Artificial Neural Networks are one of the tools used to predict tool wear or surface roughness in machining. Model development is applicable when regression models do not give satisfactory results. In this work, Multilayer Perceptron networks were used to predict the tool flank wear and tool corner wear during milling of Aluminium Matrix Composite with 10% of SiC content. The signals of vibration acceleration and cutting forces were selected as input to the network and the tests were carried out with three cutting speeds. Based on the analysis of the developed models, the models with the best efficiency were selected and the quality of wear prediction was assessed.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

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


    Beteiligte:


    Erscheinungsdatum :

    01.06.2020


    Format / Umfang :

    1106994 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Milling Tool Wear Prediction Based on 1DCNN-LSTM

    Xia, Wanliang / Zhou, Jin / Jia, Wenju et al. | Springer Verlag | 2023



    Helicopter Noise Prediction Using an Artificial Neural Network Tool

    Cenedese, F. / American Institute of Aeronautics and Astronautics | British Library Conference Proceedings | 2007


    MULTIPLE REGRESSION AND COMMITTEE NEURAL NETWORK FORCE PREDICTION MODELS IN MILLING FRP

    Sheikh-Ahmad, J. / Twomey, J. / Kalla, D. et al. | British Library Online Contents | 2007


    Aluminium matrix composite

    Emerald Group Publishing | 1998