The real-time monitoring of tool wear is critical to ensure the high-quality machining. However, there are some problems in machining condition monitoring, such as large amount of data, redundancy of machining information and insufficient prediction accuracy, which affect the reliability of the tool wear monitoring process. This paper presents tool wear monitoring with multi-sensor instrument and tool wear compensation in milling process. Through the optimal feature combination of fusion signals, a tool wear prediction model based on 1DCNN-LSTM is constructed. Compared with the tool wear prediction model without feature selection, the prediction accuracy and calculation efficiency of 1DCNN-LSTM model are significantly improved.
Milling Tool Wear Prediction Based on 1DCNN-LSTM
Lect.Notes Mechanical Engineering
Conference on Mechanical, Automotive and Materials Engineering ; 2022 December 16, 2022 - December 18, 2022
Proceedings of the 8th International Conference on Mechanical, Automotive and Materials Engineering ; Kapitel : 7 ; 77-91
06.08.2023
15 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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