Modeling and optimization of the cutting fluid flow and parameters for increasing tool life in slot milling on St52

In this paper the CNC machining of St52 was modeled using an artificial neural network (ANN) in the form of a four-layer multi-layer perceptron (MLP). The cutting parameters used in the model were cutting fluid flow, feed rate, spindle speed and the depth of cut and the model output was the tool life. For obtaining more accuracy and spending less time Taguchi design of experiment (DOE) has been used and correlation between the output of the ANN and the experimental results was 96%. Further optimization process has been done by use of a genetic algorithm (GA). After optimization process tool life was increased about 8% equal to 33 min and was corroborated by experimental tests. This demonstrates that the coupling of an ANN with the GA optimization technique is a valid and useful approach to use. © 2013 World Scientific Publishing Company.