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HomeKey Engineering MaterialsKey Engineering Materials Vol. 1050Graph Neural Network for Draw-in Prediction in...

Graph Neural Network for Draw-in Prediction in Sheet Metal Stamping

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Abstract:

Draw-in distance is a key index for evaluating the quality of sheet metal stamping. Its accurate prediction is therefore required for tool design and process control. Traditional finite element (FE) simulations, while accurate, are computationally intensive and time-consuming for iterative design optimization. In this study, a graph neural network (GNN) method is proposed to predict draw-in during sheet metal forming. A dataset was built from FE simulations with different process settings, including blank holder force and draw bead force. The GNN model uses node coordinates and edge features to describe the spatial relations in the sheet. A multi-level loss function was applied. The coordinate error and edge distance error were included. In this way, the shape of the sheet is better preserved. The trained GNN can be used as a fast model for draw-in prediction. It can also be used for inverse analysis, where the process parameters are found from a given draw-in result. This provides an efficient tool for sheet metal forming design and optimization.

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Advanced Numerical and AI Strategies for Material Forming

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Periodical:

Key Engineering Materials (Volume 1050)

Pages:

137-144

DOI:

https://doi.org/10.4028/p-wKWw4q

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Online since:

April 2026

Authors:

Hui Wang*, Taegyun Ahn, Lu Huang, Seog Chan Oh, Liang Huang, Andrey Ilinich, Sathya Dev, Jeong Whan Yoon

Keywords:

Draw-In Prediction, Graph Neural Networks, Machine Learning, Sheet Metal Forming

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Creative Commons CC BY 4.0

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* - Corresponding Author

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