Approaches to evaluate the virtual instrumentation measurement uncertainties (original) (raw)

Uncertainty Management in the measurements performed by means of virtual instruments

2008 IEEE Iinternational Workshop on Advanced Methods for Uncertainty Estimation in Measurement, 2008

The uncertainty management is the discipline of optimising the cost of a measurement versus the uncertainty target. In particular, the paper deals with the measurements performed by using a generic virtual instrument. The task is achieved by using the PUMA (Procedure for Uncertainty Management) method that is an iterative technique originally conceived for geometrical and mechanical measurements. The approach is completely based on the "Guide to the Expression of Uncertainty in Measurement" rules, but provides a more engineering methodology, since it allows to optimize the cost of the measurements versus the uncertainty target, avoiding the use of inadequate or, on the contrary, too expensive resources for the uncertainty estimation. According to the PUMA method, four different approaches to evaluate the uncertainties are presented and they are applied starting from the coarser, but quicker and cheaper one and keeping on with the more precise methodologies. In the practical cases, it is very probable to prove that the first iteration of the PUMA method is enough to optimise the uncertainty budget.

Two Algorithms for the Auto-Estimation of the Uncertainty in the Virtual Instrumentation

2003

In our recent papers we have dealt with the assessment of the uncertainties associated with the virtual instrument measurements, proposing two completely different methods. The first one is based on an original application of the "uncertainty propagation law" of the ISO "Guide to the expression of uncertainty in measurement". The second one is a numerical approach, based on the Monte

Uncertainty estimation by the concept of virtual instruments

Proceedings of SPIE - The International Society for Optical Engineering, 2001

For the calibration of length standards and instruments, various methods are available for which usually an uncertainty according to the GUM [1] can be set up. However, from calibration data of a measuring instrument it is not always evident what the uncertainty will be in an actual measurement (or calibration) using that calibrated instrument. Especially where many measured data are involved, such as in CMM measurements, but also in typical dimensional geometry measurements such as roughness, roundness and flatness measurements, setting up an uncertainty budget according to the GUM for each measurement can be tedious and even impossible. On the other hand, international standards require that for a proof of the conformance to specifications, the measurement uncertainty must be taken into account (ISO 14253-1 [2]). Apart from this it is not so consistent that a lot is invested in the calibration of instruments where it is still unclear what the uncertainty is of measurements carried out with these 'calibrated' instruments. In this paper it is shown that the 'standard' GUM-uncertainty budget can be modified in several ways to allow for more complicated measurements. Also, it is shown how this budget can be generated automatically by the measuring instrument, by the simulation of measurements by instruments with alternative metrological characteristics, so called virtual instruments. This can lead to a measuring instrument where, next to the measured value, also the uncertainty is displayed. It is shown how these principles are already used for roughness instruments, and how they can be used as well for e.g. roundness, cylindricity, flatness and CMM measurements.

Virtual Measuring Instruments as Means of Uncertainty Evaluation

Контрольно-измерительная техника, 2020

Modern measuring instruments as highly technological, precise, multi-functional tools today are complex systems, and estimation of their uncertainty turns into a non-trivial task of science. To provide information about the probability of results, their convergence, and reproducibility, it is necessary to analyze the task-oriented measurement uncertainty. As an approach to determining the uncertainty of complex systems, to avoid the need for professionally experienced personnel and expensive "artifacts" used for evaluation, there is a method of a so-called virtual measuring instrument. In this method, the measurement process is simulated, taking into account the influence of the main disturbance parameters and conducting statistical analysis using the Monte Carlo approach. All characteristics of virtual modules repeat the properties of real devices but allow quick and qualitative evaluation of environmental parameters' effect on the accuracy, as well as the uncertainty of measurement. It allows us to evaluate the correctness of the result under the present conditions. The measurement uncertainty is usually caused by several major sources. Uncertainty depends on the method of measurement, but there are still common factors, i.e. uncertainty caused by measuring instruments, methods, operators, and environment. Among environmental influences, it is important to highlight-the change of light and temperature, which can vary widely variate at the production process, and at the same time have a crucial impact on the uncertainty of measurement. The paper presents a virtual measurement instrument method and its known implementations.

Problem of Applying Modern Uncertainty Concepts to the Measurement of Instrument-Specific Parameters

IEEE Transactions on Instrumentation and Measurement, 2006

This paper presents a number of problems that occur when applying the Guide to the Expression of Uncertainty in Measurement (GUM) to modern instruments. These instruments are often automatic, which makes it complicated to evaluate the uncertainty components in each measurement step because it is difficult to control and analyze them. Many of these instruments try to quantify instrument-specific parameters, which are difficult to compare with others that have the same dimension but are measured using other techniques. Often, these parameters lack traceable calibration, which may result in a large uncertainty component. This paper also considers the human aspect of the measurement process.

Virtual Instruments in Dimensional Metrology

Proceedings of Proceedings of Research in Engineering Education Symposium 2011 Rees2011 Research in Engineering Education Symposium 2011 Rees2011 04 10 2011 07 10 2011 Madrid Espana, 2011

offers its PhD students, as a course work, the construction of a virtual instrument. This virtual instrument simulates the imaging of a part to be measured by optical dimensional metrology instruments (microscopes, profile projectors, vision machines). The LMM provides students with images similar to those they would obtain with real instrumentation for the instrument adjustment and calibration process. Working with these images, students should determine the adjustment parameters of the virtual instrument. Once these parameters are set, the student can perform the proper calibration of the virtual instrument. Beyond this process, the instrument is already able to perform traceable measurement. In order to do that, LMM offers students some images of parts. Students should perform some measurements using those images and estimate the corresponding uncertainties.

Evaluation of measurement uncertainty – Monte Carlo method

Mechanik

Advantages of Monte Carlo method are presented and compared with A and B type method of measurement uncertainty evaluation. Problem of uncertainty determination, in case of two or more dominant components, is discussed. Results of experiment to evaluate impact of probing strategy on measurement uncertainty of roundness deviation are presented. Issue of ‘systematic error’ in evaluation of coordinate measurement uncertainty is analyzed.

Application of Simulation Software to Coordinate Measurement Uncertainty Evaluations

NCSLI Measure, 2007

Uncertainty evaluations for coordinate measuring machine (CMM) metrology are problematic due to the number, ranges, interactions and generally unknown sensitivity coefficients of the parameters that can influence the measurement result. The situation is particularly difficult when a task-specific uncertainty is required and poses problems for both auditors and metrology practitioners. Auditors often lack satisfactory tools for a comprehensive assessment of a client’s claims of traceability. Measurement professionals, similarly, have difficulty demonstrating compliance with measurement traceability requirements and, in addition, can find themselves at a real economic disadvantage if reliable measurement uncertainties are not known. In this paper, the historical perspective of, the motivations for, and the necessity of task-specific uncertainty evaluations are briefly discussed. This is followed by a presentation of the requirements and desirable features of a credible method for task-specific CMM uncertainty evaluation. Next, a description of the major design features of a practical software application for evaluating uncertainties of CMM measurements are presented. This is concluded by presenting several application examples and case studies which demonstrate that, in the arena of task-specific CMM uncertainty evaluation, simulation methods exhibit notable strength and versatility.