Grey relational analysis based optimization of process parameters for efficient performance of fused deposition modelling based 3D printer

Abstract

Rapid prototyping techniques such as three-dimensional (3D) printing have rapidly gained popularity in industry since material layers are added rather than removed. Additive manufacturing creates objects from 3D CAD model data by layering materials, thus saving time and money. Fused deposition modelling (FDM) is the most often utilized additive manufacturing technology. To find the best parameters simultaneously affecting tensile strength, flexural strength, and wear resistance, this research aims to make use of grey relational analysis (GRA). In this investigation, the effect of different combinations of layer height, extruder temperature, infill percentage and print speed on the three mechanical properties is examined in depth. GRA optimization is used to find the ideal design factor levels for achieving the lowest wear rate while still providing the maximum possible tensile and flexural strength for the data obtained for sixteen experiments as per L16 orthogonal array. It is crucial to do Analysis of Variance (ANOVA) analysis in order to figure out the parameter contribution ratios. The grey relational grade (GRG) for any combination can be predicted quite accurately using regression analysis. The findings of the confirmation experiments demonstrated that the information gleaned from regression analysis is in line with that acquired from the experiments themselves.