Multi-objective Optimisation and Multi-criteria Decision Making for FDM Using Evolutionary Approaches

In this paper, we describe a systematic multi-objective problem solving approach, simulataneosly minimizing two conflicting goals average surface roughness ‘Ra’ and build time ‘T ’, for object manufacturing in FDM process by usage of evolutionary algorithms. Popularly used multi-objective genetic algorithm NSGA-II and recently proposed multi-objective particle swarm optimization (MOPSO) algorithms, are employed for the optimization purposes. Statistically significant performance measures are employed to compare the two algorithms and means to arrive at approximate Pareto-optimal fronts are also suggested. To refine the solutions obtained by the optimizers, a mutation driven hill climbing local search is also proposed. Several suggestions and three new proposals pertaining to the issue of decision making in presence of trade-off solutions are also made. The overall procedure is integrated into a MORPE Multi-objective Rapid Prototyping Engine. Several sample objects are considered for simulation to demonstrate the working of MORPE. Finally, a careful study of optimal build directions for several components considered indicates a trend, providing an insight into the FDM processes and can be considered useful for various practical RP applications. Multi-objective Optimization, Decision Making, Genetic Algorithms, Particle Swarm Optimization and FDM rapid prototyping process.