Mechanism-Based Structured Deep Neural Network for Cutting Force Forecasting Using CNC Inherent Monitoring Signals

Cutting force can provide highly sensitive and quick response to unpredictable variations of the machining system, which has been considered as the most valuable physical signals for machining state monitoring. As a reliable, practical and cost-effective solution for cutting force monitoring, forecasting cutting force using computer numerical control (CNC) inherent monitoring signals has great potential to be applied in real industry. However, existing mechanism-based methods suffer from inaccurate identification process and underlying modeling errors, while data-driven methods are highly data-dependent due to the lack of physical interpretability and generalization ability. This article proposes a mechanism-based structured deep neural network (MS-DNN) for cutting force forecasting. By taking sub neural networks as approximators for submodels and reserving the connections among the variables of the end-to-end mechanism model without parameter identification, MS-DNN is equivalent to the mechanism model in form, which can naturally inherit the physical interpretability and generalization ability of the mechanism, but is much more powerful in modeling the complex dynamic relationships between cutting force and CNC inherent signals. The proposed MS-DNN is verified on both simulation and real experimental datasets, and the results show MS-DNN can achieve an excellent performance in cutting force forecasting using CNC inherent signals.