Knowledge Distillation-enabled Multi-stage Incremental Learning for Online Process Monitoring in Advanced Manufacturing

Zhangyue Shi, Yuxuan Li, Chenang Liu
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引用次数: 2

Abstract

In advanced manufacturing, the incorporation of online sensing technologies has enabled great potentials to achieve effective in-situ process monitoring via machine learning-based approaches. In manufacturing practice, the online sensor data are usually collected in a progressive manner, and the stream data collected at latter stages may also contain informative knowledge for process monitoring. Therefore, it is highly valuable to make the machine learning-based monitoring model learn incrementally in manufacturing. To achieve this goal, this paper develops a multi-stage incremental learning approach enabled by the knowledge distillation, which distills representative information from the machine learning model trained at early/offline stage and then enhances the monitoring performance at the latter stages. To validate its effectiveness, a real-world case study in additive manufacturing, which is an emerging advanced manufacturing technology, is conducted. The experimental results show that the developed knowledge distillation-enabled multi-stage incremental learning is very promising to improve the online monitoring performance in advanced manufacturing.
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基于知识提炼的先进制造过程在线监控多阶段增量学习
在先进制造业中,在线传感技术的结合使得通过基于机器学习的方法实现有效的原位过程监测具有很大的潜力。在制造实践中,在线传感器数据通常以渐进的方式收集,后期收集的流数据也可能包含用于过程监控的信息性知识。因此,使基于机器学习的监控模型在制造业中实现渐进式学习具有重要的应用价值。为了实现这一目标,本文开发了一种基于知识蒸馏的多阶段增量学习方法,该方法从早期/离线阶段训练的机器学习模型中提取有代表性的信息,然后增强后期阶段的监控性能。为了验证其有效性,对增材制造这一新兴的先进制造技术进行了实际案例研究。实验结果表明,本文提出的基于知识提炼的多阶段增量学习方法对提高先进制造业在线监测性能有很大的帮助。
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