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
  • [21] QUANTIFYING MODEL UNCERTAINTY USING MEASUREMENT UNCERTAINTY STANDARDS
    Du, Xiaoping
    Shah, Harsheel
    PROCEEDINGS OF THE ASME INTERNATIONAL DESIGN ENGINEERING TECHNICAL CONFERENCES AND COMPUTERS AND INFORMATION IN ENGINEERING CONFERENCE, 2011, VOL 5, PTS A AND B, 2012, : 1161 - 1167
  • [22] Evaluation of measurement uncertainty from a nonstationary process
    Zhang, Nien Fan
    MEASUREMENT SCIENCE AND TECHNOLOGY, 2019, 30 (06)
  • [23] The general process to evaluate uncertainty in EMC measurement
    Tan, HF
    Liu, P
    Sha, F
    2002 3RD INTERNATIONAL SYMPOSIUM ON ELECTROMAGNETIC COMPATIBILITY, 2002, : 226 - 229
  • [24] Metrology and process control: dealing with measurement uncertainty
    Potzick, James
    METROLOGY, INSPECTION, AND PROCESS CONTROL FOR MICROLITHOGRAPHY XXIV, 2010, 7638
  • [25] Measurement error, measurement uncertainty, and measurand uncertainty
    Chunovkina, AG
    MEASUREMENT TECHNIQUES, 2000, 43 (07) : 581 - 586
  • [26] Measurement error, measurement uncertainty, and measurand uncertainty
    A. G. Chunovkina
    Measurement Techniques, 2000, 43 : 581 - 586
  • [27] Including covariances in calibration to obtain better measurement uncertainty estimates
    Chinellato, O
    Achermann, E
    Bröker, O
    SIAM JOURNAL ON SCIENTIFIC COMPUTING, 2004, 26 (02): : 523 - 536
  • [28] A process for the analysis of the physics of measurement and determination of measurement uncertainty in EMC test procedures
    Bronaugh, EL
    Osburn, JDM
    IEEE 1996 INTERNATIONAL SYMPOSIUM ON ELECTROMAGNETIC COMPATIBILITY - EMC: SILICON TO SYSTEMS, SYMPOSIUM RECORD, 1996, : 245 - 249
  • [29] Confirmation of the reproducibility of the measurement process by evaluating the measurement uncertainty in accordance with the industry standard
    Loktev, D. A.
    Kapitanov, A., V
    Egorov, S. B.
    Moklyachenko, A., V
    INTERNATIONAL CONFERENCE ON MODERN TRENDS IN MANUFACTURING TECHNOLOGIES AND EQUIPMENT (ICMTMTE) 2020, 2020, 971
  • [30] Possibility expression of measurement uncertainty in a very limited knowledge
    Mauris, Gilles
    2006 IEEE International Workshop on Advanced Methods for Uncertainty Estimation, 2006, : 19 - 22