Multi-Objective Optimization of Pulsed Nd: YAG Laser Cutting Process Using Entropy-Based ANN-PSO Model

Abstract

The paper investigated the efficacy of entropy-based ANN-PSO model combining Artificial Neural Networks (ANN) and Particle Swarm Optimization (PSO) for estimation and optimization of quality characteristics associated with pulsed Nd:YAG laser cutting of aluminium alloy. In the ANN-PSO model, ANN trained using backpropagation with the Bayesian regularization algorithm is employed for estimation and computation of objective function value during optimization with PSO. The entropy method is used to compute the real weight of different output quality characteristics during formulation of the combined objective function. An experiment has been conducted based on full factorial experimental design, where cutting speed, pulse energy, and pulse width are considered as controllable input parameters while kerf width, kerf deviation, surface roughness, and material removal rate are measured as output parameters. Further, the experimental dataset has been used in the ANN-PSO model for prediction and optimization. The prediction accuracy of the ANN module is indicated by a low mean absolute error of 1.74%. Experimental validation of optimized output also results in less than 2% error only. ANOVA study suggests cutting speed as the most influencing factor.