Adaptive model estimation of machine-tool thermal errors based on recursive dynamic modeling strategy

A systematic model adaptation methodology is developed in order to continuously update the thermal-error model under varying manufacturing conditions. Process-intermittent probing and adaptive system identification techniques are integrated to monitor and estimate machine-tool thermal errors and recursively modify model coefficients as manufacturing process proceeds. The recursive model adaptation, which is based on the Kalman filter parameter estimation technique and multiple-sampling horizons, minimizes intrusion to production while maintaining good model adaptation capability. The experimental results proved that the proposed methodology has good model accuracy and robustness to frequently changing working conditions.