Experimental investigation and empirical modelling of FDM process for compressive strength improvement

TLDR

Fused deposition modelling (FDM) offers tool‑free manufacturing of complex parts, but part properties, especially compressive strength, are highly sensitive to process parameters and anisotropic brittleness. The study investigates how layer thickness, build orientation, raster angle, raster width, and air gap affect the compressive stress of FDM test specimens. A predictive equation was optimized with quantum‑behaved particle swarm optimization, and compressive stress was also modeled with an artificial neural network for comparison. The results reveal a complex dependency of compressive stress on process parameters and yield a statistically validated predictive equation.

Abstract

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.