Selection of wire electrical discharge machining process parameters using non-traditional optimization algorithms

• Published in: Applied soft computing Vol. 12; no. 8; pp. 2506 - 2516
• Main Authors: Mukherjee, Rajarshi, Chakraborty, Shankar, Samanta, Suman
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.08.2012
• Subjects: Ant colony optimization; Artificial bee colony; Biogeography-based optimization; Genetic algorithm; Optimization; Particle swarm optimization; Process parameter; Sheep flock algorithm; Wire electrical discharge machining; Ant colony optimization; Process parameter; Wire electrical discharge machining; Artificial bee colony; Genetic algorithm; Biogeography-based optimization; Sheep flock algorithm; Particle swarm optimization; Optimization
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2012.03.053

[Display omitted] ► Selection of the optimal WEDM process parameters is always very important. ► This paper compares the performance of six optimization algorithms. ► Biogeography-based optimization algorithm outperforms the others. ► The process engineer can now easily set the WEDM process parameters. Selection of the optimal values of different process parameters, such as pulse duration, pulse frequency, duty factor, peak current, dielectric flow rate, wire speed, wire tension, effective wire offset of wire electrical discharge machining (WEDM) process is of utmost importance for enhanced process performance. The major performance measures of WEDM process generally include material removal rate, cutting width (kerf), surface roughness and dimensional shift. Although different mathematical techniques, like artificial neural network, gray relational analysis, simulated annealing, desirability function, Pareto optimality approach, etc. have already been applied for searching out the optimal parametric combinations of WEDM processes, but in most of the cases, sub-optimal or near-optimal solutions have been arrived at. In this paper, an attempt is made to apply six most popular population-based non-traditional optimization algorithms, i.e. genetic algorithm, particle swarm optimization, sheep flock algorithm, ant colony optimization, artificial bee colony and biogeography-based optimization for single and multi-objective optimization of two WEDM processes. The performance of these algorithms is also compared and it is observed that biogeography-based optimization algorithm outperforms the others.