Tool failure may result in losses in surface finish and dimensional accuracy of a finished part, or possible damage to the work piece and machine. This paper presents a Fuzzy Neural Network (FNN) which is designed and developed for machinery prognostic monitoring. The FNN is basically a multi-layered fuzzy-rule-based neural network which integrates a fuzzy logic inference into a neural network structure. The fuzzy rules help to speed up the learning process of the complex conventional neural network structure and improve the accuracy in prediction and rate of convergence. A case study for prediction of tool life in a dry milling operation is presented to demonstrate the viability of the proposed FNN for tool condition monitoring. A comparison was made in the case study on prediction performances of different models established with the same set of
experimental data. It is shown that the FNN is superior to conventional Multi-Regression Models (MRM), Backpropagation Neural Networks (BPNN) and Radial Basis Function Networks (RBFN) in term of prediction accuracy and BPNN in learning speed.
Reference paper - Fuzzy Neural Network Modelling for Tool Wear Estimation in Dry Milling Operation
