Deep transfer learning with graph neural networks for the selection of manufacturing processes

The quality of process planning, linking product design and manufacturing, critically depends on selecting suitable manufacturing processes. In recent years, several deep learning approaches have been proposed for selecting manufacturing processes, frequently relying on synthetically created datasets. While such datasets allow models to capture general manufacturing patterns, they fail to reflect the complexity and variability of industrial 3D CAD models. As a result, the effectiveness of deep learning for predicting manufacturing processes is constrained by the limited availability of high-quality, annotated industrial data. To mitigate the effort for annotation of industrial data, this paper investigates deep transfer learning for the selection of manufacturing processes. Models are first pre-trained on a synthetically designed dataset and subsequently fine-tuned on smaller sets of real industrial data. The findings demonstrate that deep transfer learning enables improved performance in the selection of manufacturing processes.