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.


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    Title :

    Milling Tool Wear Prediction Based on 1DCNN-LSTM


    Additional title:

    Lect.Notes Mechanical Engineering


    Contributors:
    Mo, John P.T. (editor) / Xia, Wanliang (author) / Zhou, Jin (author) / Jia, Wenju (author) / Guo, Miaoxian (author)

    Conference:

    Conference on Mechanical, Automotive and Materials Engineering ; 2022 December 16, 2022 - December 18, 2022



    Publication date :

    2023-08-06


    Size :

    15 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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