Texture-guided generative structural designs under local control. (March 2019)

• Record Type: Journal Article
• Authors: Hu, Jingqiao; Li, Ming; Gao, Shuming
• Is Part Of: Computer aided design. Volume 108(2019)
• Page Start: 1
• Page End: 11
• Publication Date: 2019-03
• Subjects: Generative design -- Texture-guided -- Topology optimization -- Physics-adaptive regulator -- Local feature control; Computer-aided design -- Periodicals; Engineering design -- Data processing -- Periodicals; Computer graphics -- Periodicals; Conception technique -- Informatique -- Périodiques; Infographie -- Périodiques; Computer graphics; Engineering design -- Data processing; Periodicals; Electronic journals; 620.00420285
• DOI: 10.1016/j.cad.2018.10.002
• Languages: English
• ISSNs: 0010-4485

Abstract

Abstract: A novel generative design approach is developed in this study, which produces a mechanically optimized topological design while simultaneously mimicking an input exemplar texture. The textures are believed embedding certain functional information intrinsic to these objects. Designing objects similar to these textures will not only maintain such functions within the design but also widely expand the design space to explore more design options. However, simple textural replications or reconstructions cannot produce expected designs as an ideal structure has to adapt to the variations of the complex stress distributions caused by external loadings. On the other hand, a simple topology optimization formulation under a single global similarity constraint may produce undesirable structures exhibiting geometric disconnections or boundary protrusions. Due to these considerations, the proposed approach carefully formulates the problem as a classical topological compliance minimization problem under block-wise similarity constraints between the target design and an input texture. In addition, a novel physics-adaptive regulator is also proposed, which fine-tunes the block similarity according to its per-element compliances. Ultimately, we can create a set of design options both physically optimized and geometrically smooth for generative design. Extensive numerical results were also tested to demonstrate the approach's performance. Highlights: Novel generative design approach creates design options by mimicking textures. Formulated as topology optimization of block-wise similarity constraints, avoiding disconnection. Novel physics-adaptive regulator, avoiding protrusions, resulting smooth designs. Extensive 2D examples to test the approach's performance.