Multiple regression and neural networks analyses in composites machining

The machining forces-tool wear relationship of an aluminium metal matrix composite has been studied in this paper using multiple regression analysis (MRA) and generalised radial basis function (GRBF) neural network. The results show that using the force-wear equation derived from MRA is a fairly accurate way of predicting the attainment of prescribed tool wear. However, the use of a neural network analysis can further improve the accuracy of the tool wear prediction particularly when the functional dependency is nonlinear. It is evident that the relationship derived from the feed force data is more accurate than that derived from the cutting force.