A Comparative Study between Regression and Neural Networks for Modeling Al6082-T6 Alloy Drilling
AbstractApart 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
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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.
Karkalos NE, Efkolidis N, Kyratsis P, Markopoulos AP. A Comparative Study between Regression and Neural Networks for Modeling Al6082-T6 Alloy Drilling. Machines. 2019; 7(1):13.Chicago/Turabian Style
Karkalos, Nikolaos E.; Efkolidis, Nikolaos; Kyratsis, Panagiotis; Markopoulos, Angelos P. 2019. "A Comparative Study between Regression and Neural Networks for Modeling Al6082-T6 Alloy Drilling." Machines 7, no. 1: 13.
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