MULTI-PARAMETER ANN MODEL FOR FLAT-END MILLING

The quality, productivity and safety of machining can be significantly improved through the optimization of cutting conditions. The first step in achieving such an objective is the development of accurate and reliable models for predicting the critical process parameters. In this paper, an innovative Artificial Neural Network (ANN) model that predicts both cutting force and surface roughness in end milling is developed and validated. A set of five input variables is selected to represent the machining conditions while twelve quantities representing two key process parameters, namely, cutting force and surface roughness, form the variables of the network output. Full factorial design of experiments is used to generate data for both training and validation. Successful training of the neural network is demonstrated through comparison of simulated and experimental results for four different output variables, namely cutting force, surface roughness, feed marks, and tooth passing frequency. The predictive ability of the model is verified experimentally by comparing simulated output variables with their experimental counterparts. A good agreement is observed.