Genetic Algorithm and Simulated Annealing to estimate optimal process parameters of the abrasive waterjet machining

In this study, two computational approaches, Genetic Algorithm and Simulated Annealing, are applied to search for a set of optimal process parameters value that leads to the minimum value of machining performance. The objectives of the applied techniques are: (1) to estimate the minimum value of the machining performance compared to the machining performance value of the experimental data and regression modeling, (2) to estimate the optimal process parameters values that has to be within the range of the minimum and maximum coded values for process parameters of experimental design that are used for experimental trial and (3) to evaluate the number of iteration generated by the computational approaches that lead to the minimum value of machining performance. Set of the machining process parameters and machining performance considered in this work deal with the real experimental data of the non-conventional machining operation, abrasive waterjet. The results of this study showed that both of the computational approaches managed to estimate the optimal process parameters, leading to the minimum value of machining performance when compared to the result of real experimental data.

[1]  Habibollah Haron,et al.  Application of GA to optimize cutting conditions for minimizing surface roughness in end milling machining process , 2010, Expert Syst. Appl..

[2]  Ulaş Çaydaş,et al.  A study on surface roughness in abrasive waterjet machining process using artificial neural networks and regression analysis method , 2008 .

[3]  F. Erzincanli,et al.  Prediction of minimum surface roughness in end milling mold parts using neural network and genetic algorithm , 2006 .

[4]  Henry C. W. Lau,et al.  Machining process sequencing with fuzzy expert system and genetic algorithms , 2003, Engineering with Computers.

[5]  Oğuz Çolak,et al.  Milling surface roughness prediction using evolutionary programming methods , 2007 .

[6]  S. G. Deshmukh,et al.  A genetic algorithmic approach for optimization of surface roughness prediction model , 2002 .

[7]  Mirko Ficko,et al.  Prediction of surface roughness with genetic programming , 2004 .

[8]  Tarunraj Singh,et al.  Machining condition optimization by genetic algorithms and simulated annealing , 1997, Comput. Oper. Res..

[9]  Manoj Kumar Tiwari,et al.  Modeling machine loading problem of FMSs and its solution methodology using a hybrid tabu search and , 2004 .

[10]  Christos A. Frangopoulos,et al.  A Genetic Algorithm for operation optimization of an industrial cogeneration system , 1996 .

[11]  Franci Cus,et al.  Optimization of cutting process by GA approach , 2003 .

[12]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[13]  Bean Yin Lee,et al.  The optimal cutting-parameter selection of production cost in HSM for SKD61 tool steels , 2003 .

[14]  S. Shanmugasundaram,et al.  Optimization of machining parameters using genetic algorithm and experimental validation for end-milling operations , 2007 .

[15]  Y. S. Tarng,et al.  Determination of optimal cutting parameters in wire electrical discharge machining , 1995 .

[16]  Sanghamitra Bandyopadhyay,et al.  Simulated Annealing Based Pattern Classification , 1998, Inf. Sci..

[17]  Yoke San Wong,et al.  Optimization of multi-pass milling using parallel genetic algorithm and parallel genetic simulated annealing , 2005 .

[18]  Francisco J. Campa,et al.  Model development for the prediction of surface topography generated by ball-end mills taking into account the tool parallel axis offset. Experimental validation , 2008 .

[19]  L. N. López de Lacalle,et al.  The effect of ball burnishing on heat-treated steel and Inconel 718 milled surfaces , 2007 .

[20]  S. Sharif,et al.  SIMULATED ANNEALING TO ESTIMATE THE OPTIMAL CUTTING CONDITIONS FOR MINIMIZING SURFACE ROUGHNESS IN END MILLING Ti-6Al-4V , 2010 .

[21]  Neelesh Kumar Jain,et al.  Optimization of process parameters of mechanical type advanced machining processes using genetic algorithms , 2007 .

[22]  Hasan Kurtaran,et al.  Application of response surface methodology in the optimization of cutting conditions for surface roughness , 2005 .