Knowledge Distillation-enabled Multi-stage Incremental Learning for Online Process Monitoring in Advanced Manufacturing
Zhangyue Shi, Yuxuan Li, Chenang Liu · 2022 IEEE International Conference on Data Mining Workshops (ICDMW) · 2022
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.