An Adaptive Methodology for Machine Tool Error Correction

Abstract

An adaptive methodology for machine tool error correction is presented in this paper. The estimated errors resulting from the proposed methodology could be used to adjust the depth of cut on the finish pass or correct the probing data for on-machine inspection to improve the accuracy of workpiece. A generalized error model is derived, using rigid body kinematics, to describe the error motion between the cutting tool and workpiece. Process-intermittent gauging and state observation techniques are integrated to monitor the thermally induced errors and then to modify the error model’s coefficients as the cutting process proceeds. Analysis-of-variance (ANOVA) is used to identify the significant variables for thermal effect modeling. A multiple linear regression model is derived to identify the dynamics of the time-varying thermally induced errors. Multivariable state observers are constructed to track the thermal effect in real time and to fine tune the error model’s coefficients. Experimental results show that the time-varying machine tool errors can be estimated with desirable accuracy.