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Texture-guided generative structural designs under local control
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AI
DOI：10.1016/j.cad.2018.10.002.png)
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Abstract
En 中文
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. (C) 2018 Elsevier Ltd. All rights reserved.
Keywords:
Generative design
Texture-guided
Topology optimization
Physics-adaptive regulator
Local feature control
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