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Mechanistic cutting force model has the potential for monitoring micro-milling tool wear. However, the existing studies mainly consider the linear cutting force model, and they are incompetent to monitor the micro-milling tool wear which has a significant nonlinear effect on the cutting force due to the cutting-edge radius size effect. In this study, a nonlinear mechanistic cutting force model considering the comprehensive effect of cutting-edge radius and tool wear on the micro-milling force is constructed for micro-milling tool wear monitoring. A stepwise offline optimization approach is proposed to estimate the multiple parameters of the model. By minimizing the gap between the theoretical force expressed by the nonlinear model and the force measured in real-time, the tool wear condition is online monitored. Experiments show that, compared with the linear model, the nonlinear model has significantly improved cutting force prediction accuracy and tool wear monitoring accuracy.

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