The significant advance in the boosted fabrication speed and printing resolution of additive technology has considerably increased the capability of achieving product designs with high geometric complexity. The prefabrication computation has been increasingly important and is coming to be the bottleneck in the additive manufacturing process. In this paper, the authors devise an integrated computational framework by synthesizing the parametric level set-based topology optimization method with the DLP-based SLA process for intelligent design and additive manufacturing of not only single material structures but also multi-scale, multi-functional structures. The topology of the design is optimized with a new distance-regularized parametric level set method considering the prefabrication computation. offering the flexibility and robustness of the structural design that the conventional methods could not provide. The output of the framework is a set of mask images which can be directly used in the additive manufacturing process. The proposed approach seamlessly integrates the rational design and manufacturing to reduce the complexity of the computationally-expensive prefabrication process. Two test examples, including a freeform 3D cantilever beam and a multi-scale meta-structure, are utilized to demonstrate the performance of the proposed approach. Both the simulation and experimental results verified that the new rational design could significantly reduce the prefabrication computation cost without affecting the original design intent or sacrificing original functionality.
- Manufacturing Engineering Division
Parametric Topology Optimization Toward Rational Design and Efficient Prefabrication for Additive Manufacturing
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Jiang, L, Ye, H, Zhou, C, Chen, S, & Xu, W. "Parametric Topology Optimization Toward Rational Design and Efficient Prefabrication for Additive Manufacturing." Proceedings of the ASME 2017 12th International Manufacturing Science and Engineering Conference collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing. Volume 4: Bio and Sustainable Manufacturing. Los Angeles, California, USA. June 4–8, 2017. V004T05A006. ASME. https://doi.org/10.1115/MSEC2017-2954
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