Topology optimization has become a valuable design tool for structures to be fabricated by additive manufacturing (AM). However, during early stage design, parameters are frequently evolving, resulting in multiple similar TO runs. Especially when design for manufacturing principles expand the parameter space, this iterative process is computationally burdensome, and does not take advantage of redundant information in each study. We introduce a deep learning-based framework that learns latent similarities between runs in a training set to predict near optimal designs, enabling efficient wholistic understanding of the problem setup space, which includes both loading conditions and, for the first time in this study, manufacturing process configuration. Learning was achieved using a conditional generative adversarial network (cGAN) trained on a dataset of randomized boundary conditions, loadings, and AM build orientations, and the corresponding optimal structures obtained through overhang-filtered TO. cGAN predictions showed good agreement with true optima. For even greater accuracy, predictions can be post-processed by applying a small number of TO iterations. Manifold learning techniques were used to provide further insight, and we were able to conclude that the cGAN error generally increases with distance between the load and the boundary conditions or build plate. Interestingly, in 9% of test cases, the cGAN generated structures with compliances better than the corresponding TO-calculated structures, often by as much as 50 % with an average of 7.8 %. That some of these structures appeared qualitatively different in form suggests the potential value of the approach in other domains such as generative design, where a range of alternate near-optimal designs are used to guide the ideation process.

拓扑优化已成为要通过增材制造（AM）制造的结构的有价值的设计工具。但是，在早期设计期间，参数会不断发展，从而导致多个类似的TO运行。特别是当制造原则的设计扩展了参数空间时，此迭代过程在计算上很繁琐，并且在每次研究中都没有利用冗余信息。我们引入了一个基于深度学习的框架，该框架可学习训练集中各次运行之间的潜在相似性，以预测接近最佳的设计，从而能够对问题设置空间进行高效的全面了解，其中包括加载条件以及本研究中首次涉及的制造流程配置。使用条件生成对抗网络（cGAN）在随机边界条件，载荷和AM构造方向的数据集上训练的条件生成对抗网络，以及通过悬垂过滤的TO获得的相应最佳结构，从而实现了学习。cGAN的预测与真实的最优值显示出良好的一致性。为了获得更高的准确性，可以通过应用少量的TO迭代来对预测进行后处理。流形学习技术用于提供进一步的见解，我们可以得出结论，cGAN误差通常随载荷与边界条件或构造板之间的距离而增加。有趣的是，在9％的测试用例中，cGAN生成的结构的顺应性优于相应的TO计算结构，通常高达50％，平均为7.8％。