UML knowledge model for measurement process including uncertainty of measurement

被引:0
|
作者
Bharti P. [1 ]
Yang Q. [1 ]
Forbes A.B. [2 ]
Koucha Y. [1 ]
机构
[1] Brunel University London, Uxbridge
[2] National Physical Laboratory, Teddington
关键词
Calibration; Knowledge representation; Measurement system; Ontology; UML; Uncertainty of measurement;
D O I
10.1051/ijmqe/2021024
中图分类号
学科分类号
摘要
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. © P. Bharti et al., Published by EDP Sciences, 2021.
引用
收藏
相关论文
共 50 条
  • [1] Transistor Model Verification Including Measurement Uncertainty
    Williams, Dylan F.
    Chamberlin, Richard A.
    Zhao, Wei
    Cheron, Jerome
    Urteaga, Miguel E.
    IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES, 2016, 64 (11) : 3927 - 3933
  • [2] Expressing Measurement Uncertainty in OCL/UML Datatypes
    Bertoa, Manuel F.
    Moreno, Nathalie
    Barquero, Gala
    Burgueno, Loli
    Troya, Javier
    Vallecillo, Antonio
    MODELLING FOUNDATIONS AND APPLICATIONS (ECMFA 2018), 2018, 10890 : 46 - 62
  • [3] Incorporating measurement uncertainty into OCL/UML primitive datatypes
    Bertoa, Manuel F.
    Burgueno, Loli
    Moreno, Nathalie
    Vallecillo, Antonio
    SOFTWARE AND SYSTEMS MODELING, 2020, 19 (05): : 1163 - 1189
  • [4] Incorporating measurement uncertainty into OCL/UML primitive datatypes
    Manuel F. Bertoa
    Loli Burgueño
    Nathalie Moreno
    Antonio Vallecillo
    Software and Systems Modeling, 2020, 19 : 1163 - 1189
  • [5] Verification of a Foundry-Developed Transistor Model Including Measurement Uncertainty
    Williams, Dylan
    Zhao, Wei
    Chamberlin, Richard A.
    Cheron, Jerome
    Urteaga, Miguel
    2016 87TH ARFTG MICROWAVE MEASUREMENT CONFERENCE (ARFTG), 2016,
  • [6] KNOWLEDGE AND MEASUREMENT PROCESS
    BENIOFF, P
    NUOVO CIMENTO, 1964, 34 (06): : 1596 - +
  • [7] Estimating uncertainty of a measurement process
    Chang, Ning
    Lambert, Jim
    2007 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE FOR MEASUREMENT SYSTEMS AND APPLICATIONS, 2007, : 9 - +
  • [8] Thermodynamic uncertainty relations including measurement and feedback
    Potts, Patrick P.
    Samuelsson, Peter
    PHYSICAL REVIEW E, 2019, 100 (05)
  • [9] Uncertainty Measurement for a Tolerance Knowledge Base
    Qin, Bin
    Zeng, Fanping
    Yan, Kesong
    INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS, 2020, 28 (02) : 331 - 357
  • [10] Measurement network design including traveltime determinations to minimize model prediction uncertainty
    Janssen, Gijs M. C. M.
    Valstar, Johan R.
    van der Zee, Sjoerd E. A. T. M.
    WATER RESOURCES RESEARCH, 2008, 44 (02)