Automated inspection of machine parts
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Describes a CAD-model-based machine vision system for dimensional inspection of machined parts, with emphasis on the theory behind the system. The original contributions of the work are: the use of precise definitions of geometric tolerances suitable for use in image processing, the development of measurement algorithms corresponding directly to these definitions; the derivation of the uncertainties in the measurement tasks; and the use of this uncertainty information in the decision-making process. Experimental results have verified the uncertainty derivations statistically and proved that the error probabilities obtained by propagating uncertainties are lower than those obtainable without uncertainty propagation.< Keywords:
Propagation of uncertainty
Machine Vision
Adequate measurement uncertainty evaluation is crucial for supporting basic measurement purposes. However, the most prevalent approach, the uncertainty propagation, may not be validly applicable under certain conditions which require the use of less restricted alternative method (MCM-based propagation of distributions). We demonstrate the effects of major conditions, e.g. non-linear measurement model, non-Gaussian input quantities, on the reliability of uncertainty evaluation methods, highlighting the importance of the less frequently examined impact of uncertainty component contributions to the standard uncertainty of measurand.
Propagation of uncertainty
Sensitivity Analysis
Standard uncertainty
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The paper topic is about the propagation of uncertainty in a multivariate measurement. The Law of Propagation of Uncertainty defined in the Guide to the Expression of Uncertainty in Measurement is used to calculate the standard uncertainties, covariance matrix and correlation coefficient. The procedure is presented for a set of samples, acquired from a balance platform, which are used to calculate the centre of pressure (COP) coordinates. The possibility of evaluating the uncertainty of COP results in different way is also discussed.
Propagation of uncertainty
Sensitivity Analysis
Standard uncertainty
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Propagation of uncertainty
Roundness (object)
Uncertainty Quantification
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All measurements have error that obscures the true value. The error creates uncertainty about the quality of the measured value, which is requiring testing and calibration laboratories to provide estimates of uncertainty with their measurements. Measurement uncertainties include input uncertainty, the propagation of input uncertainty, the output uncertainty and the systematic error uncertainty. Several methods for estimating the uncertainty of measurements have been introduced for different kinds of uncertainty quantification, and two data mining methodologies-Artificial Neural Network (ANN) and Support Vector Machine (SVM) are used to build the unknown propagation model. This paper will discuss the quantification of measurement uncertainty (MU) and the separation of various uncertainty sources to MU and will discuss the advantages and limitations of SVM and ANN for building the propagation model of MU.
Propagation of uncertainty
Sensitivity Analysis
Uncertainty Quantification
Backpropagation
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Propagation of uncertainty
Uncertainty Quantification
Sensitivity Analysis
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Some Important Definitions.- Probability Functions.- Other Probability Functions.- Evaluation of Measurement Data.- Propagation of Errors/Uncertainty.- Uncertainty and Calibration of Instruments.- Calculation of Uncertainty.- Uncertainty in Calibration of a Surface Plate.- Uncertainty in Mass Measurement.- Uncertainty in Volumetric Measurement.- Uncertainty in Calibration of Some More Physical Instruments.- Uncertainty in Calibration of Electrical Instruments.
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The controversy over estimates of measurement uncertainty in the Guide to the Expression of Uncertainty in Measurement and Supplement 1 to it is considered. It is shown that possible ways to overcome these disagreements are to use the methods developed by the authors. Using the example of resistance calibration on a direct current, the features of taking into account the distribution of input values in the procedure for uncertainty evaluation when using the kurtosis method and low of propagation of expanded uncertainty are shown. A model of direct measurement of the resistance value of a resistance measure using a reference ohmmeter is written, the procedures for measurement uncertainty evaluation are described, and the uncertainty budgets are given. An example of measurement uncertainty evaluation at calibrating a resistance box P33 class 0.2 using a Fluke 8508 A digital multimeter is described. The expanded uncertainty of measurement for this example was estimated based on the NIST Uncertainty Machine web application, which showed good agreement with the estimates obtained by the methods considered.
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Propagation of uncertainty
NIST
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Kurtosis
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This paper presents a probabilistic uncertainity evaluation method as described in the Guide to the Expression of Uncertainty in Measurements (GUM) and its application to probe measurements on pressure and fuel concentration. All sources of unceratinties are expressed as probability distributions. Consequently, the overall standard uncertainty of the quantity can be calculated using the Gaussian error propagation formula. The result of the uncertainty evaluation yields the most probable value of the measurand and describes its distribution in terms of rectangular (standard uncertainty) or gaussian (“expanded” uncertainty) distribution. A pitot-static probe and a fuel-concentration stem probe are used in order to demonstrate the principle of the probabilistic uncertainty evaluation method. The uncertainty induced by the pressure and concentration data acquisition system as well as the calibration of the fuel-concentration probe are included in the analysis. The overall “expanded” uncertainties for the measured and calculated values are presented as a function of different inlet fuel flows. In addition to this, the individual sources of uncertainty to the overall standard uncertainty are presented and discussed. Moreover, the transformation of standard uncertainty to “expanded” uncertainty will provide the deviation of the measurement in a 95% or 99% normal distributed interval instead of a 67% rectangular distributed interval.
Propagation of uncertainty
Uncertainty Quantification
Standard uncertainty
Sensitivity Analysis
Pitot tube
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Abstract The Guide to the expression of uncertainty in measurement (GUM) has been the enduring guide on measurement uncertainty for metrologists since its first publication in 1993. According to the GUM, a measurement should always be accompanied by a reasoned and defensible expression of uncertainty, and the primary such expression is the standard uncertainty. In this article, we distinguish between the use of an expression of uncertainty as information for the recipient of a measurement result and its use when propagating uncertainty about inputs to a measurement model in order to derive the uncertainty in a measurand. We propose a new measure of uncertainty, the characteristic uncertainty , and argue that it is more fit for these purposes than standard uncertainty. For the purpose of reporting a measurement result, we demonstrate that standard uncertainty does not have a meaningful interpretation for the recipient of a measurement result and can be infinite. These deficiencies are resolved by the characteristic uncertainty, which we therefore recommend for use in reporting. For similar reasons, we advocate the use of the median estimate as the measured value. For the purpose of propagating uncertainty in a measurement model, we propose simple propagation of the median and characteristic uncertainty and show through some examples that this characteristic uncertainty framework is simpler and at least as reliable and accurate as the propagation of estimate, standard uncertainty and effective degrees of freedom according to the GUM uncertainty framework.
Sensitivity Analysis
Propagation of uncertainty
Expression (computer science)
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Measurement uncertainty is one of the most important concepts. The ISO IEC 17025:2005 standard: describes harmonized policies and procedures for testing and calibration laboratories. Guide to the expression of uncertainty in measurement (GUM) is a direct uncertainty analysis method, which calculates the combined standard uncertainty and expanded uncertainty by law of propagation of uncertainty. Monte Carlo Method (MCM) as presented by the (GUM S1) involves the propagation of the distributions of the input sources of uncertainty by using a model to provide the distribution of the output. By random sampling, the probability density function of the input quantities. In this paper, present measurement uncertainty to circular runout error. By use shaft standard with a diameter of 32 mm., length 100 mm. From the experiment results, Comparison of GUM and MCM showed no differences. The cases the estimated uncertainty using the GUM approach slightly overestimated the results obtained with the MCM. However, the use of numerical methods such MCM as a valuable alternative to the GUM approach. The practical use of MCM it has proven to be a fundamental tool, being able to address more complex measurement problems that were limited by the GUM approximations.
Propagation of uncertainty
Sensitivity Analysis
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