Pregled bibliografske jedinice broj: 538480
Two approaches for estimation of production time : robust regression analysis and neural network
Two approaches for estimation of production time : robust regression analysis and neural network // Proceedings of the 21st International Conference on Flexible Automation and Intelligent Manufacturing (FAIM 2011) / F. Frank Chen ; Yi-Chi Wang (ur.).
Taichung: FAIM 2011 and Society of Lean Enterprise Systems of Taiwan, 2011. str. 27-34 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
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Naslov
Two approaches for estimation of production time : robust regression analysis and neural network
Autori
Lisjak, Dragutin ; Ćosić, Predrag ; Milčić, Diana
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings of the 21st International Conference on Flexible Automation and Intelligent Manufacturing (FAIM 2011)
/ F. Frank Chen ; Yi-Chi Wang - Taichung : FAIM 2011 and Society of Lean Enterprise Systems of Taiwan, 2011, 27-34
ISBN
978-986-87291-0-0
Skup
International Conference on Flexible Automation and Intelligent Manufacturing (21 ; 2011)
Mjesto i datum
Taichung, Tajvan, 26.06.2011. - 29.06.2011
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
production time; robust regression analysis; neural network
Sažetak
A robust regression analysis model as a possible approach to time/cost estimation is used for the estimation of requested results based on the previous stochastic results and experiments. The requests for classification consideration of the product shape and process sequencing are important conditions for designing a general model for the estimation of production times. In fact, it means development of a technological knowledge base. As a result of our analysis, we created eight regression equations with the obtained index of determination, with the most important independent variables different for 2D and 3D model respectively. The observed level of subjectivity, constraints and errors were the reasons to use neural networks as the second approach to estimate production times. According to the presented results, we can conclude that the assumption on the use of a neural network for the production time estimation in relation to a robust regression analysis model is justified. For all experimental models the applied backpropagation neural network gives better values of key performance indexes (R, R2, RMSE, NRMSE).
Izvorni jezik
Engleski
Znanstvena područja
Strojarstvo
POVEZANOST RADA
Projekti:
120-1201780-1779 - Modeliranje svojstava materijala i parametara procesa (Filetin, Tomislav, MZOS ) ( CroRIS)
120-1521781-3116 - UTJECAJ PROCESA PROIZVODNJE NA KOMPETITIVNOST I ODRŽIVOST RAZVOJA (Ćosić, Predrag, MZOS ) ( CroRIS)
128-1281955-1951 - Standardizacija ekološki prihvatljivih procesa grafičkih komunikacija (Milčić, Diana, MZOS ) ( CroRIS)
Ustanove:
Fakultet strojarstva i brodogradnje, Zagreb,
Grafički fakultet, Zagreb