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Pregled bibliografske jedinice broj: 538480

Two approaches for estimation of production time : robust regression analysis and neural network


Lisjak, Dragutin; Ćosić, Predrag; Milčić, Diana
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, Taiwan, 26.-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 ) ( POIROT)
120-1521781-3116 - UTJECAJ PROCESA PROIZVODNJE NA KOMPETITIVNOST I ODRŽIVOST RAZVOJA (Ćosić, Predrag, MZOS ) ( POIROT)
128-1281955-1951 - Standardizacija ekološki prihvatljivih procesa grafičkih komunikacija (Milčić, Diana, MZOS ) ( POIROT)

Ustanove:
Fakultet strojarstva i brodogradnje, Zagreb,
Grafički fakultet, Zagreb

Profili:

Avatar Url Predrag Ćosić (autor)

Avatar Url Dragutin Lisjak (autor)

Avatar Url Diana Milčić (autor)


Citiraj ovu publikaciju:

Lisjak, Dragutin; Ćosić, Predrag; Milčić, Diana
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)
Lisjak, D., Ćosić, P. & Milčić, D. (2011) Two approaches for estimation of production time : robust regression analysis and neural network. U: F. Frank Chen & Yi-Chi Wang (ur.)Proceedings of the 21st International Conference on Flexible Automation and Intelligent Manufacturing (FAIM 2011).
@article{article, year = {2011}, pages = {27-34}, keywords = {production time, robust regression analysis, neural network}, isbn = {978-986-87291-0-0}, title = {Two approaches for estimation of production time : robust regression analysis and neural network}, keyword = {production time, robust regression analysis, neural network}, publisher = {FAIM 2011 and Society of Lean Enterprise Systems of Taiwan}, publisherplace = {Taichung, Taiwan} }
@article{article, year = {2011}, pages = {27-34}, keywords = {production time, robust regression analysis, neural network}, isbn = {978-986-87291-0-0}, title = {Two approaches for estimation of production time : robust regression analysis and neural network}, keyword = {production time, robust regression analysis, neural network}, publisher = {FAIM 2011 and Society of Lean Enterprise Systems of Taiwan}, publisherplace = {Taichung, Taiwan} }




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