Jiongzhi Yang, Srivatsa Harish, Candy Li, Hengduo Zhao, Brittney Antous, Pinar Acar*
CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1285-1302, 2021, DOI:10.32604/cmc.2021.016829
Abstract This paper presents a de-novo computational design method driven by deep reinforcement learning to achieve reliable predictions and optimum properties for periodic microstructures. With recent developments in 3-D printing, microstructures can have complex geometries and material phases fabricated to achieve targeted mechanical performance. These material property enhancements are promising in improving the mechanical, thermal, and dynamic performance in multiple engineering systems, ranging from energy harvesting applications to spacecraft components. The study investigates a novel and efficient computational framework that integrates deep reinforcement learning algorithms into finite element-based material simulations to quantitatively model and design 3-D printed periodic microstructures. These algorithms… More >
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