hrcak mascot   Srce   HID

Original scientific paper

Regression analysis and neural networks as methods for production time estimation

Predrag Ćosić ; University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, Ivana Lucića 5, 10000 Zagreb, Croatia
Dragutin Lisjak   ORCID icon orcid.org/0000-0002-9976-577X ; University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, Ivana Lucića 5, 10000 Zagreb, Croatia
Dražen Antolić ; ADPP, Ilirski trg 5, 10000 Zagreb, Croatia

Fulltext: english, pdf (808 KB) pages 479-484 downloads: 1.167* cite
APA 6th Edition
Ćosić, P., Lisjak, D. & Antolić, D. (2011). Regression analysis and neural networks as methods for production time estimation. Tehnički vjesnik, 18 (4), 479-484. Retrieved from https://hrcak.srce.hr/75393
MLA 8th Edition
Ćosić, Predrag, et al. "Regression analysis and neural networks as methods for production time estimation." Tehnički vjesnik, vol. 18, no. 4, 2011, pp. 479-484. https://hrcak.srce.hr/75393. Accessed 19 Oct. 2021.
Chicago 17th Edition
Ćosić, Predrag, Dragutin Lisjak and Dražen Antolić. "Regression analysis and neural networks as methods for production time estimation." Tehnički vjesnik 18, no. 4 (2011): 479-484. https://hrcak.srce.hr/75393
Harvard
Ćosić, P., Lisjak, D., and Antolić, D. (2011). 'Regression analysis and neural networks as methods for production time estimation', Tehnički vjesnik, 18(4), pp. 479-484. Available at: https://hrcak.srce.hr/75393 (Accessed 19 October 2021)
Vancouver
Ćosić P, Lisjak D, Antolić D. Regression analysis and neural networks as methods for production time estimation. Tehnički vjesnik [Internet]. 2011 [cited 2021 October 19];18(4):479-484. Available from: https://hrcak.srce.hr/75393
IEEE
P. Ćosić, D. Lisjak and D. Antolić, "Regression analysis and neural networks as methods for production time estimation", Tehnički vjesnik, vol.18, no. 4, pp. 479-484, 2011. [Online]. Available: https://hrcak.srce.hr/75393. [Accessed: 19 October 2021]
Fulltext: croatian, pdf (808 KB) pages 479-484 downloads: 516* cite
APA 6th Edition
Ćosić, P., Lisjak, D. & Antolić, D. (2011). Regresijska analiza i neuronske mreže kao metode za procjenu proizvodnog vremena. Tehnički vjesnik, 18 (4), 479-484. Retrieved from https://hrcak.srce.hr/75393
MLA 8th Edition
Ćosić, Predrag, et al. "Regresijska analiza i neuronske mreže kao metode za procjenu proizvodnog vremena." Tehnički vjesnik, vol. 18, no. 4, 2011, pp. 479-484. https://hrcak.srce.hr/75393. Accessed 19 Oct. 2021.
Chicago 17th Edition
Ćosić, Predrag, Dragutin Lisjak and Dražen Antolić. "Regresijska analiza i neuronske mreže kao metode za procjenu proizvodnog vremena." Tehnički vjesnik 18, no. 4 (2011): 479-484. https://hrcak.srce.hr/75393
Harvard
Ćosić, P., Lisjak, D., and Antolić, D. (2011). 'Regresijska analiza i neuronske mreže kao metode za procjenu proizvodnog vremena', Tehnički vjesnik, 18(4), pp. 479-484. Available at: https://hrcak.srce.hr/75393 (Accessed 19 October 2021)
Vancouver
Ćosić P, Lisjak D, Antolić D. Regresijska analiza i neuronske mreže kao metode za procjenu proizvodnog vremena. Tehnički vjesnik [Internet]. 2011 [cited 2021 October 19];18(4):479-484. Available from: https://hrcak.srce.hr/75393
IEEE
P. Ćosić, D. Lisjak and D. Antolić, "Regresijska analiza i neuronske mreže kao metode za procjenu proizvodnog vremena", Tehnički vjesnik, vol.18, no. 4, pp. 479-484, 2011. [Online]. Available: https://hrcak.srce.hr/75393. [Accessed: 19 October 2021]

Abstracts
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.

Keywords
group technology; knowledge base; neural networks; production time; stepwise multiple linear regression; Total Cost Estimation

Hrčak ID: 75393

URI
https://hrcak.srce.hr/75393

[croatian]

Visits: 2.372 *