Abstract
The development of an error compensation model for coordinate measuring machines (CMMs) and its integration into feature measurement is presented. CMMs are widespread and dependable instruments in industry and laboratories for dimensional measurement. From the tip probe sensor to the machine display, there is a complex transformation of probed point coordinates through the geometrical feature model that makes the assessment of accuracy and uncertainty measurement results difficult. Therefore, error compensation is not standardized, conversely to other simpler instruments. Detailed coordinate error compensation models are generally based on CMM as a rigid-body and it requires a detailed mapping of the CMM’s behavior. In this paper a new model type of error compensation is proposed. It evaluates the error from the vectorial composition of length error by axis and its integration into the geometrical measurement model. The non-explained variability by the model is incorporated into the uncertainty budget. Model parameters are analyzed and linked to the geometrical errors and uncertainty of CMM response. Next, the outstanding measurement models of flatness, angle, and roundness are developed. The proposed models are useful for measurement improvement with easy integration into CMM signal processing, in particular in industrial environments where built-in solutions are sought. A battery of implementation tests are presented in Part II, where the experimental endorsement of the model is included.
Highlights
coordinate measuring machines (CMMs) flexibility to cope with the measurement of complex geometries is based on point coordinate determination and the geometry reconstruction through computer algorithms
Probed point coordinates are the original measurand of the CMM that are transformed through signal processing and the measurement model into the feature measurement
The proposed model by axis considers the evaluation of the mean error, and a measurement uncertainty contribution with origin in the non-explained error variability
Summary
CMM flexibility to cope with the measurement of complex geometries is based on point coordinate determination and the geometry reconstruction through computer algorithms. In an ordinary direct measurement, the methodology includes sampling the measurand, estimating the best expected value (statistical mean of the sample) and the associated uncertainty to this estimation. For most of the direct measurements, a first basic frequentist approach is to consider the uncertainty as the standard deviation of the mean through the standard deviation of the sample. From the dynamics of the creates deviation ofcreates the probe from theoftheoretical theoretical itpositions It seems be a major account for full deviation traceability of any seems toTherefore, be a major task totoaccount for atask full to traceability ofaany through deviation through the complete and chain of measurement a CMM.
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