contributor author | Chen, Wei (Wayne) | |
contributor author | Lee, Doksoo | |
contributor author | Balogun, Oluwaseyi | |
contributor author | Chen, Wei | |
date accessioned | 2023-11-29T19:28:09Z | |
date available | 2023-11-29T19:28:09Z | |
date copyright | 10/31/2022 12:00:00 AM | |
date issued | 10/31/2022 12:00:00 AM | |
date issued | 2022-10-31 | |
identifier issn | 1050-0472 | |
identifier other | md_145_1_011703.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4294782 | |
description abstract | Deep generative models have demonstrated effectiveness in learning compact and expressive design representations that significantly improve geometric design optimization. However, these models do not consider the uncertainty introduced by manufacturing or fabrication. The past work that quantifies such uncertainty often makes simplifying assumptions on geometric variations, while the “real-world,” “free-form” uncertainty and its impact on design performance are difficult to quantify due to the high dimensionality. To address this issue, we propose a generative adversarial network-based design under uncertainty framework (GAN-DUF), which contains a deep generative model that simultaneously learns a compact representation of nominal (ideal) designs and the conditional distribution of fabricated designs given any nominal design. This opens up new possibilities of (1) building a universal uncertainty quantification model compatible with both shape and topological designs, (2) modeling free-form geometric uncertainties without the need to make any assumptions on the distribution of geometric variability, and (3) allowing fast prediction of uncertainties for new nominal designs. We can combine the proposed deep generative model with robust design optimization or reliability-based design optimization for design under uncertainty. We demonstrated the framework on two real-world engineering design examples and showed its capability of finding the solution that possesses better performance after fabrication. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | GAN-DUF: Hierarchical Deep Generative Models for Design Under Free-Form Geometric Uncertainty | |
type | Journal Paper | |
journal volume | 145 | |
journal issue | 1 | |
journal title | Journal of Mechanical Design | |
identifier doi | 10.1115/1.4055898 | |
journal fristpage | 11703-1 | |
journal lastpage | 11703-11 | |
page | 11 | |
tree | Journal of Mechanical Design:;2022:;volume( 145 ):;issue: 001 | |
contenttype | Fulltext | |