ŠIMUNOVIĆ, Goran ;ŠARIĆ, Tomislav ;LUJIĆ, Roberto . Application of neural networks in evaluation of technological time. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 54, n.3, p. 179-188, august 2017. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/sl/article/application-of-neural-networks-in-evaluation-of-technological-time/>. Date accessed: 20 dec. 2024. doi:http://dx.doi.org/.
Šimunović, G., Šarić, T., & Lujić, R. (2008). Application of neural networks in evaluation of technological time. Strojniški vestnik - Journal of Mechanical Engineering, 54(3), 179-188. doi:http://dx.doi.org/
@article{.,
author = {Goran Šimunović and Tomislav Šarić and Roberto Lujić},
title = {Application of neural networks in evaluation of technological time},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {54},
number = {3},
year = {2008},
keywords = {process planning; artificial intelligence; neural networks; },
abstract = {The traditional approach to the process planning mostly based on experience of technologists, requires a lot of accumulated knowledge, is inflexible and time consuming. The application of artificial intelligence methods can supportand greatly improve this approach. This paper describes the results obtained by investigating the application of neural networks in evaluating the manufacturing parameters and, indirectly, technological time of the seam tube polishing. Various structures of a back-propagation neural network have been analysed and the optimum one with the minimum RMS (Root Mean Square) error selected. The obtained model was integrated into the ERP system (Enterprise Resource Planning system) of a manufacturing company. The more precise evaluations of technological time obtained by the ERP system model complete the previously defined manufacturing operations and form the basis for production planning and times of delivery control. The work of technologists is thus made easier and the production preparation technological time made shorter.},
issn = {0039-2480}, pages = {179-188}, doi = {},
url = {https://www.sv-jme.eu/sl/article/application-of-neural-networks-in-evaluation-of-technological-time/}
}
Šimunović, G.,Šarić, T.,Lujić, R. 2008 August 54. Application of neural networks in evaluation of technological time. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 54:3
%A Šimunović, Goran %A Šarić, Tomislav %A Lujić, Roberto %D 2008 %T Application of neural networks in evaluation of technological time %B 2008 %9 process planning; artificial intelligence; neural networks; %! Application of neural networks in evaluation of technological time %K process planning; artificial intelligence; neural networks; %X The traditional approach to the process planning mostly based on experience of technologists, requires a lot of accumulated knowledge, is inflexible and time consuming. The application of artificial intelligence methods can supportand greatly improve this approach. This paper describes the results obtained by investigating the application of neural networks in evaluating the manufacturing parameters and, indirectly, technological time of the seam tube polishing. Various structures of a back-propagation neural network have been analysed and the optimum one with the minimum RMS (Root Mean Square) error selected. The obtained model was integrated into the ERP system (Enterprise Resource Planning system) of a manufacturing company. The more precise evaluations of technological time obtained by the ERP system model complete the previously defined manufacturing operations and form the basis for production planning and times of delivery control. The work of technologists is thus made easier and the production preparation technological time made shorter. %U https://www.sv-jme.eu/sl/article/application-of-neural-networks-in-evaluation-of-technological-time/ %0 Journal Article %R %& 179 %P 10 %J Strojniški vestnik - Journal of Mechanical Engineering %V 54 %N 3 %@ 0039-2480 %8 2017-08-21 %7 2017-08-21
Šimunović, Goran, Tomislav Šarić, & Roberto Lujić. "Application of neural networks in evaluation of technological time." Strojniški vestnik - Journal of Mechanical Engineering [Online], 54.3 (2008): 179-188. Web. 20 Dec. 2024
TY - JOUR AU - Šimunović, Goran AU - Šarić, Tomislav AU - Lujić, Roberto PY - 2008 TI - Application of neural networks in evaluation of technological time JF - Strojniški vestnik - Journal of Mechanical Engineering DO - KW - process planning; artificial intelligence; neural networks; N2 - The traditional approach to the process planning mostly based on experience of technologists, requires a lot of accumulated knowledge, is inflexible and time consuming. The application of artificial intelligence methods can supportand greatly improve this approach. This paper describes the results obtained by investigating the application of neural networks in evaluating the manufacturing parameters and, indirectly, technological time of the seam tube polishing. Various structures of a back-propagation neural network have been analysed and the optimum one with the minimum RMS (Root Mean Square) error selected. The obtained model was integrated into the ERP system (Enterprise Resource Planning system) of a manufacturing company. The more precise evaluations of technological time obtained by the ERP system model complete the previously defined manufacturing operations and form the basis for production planning and times of delivery control. The work of technologists is thus made easier and the production preparation technological time made shorter. UR - https://www.sv-jme.eu/sl/article/application-of-neural-networks-in-evaluation-of-technological-time/
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author = {Šimunović, G., Šarić, T., Lujić, R.},
title = {Application of neural networks in evaluation of technological time},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {54},
number = {3},
year = {2008},
doi = {},
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TY - JOUR AU - Šimunović, Goran AU - Šarić, Tomislav AU - Lujić, Roberto PY - 2017/08/21 TI - Application of neural networks in evaluation of technological time JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 54, No 3 (2008): Strojniški vestnik - Journal of Mechanical Engineering DO - KW - process planning, artificial intelligence, neural networks, N2 - The traditional approach to the process planning mostly based on experience of technologists, requires a lot of accumulated knowledge, is inflexible and time consuming. The application of artificial intelligence methods can supportand greatly improve this approach. This paper describes the results obtained by investigating the application of neural networks in evaluating the manufacturing parameters and, indirectly, technological time of the seam tube polishing. Various structures of a back-propagation neural network have been analysed and the optimum one with the minimum RMS (Root Mean Square) error selected. The obtained model was integrated into the ERP system (Enterprise Resource Planning system) of a manufacturing company. The more precise evaluations of technological time obtained by the ERP system model complete the previously defined manufacturing operations and form the basis for production planning and times of delivery control. The work of technologists is thus made easier and the production preparation technological time made shorter. UR - https://www.sv-jme.eu/sl/article/application-of-neural-networks-in-evaluation-of-technological-time/
Šimunović, Goran, Šarić, Tomislav, AND Lujić, Roberto. "Application of neural networks in evaluation of technological time" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 54 Number 3 (21 August 2017)
Strojniški vestnik - Journal of Mechanical Engineering 54(2008)3, 179-188
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
The traditional approach to the process planning mostly based on experience of technologists, requires a lot of accumulated knowledge, is inflexible and time consuming. The application of artificial intelligence methods can supportand greatly improve this approach. This paper describes the results obtained by investigating the application of neural networks in evaluating the manufacturing parameters and, indirectly, technological time of the seam tube polishing. Various structures of a back-propagation neural network have been analysed and the optimum one with the minimum RMS (Root Mean Square) error selected. The obtained model was integrated into the ERP system (Enterprise Resource Planning system) of a manufacturing company. The more precise evaluations of technological time obtained by the ERP system model complete the previously defined manufacturing operations and form the basis for production planning and times of delivery control. The work of technologists is thus made easier and the production preparation technological time made shorter.