Prediction of burr formation during face milling using an artificial neural network with optimized cutting conditions

Abstract

Burrs formed during face milling operations are difficult to characterize because there are several parameters with complex interactions that affect the cutting process. In this paper, a combined artificial intelligence and optimization approach is introduced to predict burr types formed during face milling. The Taguchi method was selected for the optimization and an artificial neural network (ANN) was constructed for the machining of aluminium alloy 6061-T6. For the training of the ANN, the input was non-dimensionalized using the optimized results from the Taguchi method. The resulting ANN output was in agreement with experimental results, validating the proposed scheme.