Optimization of process parameters in the abrasive waterjet machining using integrated SA-GA

Authors:
Azlan Mohd Zain;Habibollah Haron;Safian Sharif
Affiliations:
Soft Computing Research Group, Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor, Malaysia;Soft Computing Research Group, Faculty of Computer Science & Information Systems, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor, Malaysia;Department of Manufacturing & Industrial Engineering, Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor, Malaysia
Venue:
Applied Soft Computing
Year:
2011

Citing 5
Cited 1

Machining condition optimization by genetic algorithms and simulated annealing

Computers and Operations Research
A neuro-genetic approach for selection of process parameters in abrasive waterjet cutting considering variation in diameter of focusing nozzle

Applied Soft Computing
A review of optimization techniques in metal cutting processes

Computers and Industrial Engineering
Simulated annealing based pattern classification

Information Sciences: an International Journal
Application of GA to optimize cutting conditions for minimizing surface roughness in end milling machining process

Expert Systems with Applications: An International Journal

Model development for quality features of resistance spot welding using multi-objective Taguchi method and response surface methodology

Journal of Intelligent Manufacturing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this study, Simulated Annealing (SA) and Genetic Algorithm (GA) soft computing techniques are integrated to estimate optimal process parameters that lead to a minimum value of machining performance. Two integration systems are proposed, labeled as integrated SA-GA-type1 and integrated SA-GA-type2. The approaches proposed in this study involve six modules, which are experimental data, regression modeling, SA optimization, GA optimization, integrated SA-GA-type1 optimization, and integrated SA-GA-type2 optimization. The objectives of the proposed integrated SA-GA-type1 and integrated SA-GA-type2 are to estimate the minimum value of the machining performance compared to the machining performance value of the experimental data and regression modeling, to estimate the optimal process parameters values that has to be within the range of the minimum and maximum process parameter values of experimental design, and to estimate the optimal solution of process parameters with a small number of iteration compared to the optimal solution of process parameters with SA and GA optimization. The process parameters and machining performance considered in this work deal with the real experimental data in the abrasive waterjet machining (AWJ) process. The results of this study showed that both of the proposed integration systems managed to estimate the optimal process parameters, leading to the minimum value of machining performance when compared to the result of real experimental data.