Continual learning of neural networks for quality prediction in production using memory aware synapses and weight transfer

Deep learning-based predictive quality enables manufacturing companies to make data-driven predictions of the quality of a produced product based on process data. A central challenge is that production processes are subject to continuous changes such as the manufacturing of new products, with the result that previously trained models may no longer perform well in the process. In this paper, we address this problem and propose a method for continual learning in such predictive quality scenarios. We therefore adapt and extend the memory-aware synapses approach to train an artificial neural network across different product variations. Our evaluation in a real-world regression problem in injection molding shows that the approach successfully prevents the neural network from forgetting of previous tasks and improves the training efficiency for new tasks. Moreover, by extending the approach with the transfer of network weights from similar previous tasks, we significantly improve its data efficiency and performance on sparse data. Our code is publicly available to reproduce our results and build upon them.

Automated summary

This paper explores deep learning-based predictive quality and proposes a method for continual learning to adapt to the ongoing changes in manufacturing processes. The evaluation demonstrates that the approach successfully prevents the neural network from forgetting previous tasks, improves training efficiency for new tasks, and enhances performance on sparse data by transferring network weights from similar previous tasks. Our publicly available code allows for result reproducibility and further development.