Enhancing the Structural Performance of Additively Manufactured Objects Through Build Orientation Optimization

Abstract

Additively manufactured objects often exhibit directional dependencies in their structure due to the layered nature of the printing process. While this dependency has a significant impact on the object's functional performance, the problem of finding the best build orientation to maximize structural robustness remains largely unsolved. We introduce an optimization algorithm that addresses this issue by identifying the build orientation that maximizes the factor of safety (FS) of an input object under prescribed loading and boundary configurations. First, we conduct a minimal number of physical experiments to characterize the anisotropic material properties. Next, we use a surrogate-based optimization method to determine the build orientation that maximizes the minimum factor safety. The surrogate-based optimization starts with a small number of finite element (FE) solutions corresponding to different build orientations. The initial solutions are progressively improved with the addition of new solutions until the optimum orientation is computed. We demonstrate our method with physical experiments on various test models from different categories. We evaluate the advantages and limitations of our method by comparing the failure characteristics of parts printed in different orientations.

Lightweight structure design under force location uncertainty

“…Our structural optimization method strengthens the model up to 46% while increasing its mass by only 15%. The discrepancies between the input force budget and the measured failure forces can be due to the FEA modeling of the problem as well as the possible anisotropic behavior of the 3D printed material [Ulu et al 2015].…”

Anisotropic Multicomponent Topology Optimization for Additive Manufacturing With Build Orientation Design and Stress-Constrained Interfaces

“…While such anisotropic structural behavior has been experimentally observed (e.g., [3,4,5]), limited attempts have been made to consider this factor during the computational design optimization of additively-manufactured structures. Ulu et al [6] considered structural anisotropic behaviors while optimizing the build orientations of additively manufactured mechanical products. Their optimization scheme is based on the design of experiments of printed samples and surrogate modeling.…”

Untitled

“…Umetani and Schmidt [11] addressed structural anisotropy in Fused Deposition Modeling (FDM) with the assumption that the vertical bonds between the layers are weaker than the in-layer bond for pure bending cases. Ulu et al [10] introduced build orientation optimization algorithm based on the maximum Factor of Safety (FOS) approach under single loading conditions using surrogate based method. However, it requires a large number of simulations for accurate results which is computationally expensive, especially for multiple loading conditions.…”

“…Such problems cannot be solved using conventional optimization methods and can be considered a black box. It can be solved using brute force approach or using approximate techniques such as surrogate approximation [10]. The brute force approach utilizes large number of FE (Finite Element) simulations to cover the design space which is computationally expensive.…”

“…Methodology: This work presents generalized machine learning based parameter optimization methodology for strength enhancement of 3-D printed components. The proposed methodology is a modified version of three step approach presented by Ulu et al [10]. Fig.…”