Nature evolves structures like honeycombs at optimized performance with limited material. These efficient structures can be artificially created with the collaboration of structural topology optimization and additive manufacturing. However, the extensive computation cost of topology optimization causes low mesh resolution, long solving time, and rough boundaries that fail to match the requirements for meeting the growing personal fabrication demands and printing capability. Therefore, we propose the neural synthesizing topology optimization that leverages a self-supervised coordinate-based network to optimize structures with significantly shorter computation time, where the network encodes the structural material layout as an implicit function of coordinates. Continuous solution space is further generated from optimization tasks under varying boundary conditions or constraints for users' instant inference of novel solutions. We demonstrate the system's efficacy for a broad usage scenario through numerical experiments and 3D printing.
翻译:自然会演化出像蜂巢一样的,具有材料限制的高效结构。 这些高效的结构可以通过结构拓扑优化和增材制造的协作人工创建。 然而,拓扑优化的巨大计算成本导致了低网格分辨率、长求解时间和粗糙的边界,无法满足不断增长的个人制造需求和打印能力。 因此,我们提出了神经综合拓扑优化方法,利用自监督坐标网络来优化结构,大大缩短了计算时间,其中网络将结构材料布局编码为坐标的隐式函数。 根据不同的边界条件或约束生成连续的解空间,以便用户即时推断新解。 我们通过数值实验和3D打印证明了该系统在广泛的使用场景中的有效性。