Transfer learning for predictive quality in laser-induced plasma micro-machining
Transfer learning for predictive quality in laser-induced plasma micro-machining
复制标题
激光诱导等离子体微加工中预测质量的迁移学习
DOI：
10.1016/j.mfglet.2023.08.143
复制
发表时间：
2023
影响因子：
3.9
通讯作者：
Guo, Weihong
中科院分区：
文献类型：
--
作者：
Chen, Mengfei;Malhotra, Rajiv;Guo, Weihong
In laser-induced plasma micromachining (LIPMM), a focused, ultrashort pulsed laser beam creates a highly localized plasma zone within a transparent liquid dielectric. When the beam intensity is greater than the breakdown threshold in the dielectric media, plasma is formed, which is then used to ablate the workpiece. This paper aims to facilitate in situ process monitoring and quality prediction for LIPMM by developing a deep learning model to (1) understand the relationship between acoustic emission data and quality of micromachining with LIPMM, (2) transfer such understanding across different process parameters, and (3) predict quality accurately by fine-tuning models with a smaller dataset. Experiments and results show that the relationship learned from one process parameter can be transferred to other parameters, requiring lesser data and lesser computational time for training the model. We investigate the feasibility of transfer learning and compare the performance of various transfer learning models: different input features, different convolutional neural network (CNN) structures, and the same structure with different fine-tuned layers. The findings provide insights into how to design effective transfer learning models for manufacturing applications.
