Statistical Modeling and Optimization of Surface Roughness for PLA and PLA/Wood FDM Fabricated Items

During last decades fused deposition modeling (FDM) has emerged as a widely applied additive manufacturing technology for numerous engineering applications. The present work investigates the effects of two independent variables during FDM fabrication of conventional polylactic acid (PLA) and organic biocompatible composite material with coconut flour (PLA/w) on mean surface roughness (Ra) of fabricated items. The parameter optimization adopts a customized response surface (RSM) design, based on an L9 orthogonal array. The independent variables investigated, were nozzle temperature, NT (oC) and layer thickness, LT (mm) whilst regression models for Ra concerning both materials; PLA and PLA/W, were developed to correlate the independent parameters. Proper analysis was preceded, based on response surface analysis through contour plots. The regression models were further utilized as objective functions to minimize Ra for both filament materials with the use of grey-wolf optimization genetic algorithm (GWO)