Prediction and modeling of cutting forces are major concerns of metal cutting theory. Large numbers of interrelated parameters that influence the cutting forces (cutting speed, feed rate, depth of cut, primary and secondary cutting edge angels, rake angle, nose radius, clearance angle, cutting edge, cutting tool wear, cutting tool coating type, etc.) makes the development of proper model a very difficult task. This study proposed a predictive model for cutting forces in turning process using recurrent neural network. A set of seven variables chosen to represent, the machining conditions and tool wear, used as an input of variables for the network,while the cutting forces consider as the output variables. This study shows that using recurrent neural network can lead to a more accurate predictive model for cutting forces. ICME07_028.pdf

Cutting Forces, Neural Network, Turning Process