An Integrated RSM-GA Based Approach for Multi Response Optimization of FDM Process Parameters for Pyramidal ABS Primitives

Abstract

AbstractIn this research work, a statistical model is developed for predicting the optimal process parameters of Fused Deposition Modelling (FDM) process for layout optimization. Multi response optimization of process parameters was achieved using Response Surface Methodology technique integrated with Genetic Algorithm. Response Surface Methodology (RSM) was utilized to design and conduct experiments. 86 experiments were conducted according to central composite design considering six process parameters namely raster width, raster angle, contour width, air gap, slice height and orientation to achieve four responses namely build time, model material volume, support structure volume and production cost. RSM-genetic algorithms (GA) integrated optimization is introduced in which GA is constructed including the development of coding strategy, evaluation operator and the fitness function. The constructed GA can meet the requirement of optimization work. The fitness function is defined as the sum of compulsive constraints or responses. All the constraints/responses have assigned same weightage. A Matlab genetic algorithm solver is utilized to predict best fitness values along with the optimal individual parameters in the present work.