Optimising parameters of fused filament fabrication process to achieve optimum tensile strength using artificial neural network

Abstract

Currently, the Additive Manufacturing (AM) process has become the most researched area, leading to a revolution in the manufacturing industries. Among all additive manufacturing techniques, fused filament fabrication (FFF) is famous because simple to use and economical. However, in the FFF process, the final quality of parts depends upon the rigorous selection of process parameters, as it is necessary to understand the physical phenomena of process variables and their impact on the mechanical properties. This study involves the independent analysis of three process variables like layer thickness, orientation, and printing temperature. Taguchi L9 orthogonal array opted to investigate the tensile strength of Acrylonitrile butadiene styrene (ABS). By using this, the number of experimental reduced from L27 to L9 experiments. The specimens are fabricated based on ASTM D-638 tensile standard design. For training and testing purposes, the Artificial Neural Network tool has opted by using MATLAB software 2015.