Prediction of cutting forces in turning process using de-neural networks

Authors:
Mahdi S. Alajmi;Fawzan Alfares
Affiliations:
Department of Mechanical Production, College of Technological Studies, Safat, State of Kuwait;Department of Mechanical Production, College of Technological Studies, Safat, State of Kuwait
Venue:
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
Year:
2007

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Abstract

A reliable prediction of cutting forces is the aim of many researchers. In this study cutting forces prediction was modeled using back propagation (BP) neural network with an enhancement by differential evolution (DE) algorithm. Experimental machining data is used in this study to train and evaluate the model. The data includes speed, feed rate, depth of cut, nose wear, flank wear, notch wear, feed force, vertical force, and radial force. A graphical study of the data reveals high non-linearity and early experiments carried out in this study using simple back propagation network gave marginally acceptable results. The results have shown an obvious improvement in the reliability of predicting the cutting forces over the previous work.