Journal
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
Volume 65, Issue 5, Pages -Publisher
SPRINGER
DOI: 10.1007/s00158-022-03224-x
Keywords
Topology optimization; Functionally graded structure; Multiscale; Multiclass; Shape interpolation; Data-driven design
Categories
Funding
- National Science Foundation (NSF) [OAC-1835782]
- NSF Graduate Research Fellowship [DGE-1842165]
- Zhiyuan Honors Program for Graduate Students of Shanghai Jiao Tong University
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This article proposes a data-driven framework for creating multiclass functionally graded structures with guaranteed feasibility. The framework combines multiple microstructure topologies to generate smoothly graded designs. Case studies and parameter analysis demonstrate the versatility and effectiveness of this method.
To create heterogeneous, multiscale structures with unprecedented functionalities, recent topology optimization approaches design either fully aperiodic systems or functionally graded structures, which compete in terms of design freedom and efficiency. We propose to inherit the advantages of both through a data-driven framework for multiclass functionally graded structures that mixes several families, i.e., classes, of microstructure topologies to create spatially-varying designs with guaranteed feasibility. The key is a new multiclass shape blending scheme that generates smoothly graded microstructures without requiring compatible classes or connectivity and feasibility constraints. Moreover, it transforms the microscale problem into an efficient, low-dimensional one without confining the design to predefined shapes. Compliance and shape matching examples using common truss geometries and diversity-based freeform topologies demonstrate the versatility of our framework, while studies on the effect of the number and diversity of classes illustrate the effectiveness. The generality of the proposed methods supports future extensions beyond the linear applications presented.
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