Development of family of artificial neural networks for the prediction of cutting tool condition

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dc.citation.epage178
dc.citation.spage164
dc.citation.volume15
dc.contributor.authorSpaić, O.
dc.contributor.authorKrivokapić, Z.
dc.contributor.authorKramar, D.
dc.date.accessioned2023-07-04T09:48:36Z
dc.date.available2023-07-04T09:48:36Z
dc.date.issued2020
dc.description.abstractRecently, 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 func-tion 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.
dc.identifier.doi10.14743/apem2020.2.356
dc.identifier.urihttps://vaseljena.ues.rs.ba/handle/123456789/333
dc.language.isoen
dc.publisherFaculty of Mechanical Engineering, University of Maribor,Slovenia Faculty of Mechanical Engineering
dc.sourceAdvances in Production Engineering & Management
dc.subjectDrilling; Cutting tool; Twist drill bits; Axial force; Tool wear; Prediction; Artificial neural networks; Back propagation
dc.titleDevelopment of family of artificial neural networks for the prediction of cutting tool condition
dc.typeArticle
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