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Open AccessArticle

A Comparative Study between Regression and Neural Networks for Modeling Al6082-T6 Alloy Drilling

1
School of Mechanical Engineering, Section of Manufacturing Technology, National Technical University of Athens, Heroon Polytechniou 9, 15780 Athens, Greece
2
Department of Mechanical Engineering & Industrial Design, Western Macedonia University of Applied Sciences, GR 50100 Kila Kozani, Greece
*
Author to whom correspondence should be addressed.
Machines 2019, 7(1), 13; https://doi.org/10.3390/machines7010013
Received: 31 December 2018 / Revised: 28 January 2019 / Accepted: 29 January 2019 / Published: 2 February 2019
(This article belongs to the Special Issue Advances in CAD/CAM/CAE Technologies)
Apart from experimental research, the development of accurate and efficient models is considerably important in the field of manufacturing processes. Initially, regression models were significantly popular for this purpose, but later, the soft computing models were proven as a viable alternative to the established models. However, the effectiveness of soft computing models can be often dependent on the size of the experimental dataset, and it can be lower compared to that of the regression models for a small-sized dataset. In the present study, it is intended to conduct a comparison of the performance of various neural network models, such as the Multi-layer Perceptron (MLP), the Radial Basis Function Neural Network (RBF-NN), and the Adaptive Neuro-Fuzzy Inference System (ANFIS) models with the performance of a multiple regression model. For the development of the models, data from drilling experiments on an Al6082-T6 workpiece for various process conditions are employed, and the performance of models related to thrust force (Fz) and cutting torque (Mz) is assessed based on several criteria. From the analysis, it was found that the MLP models were superior to the other neural networks model and the regression model, as they were able to achieve a relatively lower prediction error for both models of Fz and Mz. View Full-Text
Keywords: drilling; Al6082-T6; multiple regression; multi-layer perceptron; radial basis function neural network; adaptive neuro-fuzzy inference system; thrust force; cutting torque drilling; Al6082-T6; multiple regression; multi-layer perceptron; radial basis function neural network; adaptive neuro-fuzzy inference system; thrust force; cutting torque
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Karkalos, N.E.; Efkolidis, N.; Kyratsis, P.; Markopoulos, A.P. A Comparative Study between Regression and Neural Networks for Modeling Al6082-T6 Alloy Drilling. Machines 2019, 7, 13.

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