Coordinate measuring machines (CMMs) are already widely utilized as measuring tools in the modern manufacturing industry. Rapidly approaching now is the trend for next-generation CMMs. However, the increases in measuring velocity of CMM applications are limited by dynamic errors that occur in CMMs. In this paper, a systematic approach for modeling the dynamic errors of a touch-trigger probe CMM is developed through theoretical analysis and experimental study. An overall analysis of the dynamic errors of CMMs is conducted, with weak components of the CMM identified with a laser interferometer. The probing process, as conducted with a touch-trigger probe, is analyzed. The dynamic errors are measured, modeled, and predicted using neural networks. The results indicate that, using this mode, it is possible to compensate for the dynamic errors of CMMs.

Issue Section:
Technical Papers
Keywords:
position measurement, measurement errors, compensation, neural nets, machine theory
Topics:
Coordinate measuring machines, Errors, Artificial neural networks
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