In-situ tool wear area evaluation in micro milling with considering the influence of cutting force.

• A novel method of evaluation wear area for in-situ monitoring tool wear in micro milling. • Empirical statistical model including milling parameters and milling force. • The prediction results of empirical model are consistent with those of neural network. • Contribution of parameters on tool wear was obtained by grey relational analysis. 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. [ABSTRACT FROM AUTHOR]