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Publikacje Pracowników Politechniki Lubelskiej

MNiSW
140
Lista 2021
Status:
Autorzy: Kulisz Monika, Zagórski Ireneusz, Józwik Jerzy, Korpysa Jarosław
Dyscypliny:
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Rok wydania: 2022
Wersja dokumentu: Drukowana | Elektroniczna
Język: angielski
Numer czasopisma: 12
Wolumen/Tom: 15
Numer artykułu: 4277
Strony: 1 - 23
Web of Science® Times Cited: 3
Scopus® Cytowania: 3
Bazy: Web of Science | Scopus
Efekt badań statutowych NIE
Finansowanie: The project/research was financed in the framework of the project Lublin University of Technology—Regional Excellence Initiative, funded by the Polish Ministry of Science and Higher Education (contract no. 030/RID/2018/19).
Materiał konferencyjny: NIE
Publikacja OA: TAK
Licencja:
Sposób udostępnienia: Witryna wydawcy
Wersja tekstu: Ostateczna wersja opublikowana
Czas opublikowania: W momencie opublikowania
Data opublikowania w OA: 16 czerwca 2022
Abstrakty: angielski
The main purpose of the study was to define the machining conditions that ensure the best quality of the machined surface, low chip temperature in the cutting zone and favourable geometric features of chips when using monolithic two-teeth cutters made of HSS Co steel by PRECITOOL. As the subject of the research, samples with a predetermined geometry, made of AZ91D alloy, were selected. The rough milling process was performed on a DMU 65 MonoBlock vertical milling centre. The machinability of AZ91D magnesium alloy was analysed by determining machinability indices such as: 3D roughness parameters, chip temperature, chip shape and geometry. An increase in the feed per tooth fz and depth of cut ap parameters in most cases resulted in an increase in the values of the 3D surface roughness parameters. Increasing the analysed machining parameters did not significantly increase the instantaneous chip temperature. Chip ignition was not observed for the current cutting conditions. The conducted research proved that for the adopted conditions of machining, the chip temperature did not exceed the auto-ignition temperature. Modelling of cause-and-effect relationships between the variable technological parameters of machining fz and ap and the temperature in the cutting zone T, the spatial geometric structure of the 3D surface “Sa” and kurtosis “Sku” was performed with the use of artificial neural network modelling. During the simulation, MLP and RBF networks, various functions of neuron activation and various learning algorithms were used. The analysis of the obtained modelling results and the selection of the most appropriate network were performed on the basis of the quality of the learning and validation, as well as learning and validation error indices. It was shown that in the case of the analysed 3D roughness parameters (Sa and Sku), a better result was obtained for the MLP network, and in the case of maximum temperature, for the RBF network.