Issue |
MATEC Web Conf.
Volume 408, 2025
44th Conference of the International Deep Drawing Research Group (IDDRG 2025)
|
|
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Article Number | 01071 | |
Number of page(s) | 6 | |
Section | Full Papers | |
DOI | https://doi.org/10.1051/matecconf/202540801071 | |
Published online | 07 May 2025 |
Application of Recurrent Neural Networks in Uncertainty Analysis of Sheet Metal Forming
1
Institute of Science and Innovation in Mechanical and Industrial Engineering (INEGI),
Porto, Portugal
2
Faculty of Engineering of University of Porto (FEUP),
Porto, Portugal
3
Centre for Mechanical Engineering, Materials and Processes (CEMMPRE), Department of Mechanical Engineering, University of Coimbra,
Portugal
4
Centre for Mechanical Technology and Automation (TEMA), Mechanical Engineering Department, University of Aveiro,
Portugal
* Corresponding author: dcruz@inegi.up.pt
The quality of deep-drawn sheet metal components can be strongly influenced by different sources of uncertainty, such as variations in process conditions, deviations in tool geometry, and variations in material properties between coils. Identifying the underlying causes of forming defects remains a challenging and time-consuming task due to the complexity of the forming process. This study presents a machine learning (ML) framework for tracing sources of uncertainty in the forming of a cylindrical cup. By analysing key outputs from a standardized cylindrical cup test, including force-displacement sequences, earing evolution, and thickness distribution in different sections of the cup, the ML model aims to predict multiple sources of uncertainty. After identifying the principal sources of variation, numerical simulations using finite element analysis were performed to create a comprehensive dataset for the development and training of the ML model. The results from the simulated tests can be considered as sequential data, allowing their evaluation using recurrent neural networks (RNNs), which are particularly suited for modelling temporal or ordered datasets. The RNNs demonstrated strong performance in leveraging the temporal features of the forming data, achieving high accuracy in identifying the origins of uncertainty.
Key words: Sheet metal forming / Uncertainty quantification / Machine Learning (ML) / Recurrent Neural Networks (RNNs)
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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