Abstract
In order to realize the tool wear in-situ monitoring in micro milling, a novel two-dimensional tool wear estimation approach is developed in this work. The novelty and strong point of the approach is that it can achieve both high estimation accuracy and computational efficiency for fast tool condition monitoring. For this purpose, an empirical statistical model including both process parameters and force features is firstly proposed for in-situ tool wear area estimation. Then the model is improved to enhance its practicability. By comparing the experimental measurements against the results predicted by the improved model and neural network model, it is shown that the improved model has better prediction effect, which illustrates that this approach can realize tool wear estimation in micro milling. Finally, the influence of each variable in improved model on tool wear is analyzed by grey relational degree. The results of this study indicate that this approach can be used to optimize cutting parameters and predict tool wear online.
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