Fused deposition modelling (FDM) is gaining distinct advantage in manufacturing industries because of its ability to manufacture parts with complex shapes without any tooling requirement and human interface. The properties of FDM built parts exhibit high dependence on process parameters and can be improved by setting parameters at suitable levels. Anisotropic and brittle nature of build part makes it important to study the effect of process parameters to the resistance to compressive loading for enhancing service life of functional parts. Hence, the present work focuses on extensive study to understand the effect of five important parameters such as layer thickness, part build orientation, raster angle, raster width and air gap on the compressive stress of test specimen. The study not only provides insight into complex dependency of compressive stress on process parameters but also develops a statistically validated predictive equation. The equation is used to find optimal parameter setting through quantum-behaved particle swarm optimization (QPSO). As FDM process is a highly complex one and process parameters influence the responses in a non linear manner, compressive stress is predicted using artificial neural network (ANN) and is compared with predictive equation

Dimensional accuracy of a fused deposition modelling (FDM) built part is greatly influenced by many process parameters. In this study, the effect of five process parameters such as layer thickness, part build orientation, raster angle, air gap, and raster width along with their interactions has been studied using Taguchi's L₂₇ orthogonal array. Experimental results indicate that the measured dimension is always more than the desired value along the thickness direction but the length, width, and diameter of hole of test part are less than the desired value. It has been observed that optimal factor settings for each performance characteristic such as percentage change in length, width, thickness, and diameter are different. In order to minimize four responses simultaneously, the grey-Taguchi method is adopted and optimum factor levels have been reported. Finally, overall dimensional accuracy is predicted using artificial neural network (ANN).