Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/287161 
Year of Publication: 
2021
Citation: 
[Journal:] Journal of Intelligent Manufacturing [ISSN:] 1572-8145 [Volume:] 33 [Issue:] 1 [Publisher:] Springer US [Place:] New York, NY [Year:] 2021 [Pages:] 283-292
Publisher: 
Springer US, New York, NY
Abstract: 
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.
Subjects: 
Continual learning
Deep learning
Artificial intelligence
Manufacturing
Predictive quality
Regression
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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