Measurements and Uncertainties

The ,aim of this ,Beginner’s Guide is to introduce ,the subject of measurement uncertainty. Every measurement,is subject to some,uncertainty. A measurement,result is only complete,if it is accompanied ,by a ,statement of the ,uncertainty in the ,measurement. Measurement uncertainties can come from the measuring instrument, from the item being measured, from the environment, from the operator, and from other sources. Such uncertainties can be estimated using statistical analysis of a set of measurements, and using other kinds of information about the measurement,process. There are established rules for how to calculate an

American Journal of Engineering and Applied Sciences

The imposition of practice, the current world, the laboratory measurement, calibration should be approved by points of credit to national or international and should be compatible with the requirements specification (ISO 17025) for the adoption of efficient laboratories. Those requirements were included the testing process or scale limits to doubt that mentioned in the measurement certificate, which recognizes the customer to achieve quality and efficiency in the process of measurement. In this study we would theoretically try to clarify, indicate what the uncertainty in the measurement, standard types of uncertainty and how to calculate the budget of uncertainty as we should show some examples of how the scientific calculation of the budget challenge with some measure the lengths of the laboratory. After analyzing the results we had found during the measurement using CMM, we had found that the value of non-statistical uncertainty in the measurement type (b) piece length of one meter was ±1.9257 µm. and when using the configuration measuring device, we had gotten the value of the extended standard combined uncertainty ±2.030 µm when measured the screws value of 1.2707 mm. When used the configuration measuring device, we had gotten the value of the extended standard combined uncertainty ±2.030 µm when measuring the screws value of 1.2707 mm. We concluded that the impact of uncertainty on the measured results a high fineness degree and less impact on the smoothness of a piece with low fineness, careful calibration of measuring instrument Careful calibration of measuring instrument and equipment by measurement standard is of the utmost importance and equipment by measurement standard is of the utmost importance and laboratories must calculate the uncertainty budget as a part of measurement evaluation to provide high quality measurement results.

2023

OBJECTIVE: 1. To learn how measuring lengths using a ruler and a digital caliper. 2. To use the measurements obtained to calculate the density of a samples. 3. To see how measurement uncertainties affect the results. 4. To give an understanding of the use of graphs.

A measure of the closeness of agreement between an individual result and the accepted value. An accurate result is in close agreement with the accepted value.

2021

International Journal of Measurement Technologies and Instrumentation Engineering, 2012

Uncertainty of measurement has attracted more research in recent past. In this paper, an attempt is made to explain the concept and need of measurment uncertainty. The uncertainty of measurement is related to measurement and calibration process only. The uncertainty of measurement(UOM) is applied in various application such as mechanical, chemical, electrical and civil testing equipments. The paper focus on difficulties in estimation of sources of uncertainty and their estimation. The gaps in the research are identified along with the scope of UOM in various application.The effects of qualitative factors can be possible using data collection through questionnaire.

IEEE Transactions on Instrumentation and Measurement, 2000

Against the tradition, which has considered measurement able to produce pure data on physical systems, the unavoidable role played by the modeling activity in measurement is increasingly acknowledged, particularly with respect to the evaluation of measurement uncertainty. This paper characterizes measurement as a knowledge-based process and proposes a framework to understand the function of models in measurement and to systematically analyze their influence in the production of measurement results and their interpretation. To this aim, a general model of measurement is sketched, which gives the context to highlight the unavoidable, although sometimes implicit, presence of models in measurement and, finally, to propose some remarks on the relations between models and measurement uncertainty, complementarily classified as due to the idealization implied in the models and their realization in the experimental setup.

Measurement, 2011

The paper discusses the problem of quantifying the contribution of systematic errors on the overall measurement uncertainty. After a brief review of the recommendations of the Guide to the Expression of Uncertainty in Measurement (GUM), and of some notable alternative methods already proposed in the literature, the authors outline a fully experimental approach, which is based on the ISO 5725 ideas, circumventing the adoption of the concepts of ''degree of belief'' and ''subjective probability'' which, instead, are central both in the GUM and in the proposed alternatives. As a matter of fact, the proposed experimental approach does not alter the mathematical framework of the GUM (differently from the alternative proposals), but imposes that the uncertainty is always an experimentally verifiable, or refutable, figure. Theoretical considerations are followed by the description of an experimental scheme to study the uncertainty due to systematic errors in generic instruments and, in particular, in ADC-based instruments. A major focus is given to experimental results obtained for the systematic gain error, which, in turn, plays a significant role in the overall error contribution. The ultimate goal of the paper is to provide a basis to promote reflections upon a central problem: how much subjectivity and objectivity should be allowed in an uncertainty evaluation.

2014

In the last few decades the role played by models and modeling activities has become a central topic in the scientific enterprise. In particular, it has been highlighted both that the development of models constitutes a crucial step for understanding the world and that the developed models operate as mediators between theories and the world. Such perspective is exploited here to cope with the issue as to whether error-based and uncertainty-based modeling of measurement are incompatible, and thus alternative with one another, as sometimes claimed nowadays. The crucial problem is whether assuming this standpoint implies definitely renouncing to maintain a role for truth and the related concepts, particularly accuracy, in measurement. It is argued here that the well known objections against true values in measurement, which would lead to refuse the concept of accuracy as non-operational, or to maintain it as only qualitative, derive from a not clear distinction between three distinct processes: the metrological characterization of measuring systems, their calibration, and finally measurement. Under the hypotheses that (1) the concept of true value is related to the model of a measurement process, (2) the concept of uncertainty is related to the connection between such model and the world, and (3) accuracy is a property of measuring systems (and not of measurement results) and uncertainty is a property of measurement results (and not of measuring systems), not only the compatibility but actually the conjoint need of error-based and uncertainty-based modeling emerges.