Artificial Neural Network models for tool wear prediction during Aluminium Matrix Composite milling

Artificial Neural Networks are one of the tools used to predict tool wear or surface roughness in machining. Model development is applicable when regression models do not give satisfactory results. In this work, Multilayer Perceptron networks were used to predict the tool flank wear and tool corner wear during milling of Aluminium Matrix Composite with 10% of SiC content. The signals of vibration acceleration and cutting forces were selected as input to the network and the tests were carried out with three cutting speeds. Based on the analysis of the developed models, the models with the best efficiency were selected and the quality of wear prediction was assessed.