An Adaptive Error Correction Method Using Feature-Based Analysis Techniques for Machine Performance Improvement, Part 2: Experimental Verification

Abstract

The development and derivation of an adaptive error correction method using an interactive inspection system and feature-based analysis technique for machine performance improvement were presented in Part 1 of this paper. Here, in Part 2, the experimental verification of the derived feature-based residual error models and analysis technique is described in detail. The experimental procedure and results of both the process-intermittent and the post-process gauging are presented. A polynomial regression method is used to derive the parametric functions that represent the deviation of imperfect features from nominal. The output of these polynomial functions is compared with process-intermittent inspection data to determine the machine errors and then to identify the residual systematic errors. Feature-based residual error models are used to correlate the residual errors to the systematic residual error components. Inverse kinematics technique and multiple regression methods are used to identify and characterize the contribution of each error component as a function of the machine’s temperature profile and nominal position. The residual error components are then combined with the error components identified through the pre-process characterization process to refine the geometric-thermal error model. After the error model is adaptively fine tuned, it can be used more effectively to compensate the machine tool error for more precise manufacturing.