Generative adversarial networks for tolerance analysis

Schleich B, Qie Y, Wartzack S, Anwer N (2022)

Publication Language: English

Publication Type: Journal article, Original article

Publication year: 2022

Journal

CIRP Annals - Manufacturing Technology Elsevier

Book Volume: 71

Pages Range: 133-136

Journal Issue: 1

DOI: 10.1016/j.cirp.2022.03.021

Open Access Link: https://doi.org/10.1016/j.cirp.2022.03.021

Abstract

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.

Authors with CRIS profile

Benjamin Schleich Chair of Engineering Design Sandro Wartzack Chair of Engineering Design

Involved external institutions

École Normale Supérieure de Cachan FR France (FR)

How to cite

APA:

Schleich, B., Qie, Y., Wartzack, S., & Anwer, N. (2022). Generative adversarial networks for tolerance analysis. CIRP Annals - Manufacturing Technology, 71(1), 133-136. https://doi.org/10.1016/j.cirp.2022.03.021

MLA:

Schleich, Benjamin, et al. "Generative adversarial networks for tolerance analysis." CIRP Annals - Manufacturing Technology 71.1 (2022): 133-136.

BibTeX: Download