A Moving Morphable Void (MMV)-based explicit approach for topology optimization considering stress constraints

• Published in: Computer methods in applied mechanics and engineering Vol. 334; pp. 381 - 413
• Main Authors: Zhang, Weisheng, Li, Dong, Zhou, Jianhua, Du, Zongliang, Li, Baojun, Guo, Xu
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01-06-2018; Elsevier BV
• Subjects: [formula omitted]-adaptive mesh; CAD; Computer aided design; Design engineering; Design optimization; Finite element analysis; Finite element method; Moving Morphable Void (MMV); Optimization; Shape optimization; Stress analysis; Stress constraints; Stresses; Studies; Topological manifolds; Topology optimization; Topology optimization; Stress constraints; Moving Morphable Void (MMV); h-adaptive mesh; Shape optimization
• ISSN: 0045-7825; 1879-2138
• DOI: 10.1016/j.cma.2018.01.050

Topology optimization considering stress constraints has received ever-increasing attention in recent years for both of its academic challenges and great potential in real-world engineering applications. Traditionally, stress-constrained topology optimization problems are solved with approaches where structural geometry/topology is represented in an implicit way. This treatment, however, would lead to problems such as the existence of singular optima, the risk of low accuracy of stress computation, and the lack of direct link between optimized results and computer-aided design/engineering (CAD/CAE) systems. With the aim of resolving the aforementioned issues straightforwardly, a Moving Morphable Void (MMV)-based approach is proposed in the present study. Compared with existing approaches, the distinctive advantage of the proposed approach is that the structural geometry/topology is described in a completely explicit way. This feature provides the possibility of obtaining optimized designs with crisp and explicitly parameterized boundaries using much fewer numbers of degrees of freedom for finite element analysis and design variables for optimization, respectively. Several numerical examples provided demonstrate the effectiveness and advantages of the proposed approach.