Pregled bibliografske jedinice broj: 670423
Estimation of Production Time by Regression and Neural Networks
Estimation of Production Time by Regression and Neural Networks // 3rdInternational Scientific Conference, Management of Technology - Step to Sustainable Production MOTSP2011, 8-10 June 2011, Bol, Island Brac, Croatia, Conference Proceedings / Ćosić, Predrag (ur.).
Zagreb: Printera Grupa, 2011. str. 243-250 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 670423 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Estimation of Production Time by Regression and Neural Networks
Autori
Ćosić, Predrag ; Lisjak, Dragutin ; Antolić, Dražen
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
3rdInternational Scientific Conference, Management of Technology - Step to Sustainable Production MOTSP2011, 8-10 June 2011, Bol, Island Brac, Croatia, Conference Proceedings
/ Ćosić, Predrag - Zagreb : Printera Grupa, 2011, 243-250
ISBN
978-953-7738-10-5
Skup
3rdInternational Scientific Conference, Management of Technology - Step to Sustainable Production MOTSP2011
Mjesto i datum
Bol, otok Brač, Hrvatska, 08.06.2011. - 10.06.2011
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
stepwise multiple linear regression; group technology; knowledge base; production time; neural networks; TCE
Sažetak
The estimation of production times will be the necessary future basis for cost estimation, cost reduction or TCE (Total Cost Estimation). An experienced process planner usually makes decisions based on comprehensive data without breaking it down into individual parameters. So, as the first phase it was necessary to establish a technological knowledge base, define features of the 2D drawing (independent variables), possible dependent variables, size and criteria for sample homogenization (principles of group technology) for carrying out analysis of variance and regression analysis. The second phase in the research was to investigate the possibility for easy automatic, direct finding and applying 3D features of an axial symmetric product to the regression model. The third phase in the research was to investigate the possibility for the application of neural networks in production time estimation and to compare the 224 results between the regression models and neural network models. The most important characteristic of our approach presented in this paper is estimation of production times using group technology, regression analysis and neural networks.
Izvorni jezik
Engleski
Znanstvena područja
Strojarstvo
POVEZANOST RADA
Projekti:
120-1521781-3116 - UTJECAJ PROCESA PROIZVODNJE NA KOMPETITIVNOST I ODRŽIVOST RAZVOJA (Ćosić, Predrag, MZOS ) ( CroRIS)
Ustanove:
Fakultet strojarstva i brodogradnje, Zagreb
