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Regression analysis, support vector machines, and Bayesian neural network approaches to modeling surface roughness in face milling

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Abstract

This study examines the influence of cutting speed, feed, and depth of cut on surface roughness in face milling process. Three different modeling methodologies, namely regression analysis (RA), support vector machines (SVM), and Bayesian neural network (BNN), have been applied to data experimentally determined by means of the design of experiment. The results obtained by the models have been compared. All three models have the relative prediction error below 8%. The best prediction of surface roughness shows BNN model with the average relative prediction error of 6.1%. The research has shown that, when the training dataset is small, both BNN and SVR modeling methodologies are comparable with RA methodology and, furthermore, they can even offer better results. Regarding the influence of the examined cutting parameters on the surface roughness, it has been shown that the feed has the largest affect on it and the depth of cut the least.

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Authors and Affiliations

  1. Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, Ruđera Boškovića bb, 21000, Split, Croatia

    B. Lela, D. Bajić & S. Jozić

Authors
  1. B. Lela
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  2. D. Bajić
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  3. S. Jozić
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Correspondence to B. Lela.

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Lela, B., Bajić, D. & Jozić, S. Regression analysis, support vector machines, and Bayesian neural network approaches to modeling surface roughness in face milling. Int J Adv Manuf Technol 42, 1082–1088 (2009). https://doi.org/10.1007/s00170-008-1678-z

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  • DOI: https://doi.org/10.1007/s00170-008-1678-z

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