Multi-objective Optimisation and Multi-criteria Decision Making in SLS Using Evolutionary Approaches

This paper proposes an integrated approach to arrive at optimal build orientations, simultaneously minimizing surface roughness ’Ra’ and build time ’T ’, for object manufacturing in SLS process. The optimization task is carried out by two popularly known multi-objective evolutionary optimizers NSGA-II (non-dominated sorting genetic algorithm) and MOPSO (multi-objective particle swarm optimizer). The performance comparison of these two optimizers, along with an approximation of Pareto-optimal front is done using two statistically significant performance measures. Three proposals addressing the task of decision making, i.e. selecting one solution in presence of multiple trade-off solutions, are made to facilitate the designer. A hill climbing local search procedure is also proposed to further refine the solutions obtained by evolutionary optimizers. The overall procedure is integerated into a MORPE Multi-objective Rapid Prototyping Engine. Several sample objects are considered for experimentation to demonstrate the working of MORPE. A careful study of optimal build directions for several components indicates a trend, providing insight into the SLS processes which can be regarded highly useful for various practical RP applications. Multi-objective Optimization, Decision Making, Genetic Algorithms, Particle Swarm Optimization and SLS.