Abstract

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.

Highlights

  • Many attempts have been made in the past to develop a data model for measurement systems in metrology

  • This paper presents a knowledge representation model about information flow in the measurement system that includes an essential part of the measurement process: uncertainty of measurement results

  • It is important to note that the evaluation of calibration uncertainty for different measurements is very similar to the uncertainty evaluation in this case study

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Summary

Introduction

Many attempts have been made in the past to develop a data model for measurement systems in metrology These models include visual, analytical, and linguistic representations. Knowledge representation helps to extract the relevant knowledge from a precise data model by inference rules and designing an application model for that knowledge. This data model defines concepts and relationship of measurement system for the ontology development.

Knowledge representation for measurement system
Importance of uncertainty
Proposed knowledge model for measurement system
Stage 1
Stage 2
Stage 3
Case study
Uncertainty budget
Other type B uncertainty calculations
Sensitivity coefficients
Discussions and applications
Conclusion
Full Text
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