CMM uncertainty analysis with factorial design

Piratelli-Filho, Antonio; Giacomo, Benedito Di

doi:10.1016/s0141-6359(03)00035-7

Cited by 36 publications

(15 citation statements)

References 3 publications

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“…There are several studies that have used experimental design for estimating uncertainty of CMM measurements. Approach for determining the measurement uncertainty of CMM through test performances consists of measuring calibrated ball-bars of different lengths in different orientations in the work space according to selected plan of the experiment [16]. Feng and associates also used DOE to investigate the effects of factors and their interactions on the CMM measurement uncertainty as it was defined in the GUM [17], [18].…”

Section: B Uncertainty Sources In Cmm Measurementmentioning

confidence: 99%

Different Approaches in Uncertainty Evaluation for Measurement of Complex Surfaces Using Coordinate Measuring Machine

Vrba

Palenčár

Hadžistević

et al. 2015

Measurement Science Review

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This paper describes a methodology for uncertainty assessment for Coordinate Measuring Machine measurement of complex real work pieces from industry. The study applied two approaches (in scanning mode only) for estimating the measurement uncertainty with the support of Taguchi plan in the experiment containing five factors: scanning speed, sample density, probe configuration, scanning direction, and position of measuring object. In the first approach the uncertainty was estimated by measuring the basic geometric objects (primitives like sphere and torus) representing the decomposition of complex surfaces and in the second one a complex surface was treated as an unknown quantity. Calculated uncertainty Type A for both measurement tasks was in the range from 0.65 µm to 6.47 µm. Evaluation of the uncertainty Type B covered specifications of the machine and standard uncertainties derived from temperature effects. Total u B component was found to be in order of 0.4 µm. Future research will be directed towards the development and application of simulation methods

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Section: B Uncertainty Sources In Cmm Measurementmentioning

confidence: 99%

Different Approaches in Uncertainty Evaluation for Measurement of Complex Surfaces Using Coordinate Measuring Machine

Vrba

Palenčár

Hadžistević

et al. 2015

Measurement Science Review

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“…Among these master gauges different three-dimensional features can be found, like small cylinders and rectangular prisms [5], a combination of cylinders & cones [6], and other geometric characteristics [7,8], Ball plates [9,10], ring gauges [11] or Ball Bar devices [12].…”

Section: Introductionmentioning

confidence: 99%

“…Some authors develop analytical models to calculate uncertainty taking into account different tolerances in specific tasks [9,15,16]. From a different approach to the matter, others researchers start from the Virtual CMM concept [17] and propose models that attempt to predict the CMM uncertainty by modelling the CMM behaviour [18−20], using factorial design [12] or taking into account the effects of several contributions [21−23].…”

Section: Introductionmentioning

confidence: 99%

A Statistical Approach To Prediction Of The CMM Drift Behaviour Using A Calibrated Mechanical Artefact

Cuesta

Álvarez

Sánchez-Lasheras

et al. 2015

Metrology and Measurement Systems

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This paper presents a multivariate regression predictive model of drift on the Coordinate Measuring Machine (CMM) behaviour. 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 the CMM behaviour, and then detected a trend on the CMM performance 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 can predict the CMM performance and measurement reliability over time and also can estimate an optimized period between calibrations for a specific measurement length or accuracy level.

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“…Results were carried out in the case of simple geometrical objects or calibrated artefacts (e.g. sphere, ring, plane), using, in some cases, statistically designed experiments [3][4][5][6][7] but none of these studies dealt with complex or free form surface verifications.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty analysis of point-by-point sampling complex surfaces using touch probe CMMs

Barini

Tosello

Chiffre

2010

Precision Engineering

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CMM uncertainty analysis with factorial design

Cited by 36 publications

References 3 publications

Different Approaches in Uncertainty Evaluation for Measurement of Complex Surfaces Using Coordinate Measuring Machine

Different Approaches in Uncertainty Evaluation for Measurement of Complex Surfaces Using Coordinate Measuring Machine

A Statistical Approach To Prediction Of The CMM Drift Behaviour Using A Calibrated Mechanical Artefact

Uncertainty analysis of point-by-point sampling complex surfaces using touch probe CMMs

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