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Topology Optimization of Self-Supporting Structures for Additive Manufacturing with Adaptive Explicit Continuous Constraint

Jun Zou*, Haolei Mou
College of Safety Science and Engineering, Civil Aviation University of China, Tianjin, 300300, China
* Corresponding Author: Jun Zou. Email:
(This article belongs to this Special Issue: New Trends in Structural Optimization)

Computer Modeling in Engineering & Sciences 2022, 132(2), 451-469. https://doi.org/10.32604/cmes.2022.020111

Received 04 November 2021; Accepted 29 December 2021; Issue published 15 June 2022

Abstract

The integration of topology optimization (TO) and additive manufacturing (AM) technologies can create significant synergy benefits, while the lack of AM-friendly TO algorithms is a serious bottleneck for the application of TO in AM. In this paper, a TO method is proposed to design self-supporting structures with an explicit continuous self-supporting constraint, which can be adaptively activated and tightened during the optimization procedure. The TO procedure is suitable for various critical overhang angles (COA), which is integrated with build direction assignment to reduce performance loss. Besides, a triangular directional self-supporting constraint sensitivity filter is devised to promote the downward evolution of structures and maintain stability. Two numerical examples are presented; all the test cases have successfully converged and the optimized solutions demonstrate good manufacturability. In the meanwhile, a fully self-supporting design can be obtained with a slight cost in performance through combination with build direction assignment.

Keywords

Topology optimization; additive manufacturing; self-supporting constraint; build direction assignment; gradual evolution

Cite This Article

Zou, J., Mou, H. (2022). Topology Optimization of Self-Supporting Structures for Additive Manufacturing with Adaptive Explicit Continuous Constraint. CMES-Computer Modeling in Engineering & Sciences, 132(2), 451–469.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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