Tool life model for Al2O3 particle reinforced MMCs using genetic expression programming

Abstract

In the present work, the wear of cutting tools in the machining of 7075Al alloy composites reinforced with 10 and 25 wt-% and 16 μm average size Al₂O₃ particles was investigated. Machining tests were performed on both as cast and heat treated composites by turning process using a physical vapour deposition coated carbide tool CP500 at different cutting conditions. A new model was developed to predict the tool life by genetic expression programming. The training and validation data sets were obtained from the well established machining test results. The weight fraction of particle P_w, cutting speed V, feedrate f and heat treatment Ht were used as independent input variables, while tool life T was used as a dependent output variable. Different models for tool life were predicted on the basis of the training data set using genetic programming, and the accuracy of the best model was proved with validation data set. The test results showed that the genetic expression programming model has produced correlation coefficient R values of ～0·958 for the training data and 0·952 for the validation data. The predicted tool life results were compared with experimental results and found to be in good agreement with the experimentally observed ones.