For modern metalworking machine tool systems, which operate with limited operator participation, there is a need to develop methods for automatic control and recognition of the states of cutting tools. It provides the possibility of predicting their residual resource and timely replacement. In recognizing the cutting tools (CT) states, data mining methods are increasingly used: decision trees, genetic algorithms, fuzzy logic, and neural networks. Therefore, the task of improving the quality of CT wear zones textures classes’ recognition (based on convolutional models’ application) is relevant. The article aims to create a new fuzzy neural net classifier to improve the quality of cutting tool wear zones recognition texture classes. The scientific novelty consists in creating convolutional models and a new fuzzy neural net classifier of CT wear zone textures – for recognizing wear mechanisms on the flanks of cutters in finishing turning conditions. Practical usefulness consists in improving the quality of textures classes of CT wear zones recognition to the level of almost 100%. An example of a corrective influence on the turning process is a change in the feed rate S – in the case of concentrated wear traces recognition on the near-vertex section of the cutter’s flank surface.


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    Titel :

    Improvement of the Quality of Wear Zones for Cutting Tools Textures Classes Recognition Based on Convolutional Models


    Weitere Titelangaben:

    Lect.Notes Mechanical Engineering


    Beteiligte:
    Ivanov, Vitalii (Herausgeber:in) / Trojanowska, Justyna (Herausgeber:in) / Pavlenko, Ivan (Herausgeber:in) / Rauch, Erwin (Herausgeber:in) / Piteľ, Ján (Herausgeber:in) / Fomin, Oleksandr (Autor:in) / Derevianchenko, Oleksandr (Autor:in) / Volkova, Natalya (Autor:in) / Skrypnyk, Natalia (Autor:in)

    Kongress:

    Design, Simulation, Manufacturing: The Innovation Exchange ; 2023 ; High Tatras, Slovakia June 06, 2023 - June 09, 2023



    Erscheinungsdatum :

    2023-05-23


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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