Prediction of cutting temperature in orthogonal machining of AISI 316L using artificial neural network
Loading...
Files
Date
2016
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier Science Bv
Access Rights
info:eu-repo/semantics/closedAccess
Abstract
In this study, an approach based on artificial neural network (ANN) was proposed to predict the experimental cutting temperatures generated in orthogonal turning of AISI 316L stainless steel. Experimental and numerical analyses of the cutting forces were carried out to numerically obtain the cutting temperature. For this purpose, cutting tests were conducted using coated (TiCN + Al2O3 + TiN and Al2O3) and uncoated cemented carbide inserts. The Deform-2D programme was used for numerical modelling and the Johnson-Cook (J-C) material model was used. The numerical cutting forces for the coated and uncoated tools were compared with the experimental results. On the other hand, the cutting temperature value for each cutting tool was numerically obtained. The artificial neural network model was used to predict numerical cutting temperatures by means of the numerical cutting forces. The best results in predicting the cutting temperature were obtained using the network architecture with a hidden layer which has seven neurons and LM learning algorithm. Finally, the experimental cutting temperatures were predicted by entering the experimental cutting forces into a formula obtained from the artificial neural networks. Statistical results (R-2, RMSE, MEP) were quite satisfactory. This demonstrates that the established ANN model is a powerful one for predicting the experimental cutting temperatures. (C) 2015 Elsevier B.V. All rights reserved.
Description
Aslantas, Kubilay/0000-0003-4558-4516; KARA, Fuat/0000-0002-3811-3081
WOS: 000366805900005
WOS: 000366805900005
Keywords
ANN, Finite element model, Cutting temperature, Orthogonal cutting, Cutting forces
Journal or Series
Applied Soft Computing
WoS Q Value
Q1
Scopus Q Value
Q1
Volume
38
