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Materials 2016, 9(2), 82; doi:10.3390/ma9020082

Hybrid ABC Optimized MARS-Based Modeling of the Milling Tool Wear from Milling Run Experimental Data

1
Department of Mathematics, Faculty of Sciences, University of Oviedo, C/Calvo Sotelo s/n, 33007 Oviedo, Spain
2
Department of Mining Exploitation, University of Oviedo, 33004 Oviedo, Spain
3
Department of Mining Technology, Topography and Structures, University of León, 24071 León, Spain
*
Author to whom correspondence should be addressed.
Academic Editors: Jai-Sung Lee and J. Paulo Davim
Received: 18 November 2015 / Revised: 8 January 2016 / Accepted: 25 January 2016 / Published: 28 January 2016
(This article belongs to the Section Manufacturing Processes and Systems)
View Full-Text   |   Download PDF [3534 KB, uploaded 28 January 2016]   |  

Abstract

Milling cutters are important cutting tools used in milling machines to perform milling operations, which are prone to wear and subsequent failure. In this paper, a practical new hybrid model to predict the milling tool wear in a regular cut, as well as entry cut and exit cut, of a milling tool is proposed. The model was based on the optimization tool termed artificial bee colony (ABC) in combination with multivariate adaptive regression splines (MARS) technique. This optimization mechanism involved the parameter setting in the MARS training procedure, which significantly influences the regression accuracy. Therefore, an ABC–MARS-based model was successfully used here to predict the milling tool flank wear (output variable) as a function of the following input variables: the time duration of experiment, depth of cut, feed, type of material, etc. Regression with optimal hyperparameters was performed and a determination coefficient of 0.94 was obtained. The ABC–MARS-based model's goodness of fit to experimental data confirmed the good performance of this model. This new model also allowed us to ascertain the most influential parameters on the milling tool flank wear with a view to proposing milling machine's improvements. Finally, conclusions of this study are exposed. View Full-Text
Keywords: multivariate adaptive regression splines (MARS); artificial bee colony (ABC); statistical learning techniques; milling tool wear monitoring; hyperparameter selection; regression analysis multivariate adaptive regression splines (MARS); artificial bee colony (ABC); statistical learning techniques; milling tool wear monitoring; hyperparameter selection; regression analysis
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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García Nieto, P.J.; García-Gonzalo, E.; Ordóñez Galán, C.; Bernardo Sánchez, A. Hybrid ABC Optimized MARS-Based Modeling of the Milling Tool Wear from Milling Run Experimental Data. Materials 2016, 9, 82.

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