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 language | English |
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Title of host publication | Proceedings - 2020 IEEE International Conference on Engineering, Technology and Innovation, ICE/ITMC 2020 |
Publisher | IEEE |
ISBN (Electronic) | 9781728170374, 978-1-7281-7037-4 |
DOIs | |
Publication status | Published - Jun 2020 |
Event | 2020 IEEE International Conference on Engineering, Technology and Innovation, ICE/ITMC 2020 - Online, Cardiff, United Kingdom Duration: 15 Jun 2020 → 17 Jun 2020 |
Conference
Conference | 2020 IEEE International Conference on Engineering, Technology and Innovation, ICE/ITMC 2020 |
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Country/Territory | United Kingdom |
City | Cardiff |
Period | 15/06/20 → 17/06/20 |
Keywords
- artificial intelligence
- automation
- classification
- computer-aided-design
- design
- engineering
- geometry
- joining elements
- machine learning
- neural networks
- regression
- spot welding
- voxel
- n/a OA procedure