Optimization of fused deposition modeling process using a virus-evolutionary genetic algorithm

Abstract Fused Deposition modelling (FDM) is one of the most widely used Additive Manufacturing technologies that extrude a melted plastic filament through a heated nozzle in order to build final physical models layer-by-layer. In this research, a virus-evolutionary genetic algorithm (MOVEGA) is developed and implemented to solve a multi-objective optimization problem related to fused deposition modelling. Taguchi approach was first employed for the experimental procedure design and nine test parts were built according to L9 orthogonal array. The examined process parameters were the deposition angle, layer thickness, and infill ratio each one having three levels. Infill pattern was constant to honeycomb selection. Fabrication time of ABS (Acrylonitrile-Butadiene-Styrene) 3D printed specimens was measured during experiments and analyzed by using Analysis of Means (ANOM) and Analysis of Variance (ANOVA) techniques. Shape accuracy was measured by considering the parts’ dimensions in X, Y and Z axes and expressed as the overall error for control. Regression models were developed to use them as objective functions for a group of multi-objective optimization algorithms. Multi-objective Greywolf (MOGWO), and multi-universe (MOMVO) algorithms where also selected for optimizing the FDM problem to compare results. To evaluate the algorithms and judge superiority with reference to the non-dominated solution sets obtained, the hypervolume indicator was adopted. It has been verified that MOVEGA exhibited superiority in its performance for optimizing FDM problems when compared to heuristics such as MOGWO and MOMVO algorithms whilst it has strong potentials to be coupled with “Internet of Things” (IoT) platforms to facilitate the intelligent optimization control referring to a range of resources, consumables and software.