Fakulteta za strojništvo, Ljubljana (FSLJ)
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  • Development of family of artificial neural networks for the prediction of cutting tool condition
    Spaić, Obrad ; Krivokapić, Zdravko, strojnik ; Kramar, Davorin
    Recently, besides regression analysis, artificial neural networks (ANNs) are increasingly used to predict the state of tools. Nevertheless, simulations trained by cutting modes, material type and the ... method of sharpening twist drills (TD) and the drilling length from sharp to blunt as input parameters and axial drilling force and torque as output ANN parameters did not achieve the expected results. Therefore, in this paper a family of artificial neural networks (FANN) was developed to predict the axial force and drilling torque as a function of a number of influencing factors. The formation of the FANN took place in three phases, in each phase the neural networks formed were trained by drilling lengths until the drill bit was worn out and by a variable parameter, while the combinations of the other influencing parameters were taken as constant values. The results of the prediction obtained by applying the FANN were compared with the results obtained by regression analysis at the points of experimental results. The comparison confirmed that the FANN can be used as a very reliable method for predicting tool condition.
    Vir: Advances in production engineering & management. - ISSN 1854-6250 (Vol. 15, no. 2, Jun. 2020, str. 164-178)
    Vrsta gradiva - članek, sestavni del
    Leto - 2020
    Jezik - angleški
    COBISS.SI-ID - 49477379

    Povezava(-e):

    http://apem-journal.org/Archives/2020/Abstract-APEM15-2_164-178.html
    Digitalna knjižnica Slovenije - dLib.si
    DOI

vir: Advances in production engineering & management. - ISSN 1854-6250 (Vol. 15, no. 2, Jun. 2020, str. 164-178)

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