Parametric optimization of parameters affecting dimension precision of FDM printed part using hybrid Taguchi-MARCOS-nature inspired heuristic optimization technique

Abstract

Fused Deposition Modelling is a fast emerging technology due to its capacity to generate usable components with multiple geometrical designs in a fair period of time without the usage of any tooling or human interaction. The features and reliability of FDM fabricated parts are highly dependent on a small number of processing variables and their settings. The current study examines the relationship between five significant processing constraints. i.e. raster angle, part orientation, air gap, layer thickness and raster width and what effect do they have on the dimensional accuracy of the fabricated part. Twenty-seven experiments were piloted and configured using Taguchi’s architecture and recently formulated MARCOS Method. Here, the Genetic Algorithm Optimization, Simulated Annealing Algorithm Optimization, Particle Swarm Optimization, Grey-Wolf Optimization Algorithm, Moth Flame Optimization Algorithm, Whale Optimization Algorithm, Jaya Algorithm Optimization, Sunflower Optimization Algorithm, Lichtenberg Algorithm Optimization and Forensic Based Investigation Optimization approaches as ten different optimizations have been utilized to predict the optimal setting of the experiment. A comparative inspection of these nature-inspired algorithms in FDM printed part was performed in this study which reported part orientation as the most significant element.