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.

基于深度学习的预测质量使制造公司能够根据过程数据对生产的产品质量进行数据驱动的预测。一个核心挑战是生产过程会受到持续变化的影响，例如新产品的制造，结果是先前训练的模型可能不再在过程中表现良好。在本文中，我们解决了这个问题，并提出了一种在此类预测质量场景中进行持续学习的方法。因此，我们调整并扩展了记忆感知突触方法，以在不同的产品变化中训练人工神经网络。我们对注塑成型中真实世界回归问题的评估表明，该方法成功地防止了神经网络忘记以前的任务，并提高了新任务的训练效率。此外，通过从类似的先前任务中转移网络权重来扩展该方法，我们显着提高了其在稀疏数据上的数据效率和性能。我们的代码是公开的，可用于重现我们的结果并以此为基础。