Comprehensive clustering-based topology optimization for connectable multi-scale additive manufacturing structures

Additive Manufacturing(2022)

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摘要
This paper develops a multi-scale topology optimization method that realizes optimized structural stiffness design while achieves inter-connectivity among the heterogeneous unit cells. Specifically, about the technical details, lattice structure topology optimization (LSTO) is conducted by optimizing the parameter field of the specially-designed multi-variable lattices, through which the optimized lattice parameters reflect the density and stress states of the associated macro-element. Then, the macro-elements with close lattice parameters are gathered into clusters, providing the initial guess for the next-step freeform optimization. Finally, multiscale topology optimization (MTO) through the inverse homogenization approach is performed to further design the unit cell structures. The unit cell structures for each cluster are forced to be identical to save homogenization-related computational resources and the interconnectivity is ensured due to the optimized and perfectly connected initial guess from LSTO. Using the proposed method, three classical numerical examples are studied that prove the effects of improved mechanical performance, ensured micro-structure inter-connectivity, and the affordable computing scale. Finally, mechanical tests are conducted to verify the design performance benefits of the proposed method.
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关键词
Topology optimization,Multiscale design,Homogenization,Design for additive manufacturing,Connectivity
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