@inproceedings{3ab81a10b181405895b90bbc7f9c4a3e,
title = "Multi-domain Features Fusion Adaptive Neural Network Tool Wear Recognition Model",
abstract = "The condition of cutting tools is affecting the product quality, production cost and profit. Monitoring the condition correctly and accurately is very import in machining industry. In this paper, a tool wear recognition model based on adaptive neural networks with multi-domain feature fusion is presented. First, the vibration signals obtained from the sensors mounted on the working area is processed to generate the time-domain and frequency-domain features, which form a multi-dimension space. Then the core features are identified according to the distance criteria. Finally, LSTM neural network is used to determine the tool wear condition during the machining process by processing the core features. The model is verified by the data collected from industry practical experiments. The results shows that our model can successfully increase the precision of tool wear classification and has certain generalization ability under different working conditions compared with the single eigenvalue prediction method.",
keywords = "ANN adaptive neural network, CNC, Multi-domain features, Tool wear status",
author = "Hanyang Wang and Ming Luo and Fengshou Gu",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; International Conference of The Efficiency and Performance Engineering Network 2022, TEPEN 2022 ; Conference date: 18-08-2022 Through 21-08-2022",
year = "2023",
month = mar,
day = "4",
doi = "10.1007/978-3-031-26193-0_66",
language = "English",
isbn = "9783031261923",
volume = "129",
series = "Mechanisms and Machine Science",
publisher = "Springer, Cham",
pages = "751--765",
editor = "Hao Zhang and Yongjian Ji and Tongtong Liu and Xiuquan Sun and Ball, {Andrew David}",
booktitle = "Proceedings of TEPEN 2022",
address = "Switzerland",
}
