Grey relational analysis based particle swarm optimisation (PSO) of quality characteristics in laser cutting of titanium alloy sheet

Abstract

ABSTRACT Machining of advanced engineering material like titanium alloy (grade 5) with precision and accuracy is an evolving requirement. The selection of an appropriate advanced machining method and range of cutting parameters is very essential to obtain high-quality cut and geometric accuracy. The traditional machining methods are incompetent to machine the titanium alloy sheet material due to its unfavourable mechanical and thermal properties and high chemical affinity at elevated temperature. The Nd:YAG laser cutting process delivers better cutting quality, precision and geometric accuracy. In this work, the regression method has been adopted for developing the empirical models of geometric quality characteristics such as kerf width, kerf deviation and kerf taper. These developed models have been optimised using grey relational analysis based particle swarm optimisation in order to ascertain the optimal range of cutting parameters pertaining to better cut with high precision and geometric accuracy. The optimisation results show individual improvement in top kerf width, top kerf deviation and kerf taper of 28.88%, 13.33% and 56.52%, respectively, and 17.42% against the initial settings for multiple performance index. Finally, the effects of input process parameters on output quality characteristics have also been investigated.