Generative adversarial networks for tolerance analysis

被引：14

作者：

Schleich, Benjamin ^{[1
]}

Qie, Yifan ^{[2
]}

Wartzack, Sandro ^{[1
]}

Anwer, Nabil ^{[2
]}

机构：

[1] Friedrich Alexander Univ Erlangen Nurnberg, Engn Design, Martensstr 9, D-91058 Erlangen, Germany

[2] Univ Paris Saclay, LURPA, ENS Paris Saclay, F-91190 Gif Sur Yvette, France

来源：

CIRP ANNALS-MANUFACTURING TECHNOLOGY | 2022年 / 71卷 / 01期

关键词：

Design; Tolerancing; Machine learning; SKIN MODEL SHAPES;

D O I：

10.1016/j.cirp.2022.03.021

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Many activities in design and manufacturing rely on realistic product representations considering geometrical deviations to assess their effects on the product function and quality. Though several approaches for tolerance analysis have been developed, they imply several shortcomings, such as the lack of form deviations consideration and the high manual modelling effort. In this paper, a novel shape-agnostic approach supported by generative adversarial networks is developed for the automated generation of part representatives with geometrical deviations. A workflow for generating these variational part representatives is highlighted and tolerance analysis case studies demonstrate the effectiveness of the proposed approach. (c) 2022 The Author(s). Published by Elsevier Ltd on behalf of CIRP. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

引用

页码：133 / 136

页数：4

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