4.7 Article

Topology optimization including a model of the layer-by-layer additive manufacturing process

Journal

Publisher

ELSEVIER SCIENCE SA
DOI: 10.1016/j.cma.2022.115203

Keywords

Topology optimization; Additive manufacturing; Layer-by-Layer process; Thermal conductivity; Self-weight; Stiffness

Funding

  1. Sao Paulo Research Foundation (FAPESP), Brazil [CEPID-CeMEAI 2013/07375-0, 2020/14288-0]
  2. National Council for Scientific and Technological Development -CNPq, Brazil [304192/2019-8, 304074/2020-9]

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This paper considers a topology optimization formulation that includes a model of the layer-by-layer additive manufacturing process. By formulating it as a multi-objective minimization problem, the performance and cost of both final and partially manufactured designs are taken into account, along with addressing important AM-related issues such as overhang and residual stresses. The study shows that the proposed approach is computationally viable and can avoid issues associated with geometry-based formulations for overhang limitation.
A topology optimization formulation including a model of the layer-by-layer additive manufacturing (AM) process is considered. Defined as a multi-objective minimization problem, the formulation accounts for the performance and cost of both the final and partially manufactured designs and allows for considering AM-related issues such as overhang and residual stresses in the optimization. The formulation is exemplified by stiffness optimization in which the overhang is limited by adding mechanical or thermal compliance as a measure of the cost of partially manufactured designs. Convergence of the model as the approximate layer-by-layer model is refined is shown theoretically, and an extensive numerical study indicates that this convergence can be fast, thus making it a computationally viable approach useful for including AM-related issues into topology optimization. The examples also show that drips and sharp corners associated with some geometry-based formulations for overhang limitation can be avoided. The codes used in this article are written in Python using only open sources libraries and are available for reference. (c) 2022 Elsevier B.V. All rights reserved.

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