Online incremental learning for tool condition classification using modified Fuzzy ARTMAP network

Condition monitoring of tool wear is paramount for guaranteeing the quality of workpiece and improving the lifetime of the cutter. To improve the training speed and the flexibility of the incremental learning, a modified Fuzzy ARTMAP classifier is developed in which the resonance layer is linked with the category node directly by many to one mapping. Therefore, the weight value and model structure can be updated simultaneously during the online incremental learning process. To testify the effectiveness of the presented method, experiments of tool condition classification in the process of end milling of Titanium alloy are carried out and two incremental learning cases are simulated. The analysis of online learning process in both cases shows that the structure and parameters of the model can be adjusted automatically without requiring access to the previous training data. At the same time, the accuracy analysis demonstrates that the presented method has strong ability to learn the new knowledge without forgetting the previous knowledge.