Hybrid neural network–particle swarm optimization algorithm and neural network–genetic algorithm for the optimization of quality characteristics during CO2 laser cutting of aluminium alloy

Abstract

Aerospace and automobile industries employ aluminium alloys due to its lightweight and excellent resistance to corrosion. As aluminium is highly reflective and highly thermally conductive, it is difficult-to-cut material by laser processing. The quality characteristics of the cut predominantly depend upon the combination of laser processing parameters. The main quality indices for evaluating CO2 laser cutting were surface roughness, kerf width and kerf taper; and the machining parameters considered were laser power, speed and gas pressure. This work suggests hybrid artificial neural network (ANN)–particle swarm optimization (PSO) algorithm and artificial neural network (ANN)–genetic algorithm (GA) to optimize the associated multi-response characteristics during CO2 laser cutting of aluminium 6061 alloys. The results illustrate that the hybrid ANN–GA and ANN–PSO model is an efficient tool for the optimization of process parameters in CO2 laser cutting of difficult-to-cut material—aluminium. From the optimization results, it can be concluded that the proposed ANN–GA approach can be efficiently utilized to optimize the parameters for obtaining minimum roughness, kerf width and kerf taper.