Automated joining element design by predicting spot-weld locations using 3D convolutional neural networks

Derk H.D. Eggink, Daniel F. Perez-Ramirez, Marco W. Groll

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

Abstract

Joining element design is mainly a manual task resulting in costly and prolonged development trajectories. Current limited automation solutions support engineers, but still lead to repetitive tasks and design iterations. Machine learning finds and exploits patterns in data to predict designs enabling engineers to focus on core competencies. This work proposes a novel methodology to predict joining element locations using machine learning. It describes two approaches to predict specifically spot-weld locations using voxels as data representation. The study presents a regression and classification concept with 3D fully convolutional neural networks. Coordinate-based performance measurements enable to compare and evaluate models regardless of learning tasks or data structures. Results indicate that both concepts can accurately predict joining locations by only considering geometry.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE International Conference on Engineering, Technology and Innovation, ICE/ITMC 2020
PublisherIEEE
ISBN (Electronic)9781728170374, 978-1-7281-7037-4
DOIs
Publication statusPublished - Jun 2020
Event2020 IEEE International Conference on Engineering, Technology and Innovation, ICE/ITMC 2020 - Online, Cardiff, United Kingdom
Duration: 15 Jun 202017 Jun 2020

Conference

Conference2020 IEEE International Conference on Engineering, Technology and Innovation, ICE/ITMC 2020
CountryUnited Kingdom
CityCardiff
Period15/06/2017/06/20

Keywords

  • artificial intelligence
  • automation
  • classification
  • computer-aided-design
  • design
  • engineering
  • geometry
  • joining elements
  • machine learning
  • neural networks
  • regression
  • spot welding
  • voxel

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