Abstract
Forming processes are known for their intricacies in prediction and control due to the complex loading conditions and material flow. This paper will first introduce the AI algorithms used or having potential to be used in forming, and then investigate the state-of-the-art advances of AI-based technologies in forming processes with four main pillars of process simulation, process design and optimization, in-situ process control, and qualification and certification of forming processes and formed products. Future directions of AI in forming for both academic research and industrial applications will be proposed to leverage digitalization and data science to explore new solutions in forming processes.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 561-587 |
| Number of pages | 27 |
| Journal | CIRP Annals |
| Volume | 73 |
| Issue number | 2 |
| DOIs | |
| State | Published - Jan 2024 |
Funding
Jian Cao (JC), Mojtaba Mozaffar and Tianju Xue were funded by the Department of Defense Vannevar Bush Faculty Fellowship to JC [USA N00014-19-1-2642]. Jian Cao also acknowledges the support from the NSF Engineering Research Center for Hybrid Autonomous Manufacturing, Moving from Evolution to Revolution (ERC-HAMMER) under Award Number EEC-2133630. Markus Bambach acknowledges funding from the German Research Foundation through grant BA4253/11\u2013-3. Marion Merklein acknowledges FOSTA project for the support. Authors would also like to thank Putong Kang, Clara-Maria Kuball, Michael Lechner, Raphaela Maerz, Daniel Quispe, Itzel Salgado, Manfred Vogel, Brett Wadman and Simon Wituschek for their contributions in organizing figures and tables and in analyzing papers. Jian Cao (JC), Mojtaba Mozaffar and Tianju Xue were funded by the Department of Defense Vannevar Bush Faculty Fellowship to JC [ USA N00014-19-1-2642 ]. Markus Bambach acknowledges funding from the German Research Foundation through grant BA4253/11-3 . Marion Merklein acknowledges FOSTA project for the support. Authors would also like to thank Putong Kang, Clara-Maria Kuball, Michael Lechner, Raphaela Maerz, Daniel Quispe, Itzel Salgado, Manfred Vogel, Brett Wadman and Simon Wituschek for their contributions in organizing figures and tables and in analyzing papers.
Keywords
- Artificial intelligence
- Machine learning
- Material characterization
- Process control
- Process design
ASJC Scopus subject areas
- Mechanical Engineering
- Industrial and Manufacturing Engineering