Generative Adversarial Networks for spot weld design

Tobias Gerlach*, Derk H.D. Eggink

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)

Abstract

Joining element and assembly design remain largely a manual process. This increases risks of more costly and longer development trajectories. Current automation solutions do not consider historical data and traditional machine learning approaches have limitations. Meanwhile, generative adversary networks became benchmark methodologies to perform generation tasks in computer vision. Products in manufacturing industry may contain thousands of spot welds, thus design automation enables engineers to focus on their core competencies. This work presents a methodology to predict spot weld locations using generative adversarial networks. A 2D-based approach implements a variant of StarGAN_v2 to predict locations. It uses domain-based prediction concepts that integrate clustering of geometrical and product manufacturing information, as well as reference guided style generation. Results indicate that generative adversarial networks can predict spot weld positions based on 2D image data.
Original languageEnglish
Title of host publication2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA )
Number of pages8
Volume26
ISBN (Electronic)978-1-7281-2989-1
DOIs
Publication statusPublished - 30 Nov 2021
Externally publishedYes
Event26th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2021 - online, Vasteras, Sweden
Duration: 7 Sept 202110 Sept 2021
Conference number: 26

Publication series

NameIEEE International Conference on Emerging Technologies and Factory Automation
ISSN (Print)1946-0740

Conference

Conference26th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2021
Abbreviated titleETFA 2021
Country/TerritorySweden
CityVasteras
Period7/09/2110/09/21

Keywords

  • n/a OA procedure

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