Dynamic errors compensation of high-speed coordinate measuring machines using ANN-based predictive modeling.

Coordinate measuring machines (CMMs) are massively exploited as measuring tools in the modern manufacturing industry. The performance of these machines, in terms of accuracy, has been considerably improved in recent years by using quasi-static errors compensation. Considering the shorter cycle times required during measurement tasks, CMMs are to be operated at high measuring velocity. In such measuring conditions, dynamic errors have a critical impact on measuring accuracy. Consequently, dynamic errors assessment, modeling, and compensation are needed to improve the overall CMM metrological performances. In this paper, a comprehensive predictive modeling strategy for dynamic error compensation is developed and applied successfully. The main measuring parameters that influence CMM dynamic performance are identified and used in a systematic experimental investigation. The positioning accuracy is then evaluated concerning dynamic conditions using a high-precision laser interferometer. Based on the experimental results, neural network models are built according to a structured modeling procedure inspired by the Taguchi method. Improved statistical analysis tools and performance measurement criteria are used to extract the most appropriate variables and conditions leading to well-founded predictive modeling. The resulting models are implemented on a bridge-type CMM to compensate for both geometric and dynamic errors. The results demonstrate a reduction of more than 80% of dynamic errors. This demonstrates that the compensation of dynamic-induced errors using high-speed measurement is achieved leading to shorter cycle times of measurement tasks while maintaining high accuracy measurements. [ABSTRACT FROM AUTHOR]