Online Monitoring Machining Errors of Thin-Walled Workpiece: A Knowledge Embedded Sparse Bayesian Regression Approach

Deflection of the tool and workpiece caused by cutting forces usually leads to machining errors of thin-walled workpieces. Monitoring this kind of force-induced errors plays an extremely important role in controlling and compensating the deflection-related machining failures in real time. However, accompanied by time consuming and complicated computations, now available analytical prediction methods cannot satisfy the requirements of online machining errors prediction. Hence, data-driven regression methods are introduced to online predict the machining errors. The challenges lie in that: first, the spatial continuous distribution of machining errors needs to be constructed via limited measured points; second, the regression model must have high generalization performance to adapt varied cutting parameters; and third, the model complexity should be restrained to improve the real-time performance. To tackling these challenges, a knowledge embedded regression is presented to model the relationship between machining error and cutting parameters, cutting location, as well as online measured cutting forces. The physical mechanism about machining errors is integrated into the model for improving the generalization accuracy. A Gaussian prior distribution over the model weights is introduced to reduce the model redundancy, for the sake of learning the weight variables with limited samples and increasing the prediction efficiency. Results have indicated that the predicted machining errors by the proposed model accords with the measurement better than those predicted by a purely data-dependent regression.