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.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Milling Tool Wear Prediction Based on 1DCNN-LSTM


    Weitere Titelangaben:

    Lect.Notes Mechanical Engineering


    Beteiligte:
    Mo, John P.T. (Herausgeber:in) / Xia, Wanliang (Autor:in) / Zhou, Jin (Autor:in) / Jia, Wenju (Autor:in) / Guo, Miaoxian (Autor:in)

    Kongress:

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



    Erscheinungsdatum :

    06.08.2023


    Format / Umfang :

    15 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Turnout fault diagnosis method and system based on 1DCNN-LSTM

    FU YATING / WEN SHIMING / YANG HUI et al. | Europäisches Patentamt | 2023

    Freier Zugriff

    Electric bicycle battery identification method based on fryze theory and 1DCNN

    ZHU YONG / WANG CANHUA / CEN ZHENGJUN et al. | Europäisches Patentamt | 2023

    Freier Zugriff

    Artificial Neural Network models for tool wear prediction during Aluminium Matrix Composite milling

    Wiciak-Pikula, Martyna / Felusiak, Agata / Twardowski, Pawel | IEEE | 2020


    LSTM LSTM-based future threat prediction method and apparatus

    PARK YOUNG TACK / JEON MYUNG JOONG / KIM MIN SUNG et al. | Europäisches Patentamt | 2021

    Freier Zugriff

    Pedstrian Trajectory Prediction Based on LSTM

    Li, Shaosong / Jiang, Junchen / Zhou, Qingbin et al. | IEEE | 2023