An innovative Multisource Lightweight Adaptive Replayed Online Deep Transfer Learning algorithm for tool wear monitoring

Accurately monitoring tool wear during the cutting process is crucial for ensuring the precision manufacturing of components and enhancing machining efficiency. However, even under the same cutting conditions, there are differences in the feature distributions between offline data and online data, l...

Full description

Saved in:
Bibliographic Details
Published in:Journal of manufacturing processes Vol. 124; pp. 261 - 281
Main Authors: Gao, Zhilie, Chen, Ni, Yang, Yinfei, Li, Liang
Format: Journal Article
Language:English
Published: Elsevier Ltd 30.08.2024
Subjects:
Batch normalization adaption
Lightweight convolution layer
Online deep transfer learning
Replay buffer
Tool wear monitoring
Batch normalization adaption
Online deep transfer learning
Tool wear monitoring
Lightweight convolution layer
Replay buffer
ISSN:1526-6125, 2212-4616
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Accurately monitoring tool wear during the cutting process is crucial for ensuring the precision manufacturing of components and enhancing machining efficiency. However, even under the same cutting conditions, there are differences in the feature distributions between offline data and online data, leading to poor monitoring effectiveness of traditional offline transfer learning algorithms. To effectively address this challenge, this paper proposes an innovative algorithm named MS-LARODTL (Multisource Lightweight Adaptive Replayed Online Deep Transfer Learning). In the MS-LARODTL algorithm, a lightweight convolution layer design is initially adopted, resulting in a reduction of 86.64 % in convolution time to enhance the algorithm's execution speed. Subsequently, online data features are replayed to adapt batch normalization layer parameters through a replay buffer, thereby improving the monitoring accuracy of the algorithm. Lastly, by replaying source data and online data features and leveraging Maximum Mean Discrepancy to align data features, the algorithm's monitoring accuracy is further enhanced. Experimental results distinctly demonstrate that the MS-LARODTL algorithm surpasses other multi-source transfer learning methods across all evaluation metrics on the self-constructed CNC milling dataset. These findings underscore the effectiveness and potential of the MS-LARODTL algorithm, emphasizing its importance in the design and application of tool wear monitoring algorithms.
ISSN:1526-6125
2212-4616
DOI:10.1016/j.jmapro.2024.05.050