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DC Field | Value | Language |
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dc.contributor.author | Bharti, P | - |
dc.contributor.author | Yang, Q | - |
dc.contributor.author | Forbes, AB | - |
dc.contributor.author | Koucha, Y | - |
dc.date.accessioned | 2021-11-21T11:57:05Z | - |
dc.date.available | 2021-11-21T11:57:05Z | - |
dc.date.issued | 2021-12-08 | - |
dc.identifier | 26 | - |
dc.identifier.citation | Bharti, P., Yang, Q., Forbes, A.B. and Koucha, Y. (2021) 'UML Knowledge Model for Measurement Process Including Uncertainty of Measurement', International Journal of Metrology and Quality Engineering, 12, 26, pp. 1 - 10. doi: 10.1051/ijmqe/2021024. | en_US |
dc.identifier.issn | 2107-6839 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/23572 | - |
dc.description.abstract | Copyright © P. Bharti et al. 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. | - |
dc.description.sponsorship | Brunel University London. | en_US |
dc.format.extent | 1 - 10 | - |
dc.format.medium | Print-Electronic | - |
dc.language.iso | en_US | en_US |
dc.publisher | EDP Sciences | en_US |
dc.rights | Copyright © 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. | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0 | - |
dc.subject | UML | en_US |
dc.subject | measurement system | en_US |
dc.subject | knowledge representation | en_US |
dc.subject | uncertainty of measurement | en_US |
dc.subject | ontology | en_US |
dc.subject | calibration | en_US |
dc.title | UML Knowledge Model for Measurement Process Including Uncertainty of Measurement | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1051/ijmqe/2021024 | - |
dc.relation.isPartOf | International Journal of Metrology and Quality Engineering | - |
pubs.publication-status | Published | - |
pubs.volume | 12 | - |
dc.identifier.eissn | 2107-6847 | - |
Appears in Collections: | Dept of Mechanical and Aerospace Engineering Research Papers |
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