Tool Wear Prediction Based on LSTM-MSCNN Fusion Model and Multi-source Time-frequency Characteristics

Tool Wear Prediction Based on LSTM-MSCNN Fusion Model and Multi-source Time-frequency Characteristics

Accurate tool wear prediction is pivotal for ensuring machining quality, extending tool lifespan, and reducing operational and maintenance costs. However, modelling the complex spatio-temporal dependencies in multi-source heterogeneous sensor data remains a major challenge. This study proposes LSTM...

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Bibliographic Details
Main Authors: Zhang, YanPing, Lee Chin, Kho, Feng, Xiansong, Zhang, Mingqiang, Yuan, Dongfeng
Format: Article
Language:en
Published: CURRENTSCIENCE 2025
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://ir.unimas.my/id/eprint/50333/1/Tool%20Wear%20Prediction%20Based%20on%20LSTM%20MSCNN%20Fusion%20Model.pdf
http://ir.unimas.my/id/eprint/50333/
https://currentscience.info/index.php/cs/article/view/1326
https://doi.org0/10.52845/CS/2025-5-5-35
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http://ir.unimas.my/id/eprint/50333/1/Tool%20Wear%20Prediction%20Based%20on%20LSTM%20MSCNN%20Fusion%20Model.pdf
http://ir.unimas.my/id/eprint/50333/
https://currentscience.info/index.php/cs/article/view/1326
https://doi.org0/10.52845/CS/2025-5-5-35

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