UML knowledge model for measurement process including uncertainty of measurement Article Swipe
Priyanka Bharti
,
Qingping Yang
,
Alistair Forbes
,
Yacine Koucha
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1051/ijmqe/2021024
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1051/ijmqe/2021024
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.
Related Topics
Concepts
Computer science
Unified Modeling Language
Process (computing)
Knowledge representation and reasoning
Ontology
Representation (politics)
Object (grammar)
Domain knowledge
Data mining
Uncertainty analysis
Measurement uncertainty
Software engineering
Industrial engineering
Artificial intelligence
Engineering
Simulation
Programming language
Software
Mathematics
Philosophy
Politics
Political science
Statistics
Epistemology
Law
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1051/ijmqe/2021024
- https://www.metrology-journal.org/articles/ijmqe/pdf/2021/01/ijmqe210016.pdf
- OA Status
- gold
- Cited By
- 3
- References
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4206043771
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4206043771Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1051/ijmqe/2021024Digital Object Identifier
- Title
-
UML knowledge model for measurement process including uncertainty of measurementWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-01-01Full publication date if available
- Authors
-
Priyanka Bharti, Qingping Yang, Alistair Forbes, Yacine KouchaList of authors in order
- Landing page
-
https://doi.org/10.1051/ijmqe/2021024Publisher landing page
- PDF URL
-
https://www.metrology-journal.org/articles/ijmqe/pdf/2021/01/ijmqe210016.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.metrology-journal.org/articles/ijmqe/pdf/2021/01/ijmqe210016.pdfDirect OA link when available
- Concepts
-
Computer science, Unified Modeling Language, Process (computing), Knowledge representation and reasoning, Ontology, Representation (politics), Object (grammar), Domain knowledge, Data mining, Uncertainty analysis, Measurement uncertainty, Software engineering, Industrial engineering, Artificial intelligence, Engineering, Simulation, Programming language, Software, Mathematics, Philosophy, Politics, Political science, Statistics, Epistemology, LawTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 2, 2023: 1Per-year citation counts (last 5 years)
- References (count)
-
2Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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