Int. J. Metrol. Qual. Eng.
Volume 12, 2021
Topical Issue - Advances in Metrology and Quality Engineering
|Number of page(s)||10|
|Published online||08 December 2021|
UML knowledge model for measurement process including uncertainty of measurement
Brunel University London, Uxbridge, UK
2 National Physical Laboratory, Teddington, UK
* Corresponding author: Priyanka.Bharti@brunel.ac.uk
Received in final form: 2 August 2021
Accepted: 18 October 2021
Measurement technology has made an enormous progress in the last decade. With the advent of knowledge representation, various object-oriented models for measurement systems have been developed in the past. Most common limitations of all these models were not incorporating the uncertainty in the measurement process. In this paper, we proposed an object-oriented model depicting the information and knowledge flow in the measurement process, including the measurement uncertainty. The model has three major object classes, namely measurement planning, measurement system and analysis & documentation. These are further classified into sub-classes and relationships amongst them. Attributes and operations are also defined within the classes. This gives a practical and conceptual view of knowledge in the form of object-model for measurement processes. A case study is presented which evaluates the uncertainty of the measurement of a 100 mm gauge block, using both Type A and Type B evaluation methods of the GUM approach.This case study is very similar to the evaluation of calibration uncertainty of CMM. This model can be converted into semantic knowledge representation such as ontology of measurement process domain. Other use of this model is to support the quality engineering in manufacturing industry and research.
Key words: UML / measurement system / knowledge representation / uncertainty of measurement / ontology / calibration
© P. Bharti et al., Published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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