Forming force prediction in double-sided incremental forming via GNN-based transfer learning

This paper proposes a transfer learning approach using graph neural networks (GNN) for predicting the forming force during double-sided incremental forming (DSIF) processes. In order to address the geometry complexity of DSIF parts, a GNN-based model was proposed to aggregate surface geometric information of DSIF parts and toolpaths. Furthermore, a transfer learning method was adopted to improve the prediction. The model was pre-trained on a dataset of previously formed DSIF parts with varying geometries. To address material and machine variations, the model was further trained on the initial few layers of the observed part for calibration and subsequently predicted the forming force in the vertical direction relative to the part's coordinate system for the rest of the layers of the observed part. The performance of our proposed approach was evaluated using experimental datasets from two different machines and different input materials, demonstrating the generality and effectiveness of the approach in forming force prediction.