Comparative Evaluation of Optimization Algorithms at training of Genetic Programming for Tensile strength prediction of FDM processed part

Fused deposition modeling (FDM) is a fast growing rapid prototyping (RP) technology due to its ability to build functional parts having complex geometrical shapes in reasonable build time. The literature reveals that the properties of RP built parts such as surface roughness, strength, dimensional accuracy, build cost, etc are related to and can be improved by the appropriate settings of the input process parameters. However, it is difficult to obtain an adequate strength for some applications due to the characteristics of the process. This paper, proposes particle swarm optimization (PSO) technique to suggest theoretical combination of parameter settings to achieve good strength simultaneously for all responses. Genetic Programming (GP) is used to approximate the relationship between process parameters (Layer thickness, Raster angle, Raster width and Air gap) and the Tensile strength that can withstand the part. The performance of the proposed method is compared with Differential Evolution (DE) algorithm in terms of prediction accuracy and convergence characteristics. Results show the potential of the both algorithm used for, but DEA gives better result than PSO.