Empirical Modelling and Multi-Objective Optimisation of Laser Micro Machining on Magnesium Alloy AS21-SiC Metal Matrix Composite

Abstract

Micromachining techniques are now being used more frequently as a result of miniaturization. This technique has been supported by the requirement for material processing at an affordable cost and microatomic resolution in numerous sectors. Laser micromachining is a precise, non-contact method of machining that is used to create tiny, up to 500 m, components. The small elemental areas are the focus of laser ablation, which helps absorb a high amount of energy. In this micro-machining, metal removal rate and surface finish are represented by the deepness of the groove and the height of the recast layer. While machining, a layer called a recast layer forms on the work piece surface as a result of the tremendous heat generated, and this layer is damaging to the component's surface quality. For accurate applications, the recast layer must be as tiny as possible. As a result, the objective functions are the height of the recast layer and the deepness of the groove. Experiments designed by the DOE are used to generate empirical models. For each experimental run present in the matrix, the specified input parameter combination is set and the work piece is machined accordingly. The response surface methodology based on mathematical modeling and analysis of the machining properties of a pulsed Nd: YAG laser during micro-grooving operation on a work piece of Magnesium Silicon Alloy metal matrix composite is the focus of this research study. Initially, magnesium alloy AS21-SiC metal matrix composites are manufactured with Ultrasonic pro assisted stir casting. For the machined samples, the deepness of the groove and the height of recast layer will be measured by an optical measuring microscope. Consequently, the measured data is used by the GP to develop the mathematical models. In this work, an efficient GA-based genetic algorithm (NSGA-II) is applied to obtain the optimal parameters. As the chosen objectives are conflicting in nature, the problem is formulated as a multi-objective optimization problem.