Generative adversarial network for early-stage design flexibility in topology optimization for additive manufacturing. (April 2021)

• Record Type: Journal Article
• Authors: Hertlein, Nathan; Buskohl, Philip R.; Gillman, Andrew; Vemaganti, Kumar; Anand, Sam
• Is Part Of: Journal of manufacturing systems. Volume 59(2021)
• Page Start: 675
• Page End: 685
• Publication Date: 2021-04
• Subjects: Generative adversarial network -- Deep learning -- Topology optimization -- Additive manufacturing; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2021.04.007
• Languages: English
• ISSNs: 0278-6125

Abstract

Abstract: 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.