Parametric optimisation in Nd-YAG laser cutting of thin Ti-6Al-4V super alloy sheet using evolutionary algorithms

In this paper, genetic and simulated annealing algorithm approaches are proposed for the selection of the optimal values in efficient Nd-YAG laser cutting of thin Ti-6Al-4V super alloy sheet. The pulse width, pulse energy, cutting speed, and gas pressure are considered as process parameters. Response surface methodology based Box-Behnken design is adopted to conduct the experiments for measuring the proposed performance characteristics such as kerf deviation (KD) and metal removal rate (MRR). Quadratic regression models are developed to predict the responses using response surface methodology. Analysis of variance tests have been carried out to check the adequacy of the developed regression models. Based on the developed mathematical models, the interaction effects of the process parameters on KD and MRR are investigated. Minimising KD and maximising the MRR are considered as objectives functions. The optimal laser cutting conditions are obtained to minimise the KD and maximise the MRR in considering single and multi objective optimisation methods. Validation tests with optimal levels of process parameters were performed to illustrate the effectiveness of GA and SA algorithms. It is believed that the used algorithms provide a robust way of looking the optimum process parameters for a selected laser cutting system.