What is it about?
This paper presents a multivariate regression predictive model of drift on Coordinate Measuring Machine (CMM) behavior. Evaluation tests on a CMM with a multi-step gauge were carried out following an extended version of an ISO evaluation procedure with a periodicity of at least once a week and during more than five months. This test procedure consists in measuring the gauge for several range volumes, spatial locations, distances and repetitions. The procedure, environment conditions and even the gauge have been kept invariables, so a massive measurement dataset was collected over time under high repeatability conditions. A multivariate regression analysis has revealed the main parameters that could affect CMM behavior, and then detected a trend on the CMM uncertainty drift. A performance model that considers both the size of the measured dimension and the elapsed time since the last CMM calibration has been developed. This model could predict CMM performance and measurement reliability over time and also could estimate an optimized time between calibrations for a specific measurement length or accuracy level.
Featured Image
Why is it important?
This work has been focused on the creation of a statistical model that fits the measurement results corresponding to the measurement of the same artefact with the same CMM over an established period of time, allowing finding an equation that describes the evolution of the systematic error and the standard deviation of the measurement results over time. The model could also be used to estimate an appropriate value for the calibration or reverification period to adapt this period to the measurand size and to the tolerance range to verify (ensuring the reliability on measurements for a specific length to be measured or for a given tolerance range). It must be noted that the presented model has been developed by statistical analysis from an experimentation that guarantees the repeatability and traceability of results. In this work the same test has been repeated under realistic conditions and controlling the magnitudes of influence. So the variability is constrained to that corresponding to the normal working conditions of the CMM allowing constructing a model of behaviour of the CMM over time.
Perspectives
The extrapolation of this model allows for predicting the evolution of the CMM uncertainty, which saves time and cost, overall when specifications are not very exigent (tolerance range in the order of tenths of millimeter) or when the volume of the work piece to be measured does not cover the entire volume of the CMM or an important part of the CMM longer axis. On the other hand, if the behavior of our CMM indicates that measurements are out of the conformance zone for a given significance level, the developed model can be used to establish how much time the calibration/reverification period must be reduced in order to ensure measurements. Finally, the developed statistical model is intended to be applied to the analysis of drift or behaviour of Articulated Arm Coordinate Measuring Machines (AACMMs), or portable Coordinate Measuring Arms (CMAs). In this case, the study is even more justified due to two main reasons. On one hand, the behaviour of this type of equipment over time is not being currently studied and, on the other hand, it is evident that a correlation cannot be established with regard to Cartesian linear axis, because their structure includes rotary encoders. In fact, in order to implement this model for CMAs, it must be taken into account that the influence of the length over the measurement uncertainty is not as strong as it is in the case of CMMs. Possibly the model should consider variables such as the relative position between CMM and gauge, the quadrant of the working space where the measurement is carried out, the fixturing type or the geometrical features being measured as well as the influence of the operator that handles the CMA.
Eduardo Cuesta
University of Oviedo
Read the Original
This page is a summary of: A Statistical Approach To Prediction Of The CMM Drift Behaviour Using A Calibrated Mechanical Artefact, January 2015, De Gruyter,
DOI: 10.1515/mms-2015-0033.
You can read the full text:
Contributors
The following have contributed to this page
