50 KB1642 KB35 KB199920252008Bajić, DraženCukor, GoranLela, Branimir12 straništevilka:5letnik:54str. 322-333ISSN:0039-2480COBISSID:10558491URN:URN:NBN:SI:doc-1N29YL4BenAssociation of Mechanical Engineers and Technicians of Slovenia et al.Zveza strojnih inženirjev in tehnikov Slovenije et al.Strojniški vestnikglobina rezahitrosthrapavostnevronske mrežepodajanjestruženjeExamination and modelling of the influence of cutting parameters on the cutting force and the surface roughness in longitudinal turning|This paper examines the influence of three cutting parameters on the surface roughness and the cutting force components in longitudinal turning. The cutting speed, the feed rate and the depth of cut have been taken as influential factors. Two modelling methodologies, namely regression analysis and neural networks, have been applied to experimentally determined data. Also, for both methodologies the ability of interpolation and extrapolation has been tested. Results obtained by neural network models have been compared to those obtained by regression models. Both methodologies give nearly similar results when interpolation is observed. However, regarding extrapolation neural network models give better results. In order to find the optimum values of the cutting parameters an optimization has been carried outV prispevku raziskujemo vpliv treh rezalnih parametrov na hrapavost površine in komponente rezalne sile pri vzdolžnem struženju. Kot faktorje vpliva smo upoštevali rezalno hitrost, podajalno razmerje in globino reza. Na podatkih, dobljenih s preizkusi, smo uporabili modelirni metodi, regresijsko analizo in nevronalne mreze. Za obe metodi smo tudi preizkusili zmoznost interpolacije in ekstrapolacije. Rezultate, pridobljene z modelom nevronalnih mrez smo primerjali s tistimi, pridobljenimi z regresijskim modelom. Z obema metodama dobimo, v primeru interpolacije, podobne rezultate. V primeru ekstrapolacije, pa dobimo z modelom nevronalnih mrež boljse rezultate. Da bi dobili optimalne vrednosti rezalnih parametrov, smo izvedli optimizacijoTEXTznanstveno časopisjejournalsSlovenian National E-content AggregatorNational and University Library of SloveniaUniverza v Ljubljani, Fakulteta za strojništvo