Pregled bibliografske jedinice broj: 308828
Improvement of the mathematical model of steel carburizing using neural network and genetic algorithm
Improvement of the mathematical model of steel carburizing using neural network and genetic algorithm // 12. svjetovanje o materijalima, tehnologijama, trenju i trošenju (MATRIB 2007) : zbornik radova = proceedings / Grilec, Krešimir (ur.).
Zagreb: Hrvatsko društvo za materijale i tribologiju (HDMT), 2007. str. 115-124 (predavanje, domaća recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 308828 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Improvement of the mathematical model of steel carburizing using neural network and genetic algorithm
Autori
Lisjak, Dragutin ; Novak, Davor ; Ištvanić, Denis
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
12. svjetovanje o materijalima, tehnologijama, trenju i trošenju (MATRIB 2007) : zbornik radova = proceedings
/ Grilec, Krešimir - Zagreb : Hrvatsko društvo za materijale i tribologiju (HDMT), 2007, 115-124
ISBN
978-953-7040-12-3
Skup
Savjetovanje o materijalima, tehnologijama, trenju i trošenju (12 ; 2007)
Mjesto i datum
Vela Luka, Hrvatska, 21.06.2007. - 23.06.2007
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Domaća recenzija
Ključne riječi
carburizing; neural networks; genetic algorithm
Sažetak
In the paper, using of neural network and genetic algorithm for calculating the laws of the complex processes (among which are diffusion processes at steel carburizing) is presented. For determining of technological parameters of carburizing which are necessary for obtaining the required flow of carbon curve in the carburized layer, simulation of mathematical model for Carbomaag carburizing process is presented. For training of the neural network, the results of the empirical carburizing model that was proven in practice were used and compared to the results of computer simulation of the mathematical model. Comparing the experimental data and simulation data, it was proven that neural network shows good generalization properties for estimating of time and carbon potential required for carburizing. Based on results of the neural network (NN), using genetic algorithm (GA), the experimental equation, which is a part of the mathematical model, showing influence of alloying elements to the flow of carburizing curve was improved. Introducing of the improved equation into the existing mathematical model enables achieving of the empirically required 0.6-0.8%C in the surface layer, at the required effective carburizing depth (Edp) about 0.35%C, which enables achieving of 550 HV1 hardness after quenching.
Izvorni jezik
Engleski
Znanstvena područja
Strojarstvo
POVEZANOST RADA
Projekti:
120-1201780-1779 - Modeliranje svojstava materijala i parametara procesa (Filetin, Tomislav, MZOS ) ( CroRIS)
Ustanove:
Fakultet strojarstva i brodogradnje, Zagreb
Profili:
Dragutin Lisjak
(autor)