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Technical gazette, Vol. 25 No. 6, 2018.

Original scientific paper

https://doi.org/10.17559/TV-20180419095119

Estimation of CNC Grinding Process Parameters Using Different Neural Networks

Tomislav Šarić orcid id orcid.org/0000-0002-6339-7936 ; Mechanical Engineering Faculty, Trg I. B. Mažuranić 2, HR-35000 Slavonski Brod, Croatia
Goran Šimunović orcid id orcid.org/0000-0002-7159-2627 ; Mechanical Engineering Faculty, Trg I. B. Mažuranić 2, HR-35000 Slavonski Brod, Croatia
Đorđe Vukelić orcid id orcid.org/0000-0003-2420-6778 ; Faculty of Technical Sciences, Trg Dositeja Obradovića 6, RS-21000 Novi Sad, Serbia
Katica Šimunović orcid id orcid.org/0000-0001-5748-7110 ; Mechanical Engineering Faculty, Trg I. B. Mažuranić 2, HR-35000 Slavonski Brod, Croatia
Roberto Lujić orcid id orcid.org/0000-0001-5123-3064 ; Mechanical Engineering Faculty, Trg I. B. Mažuranić 2, HR-35000 Slavonski Brod, Croatia

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Abstract

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

algorithms of neural networks; CNC grinding; estimation; prediction

Hrčak ID:

212833

URI

https://hrcak.srce.hr/212833

Publication date:

16.12.2018.

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