Napredna pretraga

Pregled bibliografske jedinice broj: 482353

Analyzing and Modeling of the Influence of Cutting Parameters on the Cutting Force in Face Milling


Bajić, Dražen; Celent, Luka; Jozić, Sonja
Analyzing and Modeling of the Influence of Cutting Parameters on the Cutting Force in Face Milling // Trends in the Development of Machinery and Associated Technology - TMT 2010 / Sabahudin Ekinović, Yildirim Uctug, Joan Vivancos Calvet (ur.).
Zenica, BiH: Faculty of Mechanical Engineering in Zenica, 2010. str. 17-20 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


Naslov
Analyzing and Modeling of the Influence of Cutting Parameters on the Cutting Force in Face Milling

Autori
Bajić, Dražen ; Celent, Luka ; Jozić, Sonja

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Trends in the Development of Machinery and Associated Technology - TMT 2010 / Sabahudin Ekinović, Yildirim Uctug, Joan Vivancos Calvet - Zenica, BiH : Faculty of Mechanical Engineering in Zenica, 2010, 17-20

Skup
14th International Research/Expert Conference

Mjesto i datum
Mediteran (krstarenje), 11-18.09.2010

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Face milling; cutting force; regression analysis; radial basis function neural network

Sažetak
The general manufacturing problem can be described as the achievement of a predefined product quality with given equipment, cost and time constraints. This paper examines the influence of three cutting parameters on the cutting force components during face milling of steel 42CrMo4. The cutting speed, the feed per tooth and the depth of cut have been taken as influential factors. Two modeling methodologies, namely regression analysis and neural networks, have been applied to experimentally determined data. Results obtained by the models have been compared. The research has shown that whit small training dataset, neural networks modeling methodologies are comparable with regression analysis methodology and it can even offer better result, in this case average relative error of 2, 62%.

Izvorni jezik
Engleski

Znanstvena područja
Strojarstvo



POVEZANOST RADA


Projekt / tema
023-0692976-1742 - Istraživanje visokobrzinske obrade materijala (Dražen Bajić, )

Ustanove
Fakultet elektrotehnike, strojarstva i brodogradnje, Split