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Scientific paper - Original scientific paper
Estimation of CNC Grinding Process Parameters Using Different Neural Networks
Tehnički vjesnik : znanstveno-stručni časopis tehničkih fakulteta Sveučilišta u Osijeku, 25 (2018), 6; 1770-1775. https://doi.org/10.17559/TV-20180419095119
Šarić, Tomislav; Šimunović, Goran; Vukelić, Đorđe; Šimunović, Katica; Lujić, Roberto

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Šarić, T., Šimunović, G., Vukelić, Đ., Šimunović, K. & Lujić, R. (2018). Estimation of CNC Grinding Process Parameters Using Different Neural Networks. Tehnički vjesnik : znanstveno-stručni časopis tehničkih fakulteta Sveučilišta u Osijeku, 25. (6), 1770-1775. doi: 10.17559/TV-20180419095119

Šarić, Tomislav, et al. "Estimation of CNC Grinding Process Parameters Using Different Neural Networks." Tehnički vjesnik : znanstveno-stručni časopis tehničkih fakulteta Sveučilišta u Osijeku, vol. 25, no. 6, 2018, pp. 1770-1775. https://doi.org/10.17559/TV-20180419095119

Šarić, Tomislav, Goran Šimunović, Đorđe Vukelić, Katica Šimunović and Roberto Lujić. "Estimation of CNC Grinding Process Parameters Using Different Neural Networks." Tehnički vjesnik : znanstveno-stručni časopis tehničkih fakulteta Sveučilišta u Osijeku 25, no. 6 (2018): 1770-1775. https://doi.org/10.17559/TV-20180419095119

Šarić, T., et al. (2018) 'Estimation of CNC Grinding Process Parameters Using Different Neural Networks', Tehnički vjesnik : znanstveno-stručni časopis tehničkih fakulteta Sveučilišta u Osijeku, 25(6), pp. 1770-1775. doi: 10.17559/TV-20180419095119

Šarić T, Šimunović G, Vukelić Đ, Šimunović K, Lujić R. Estimation of CNC Grinding Process Parameters Using Different Neural Networks. Tehnički vjesnik : znanstveno-stručni časopis tehničkih fakulteta Sveučilišta u Osijeku [Internet]. 2018 [cited 2024 November 23];25(6):1770-1775. doi: 10.17559/TV-20180419095119

T. Šarić, G. Šimunović, Đ. Vukelić, K. Šimunović and R. Lujić, "Estimation of CNC Grinding Process Parameters Using Different Neural Networks", Tehnički vjesnik : znanstveno-stručni časopis tehničkih fakulteta Sveučilišta u Osijeku, vol. 25, no. 6, pp. 1770-1775, 2018. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:262:499893. [Accessed: 23 November 2024]

Metadata
Title (english)Estimation of CNC Grinding Process Parameters Using Different Neural Networks
AuthorTomislav Šarić
MBZ: 190264
AuthorGoran Šimunović
MBZ: 212233
AuthorĐorđe Vukelić
AuthorKatica Šimunović
MBZ: 211943
AuthorRoberto Lujić
MBZ: 212244
Author's institutionUniversity of Slavonski Brod
(Mechanical Engineering Faculty in Slavonski Brod)
(Department of Industrial Engineering)
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Mechanical Engineering
Abstract (english)
Continuation of research on solving the problem of estimation of CNC grinding process parameters of multi-layer ceramics is presented in the paper. Heuristic analysis of the process was used to define the attributes of influence on the grinding process and the research model was set. For the problem of prediction - estimation of the grinding process parameters the following networks were used in experimental work: Modular Neural Network (MNN), Radial Basis Function Neural Network (RBFNN), General Regression Neural Network (GRNN) and Self-Organizing Map Neural Network (SOMNN). The experimental work, based on real data from the technological process was performed for the purpose of training and testing various architectures and algorithms of neural networks. In the architectures design process different rules of learning and transfer functions and other attributes were used. RMS error was used as a criterion for value evaluation and comparison of the realised neural networks and was compared with previous results obtained by Back-Propagation Neural Network (BPNN). In the validation phase the best results were obtained by Back-Propagation Neural Network (RMSE 12, 43 %), Radial Basis Function Neural Network (RMSE 13, 24 %, ), Self-Organizing Map Neural Network (RMSE 13, 38 %) and Modular Neural Network (RMSE 14, 45 %). General Regression Neural Network (RMSE 21, 78 %) gave the worst results.
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review - international
Publication versionPublished version
Journal titleTehnički vjesnik : znanstveno-stručni časopis tehničkih fakulteta Sveučilišta u Osijeku
Numberingvol. 25, no. 6, pp. 1770-1775
p-ISSN1330-3651
DOIhttps://doi.org/10.17559/TV-20180419095119
URN:NBNurn:nbn:hr:262:499893
Publication2018
Document URLhttp://bib.irb.hr/974815
Type of resourceText
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2021-04-26 13:03:25