Multi-Material Composition Optimization vs Software-Based Single-Material Topology Optimization of a Rectangular Sample under Flexural Load for Fused Deposition Modeling Process

Abstract

Additive manufacturing (AM) technologies have been evolved over the last decade, enabling engineers and researchers to improve functionalities of parts by introducing a growing technology known as multi-material AM. In this context, fused deposition modeling (FDM) process has been modified to create multi-material 3D printed objects with higher functionality. The new technology enables it to combine several types of polymers with hard and soft constituents to make a 3D printed part with improved mechanical properties and functionalities. Knowing this capability, this paper aims to present a parametric optimization method using a genetic algorithm (GA) to find the optimum composition of hard polymer as polylactic acid (PLA) and soft polymer as thermoplastic polyurethane (TPU 95A) used in Ultimaker 3D printer for making a rectangular sample under flexural load in order to minimize the von Mises stress as an objective function. These samples are initially presented in four deferent forms in terms of composition of hard and soft polymers and then, after the optimization process, the final ratio of each type of material will be achieved. Based on the volume fraction of soft polymers in each sample, the equivalent topologically-optimized samples will be obtained that are solely made of single-material PLA as hard polymer under the same flexural load as applied to multi-material samples. Finally, the structural results and manufacturability in terms of the generated support structures, as key element of some AM processes, will be compared for the resultant samples created by two methods of optimization.