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



In this chapter, we methodologically describe a multi-objective problem solving approach, concurrently minimising two conflicting goals—average surface roughness—Ra and build time—T, for object manufacturing in Fused Deposition Method (FDM) process by usage of evolutionary algorithms. Popularly used multi-objective genetic algorithm (NSGA-II) and recently proposed multi-objective particle swarm optimisation (MOPSO) algorithms are employed for the optimisation purposes. Statistically significant performance measures are employed to compare the two algorithms and approximate the Pareto-optimal fronts. To refine the solutions obtained by the evolutionary optimisers, an effective mutation-driven hill-climbing local search is proposed. Three new proposals and several suggestions pertaining to the issue of decision making in the presence of multiple optimal solutions are made. The overall procedure is integrated into an engine called MORPE—multi-objective rapid prototyping engine. Sample objects are considered and several case studies are performed to demonstrate the working of MORPE. Finally, a careful investigation of the optimal build orientations for several considered objects is done or selected basis and a trend is discovered, which can be considered highly useful for various practical rapid prototyping (RP) applications.


Local Search Extreme Solution Decision Choice Rapid Prototype Method Achievement Scalarising Function 
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© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringMassachusetts Institute of TechnologyCambridgeUSA
  2. 2.Department of Mechanical EngineeringIndian Institute of Technology KanpurKanpurIndia
  3. 3.Laboratory of Manufacturing and ProductivityMassachusetts Institute of TechnologyCambridgeUSA

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