 157–159.
Shin, Y.C., & Joo, Y.S. (1992). Optimization of machining condition with practical constraints. International Journal of Production Research, 30, 2907–2919.
S?nmez, A.I., Baykasoglu, A., Dereli, T., & Filiz, I.H. (1999). Dynamic optimization of multipass milling operation via geometric programming. International Journal of Machine Tools and Manufacture, 39, 297–320.
Sundaram, R.M. (1978). An application of goal programming technique in metal cutting. International Journal of Production Research, 16, 375–382.
Tan, F.P., & Creese, R.C. (1995). A generalized multi-pass machining model for machining parameter selection in turning. International Journal of Production Research, 33, 1467–1487.
Torczon, V. (1989). Multi-directional search: a direct search algorithm for parallel machines. PhD thesis, Rice University.
Torczon, V. (1997). On the convergence of pattern search algorithms. SIAM Journal on Optimization, 7, 1–25.
Wang, Z.G., Wong, Y.S., & Rahman, M. (2004). Optimisation of multi-pass milling using genetic algorithm and genetic simulated annealing. International Journal Advanced Manufacturing Technology, 24, 727–732.
Yildiz, A.R. (2009). An effective hybrid immune-hill climbing optimization approach for solving design and manufacturing optimization problems in industry. Journal of Materials Processing Technology, 209, 2773–2780.
Yildiz, A.R., & Ozturk, F. (2006). Hybrid enhanced genetic algorithm to select optimal machining parameters in turning operation. Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture, 220, 2041–2053.
Yusup, N., Sarkheyli, A., Zain, A.M., Hashim, S.Z.M., & Ithnin, N. (2013). Estimation of optimal machining control parameters using artificial bee colony. Journal of Intelligent Manufacturing, DOI 10.1007/s10845-013-0753-y.
Zain, A.M., Haron, H., & Sharif, S. (2011). Genetic algorithm and simulated annealing to estimate optimal process parameters of the abrasive waterjet machining. Engineering with Computers, 27, 251–259.
Zhang, J.Y., Liang, S.Y., Yao, J., Chen, J.M., & Huang, J.L. (2006). Evolutionary optimization of machining processes. Journal of Intelligent Manufacturing, 17, 203–215.