As incremental forming is a relatively new sheet metal forming process, very limited analytical and finite element prediction models are available in literature to study the process mechanics and improve its performance. Thus, most studies involve many trial-and-error iterations to optimize the processing conditions in order to take advantage of high process flexibility and material formability. However, reducing efforts of trial-and-error iterations is of utmost importance to make a process financially viable. Therefore, an FE model is developed and experimentally validated to predict the forming forces involved in incremental micro-forming process. Different mass scaling factors and element-types are used to optimize and develop the model for accurate prediction in the least possible computation time.
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Forming force prediction in double-sided incremental forming via GNN-based transfer learning
- Award ID(s):
- 2133630
- PAR ID:
- 10532731
- Publisher / Repository:
- Manufacturing Letters
- Date Published:
- Journal Name:
- Journal of Manufacturing Processes
- Volume:
- 120
- Issue:
- C
- ISSN:
- 1526-6125
- Page Range / eLocation ID:
- 867 to 877
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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