Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.648940
Title: Metrology-based process modelling framework for digital and physical measurement environments integration
Author: Zhang, Xi
Awarding Body: University of Bath
Current Institution: University of Bath
Date of Award: 2013
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Abstract:
Process modelling is the activity of constructing and analyzing models, which are applicable and useful to solve predefined problems. It allows engineering process to be analyzed, and consequently leads to quality and efficiency improvement. As metrology becomes increasingly important in modern manufacturing, process modelling based on measurement techniques and operations becomes necessary and valuable. Measurement uncertainty, which is obtained from measurement operation, is regarded as a key factor in metrology-based process modelling. By analyzing measurement uncertainty, metrology-based process models can tangibly improve manufacturing quality and efficiency, improve the actualize communication between design and manufacturing, and, ultimately, achieve product lifecycle integration between design, manufacturing and verification functions. Digital measurement models can simulate measurement process, and predict task-specific measurement uncertainty in the digital environment, before carrying out capital-consuming physical measurements. However, the integration between digital and physical measurement environments is not fully approved. Measurement uncertainty predicted by the digital measurement model may show little practical significance with that obtained from physical measurements. The quality of digital measurement result highly relies on input quantities loaded into the digital measurement model. And it is hardly possible to verify digital measurement results for all of the measurement scenarios because of the high variability and complexity of inspected features and measurement tasks. This research has reviewed the fundamental technologies relating to measurement process modelling and measurement uncertainty evaluation in a digital environment, especially for coordinate measurement machines (CMMs). An initial verification work has been carried out by ‘measuring’ small features on a large-volume component in a realistic shop floor environment. This verification work has realized the limitations of the digital measurement model, and the challenge of integrating digital and physical measurement environments. Based on the initial verification work, a Measurement Planning and Implementation Framework has been proposed, aiming to analyse and improve the relationship between digital and physical measurement environments. The Framework is deployed with the statistics methods to analyse measurement uncertainty obtained from the digital and the physical measurement environments, and quantitatively predict influence levels of measurement uncertainty contributors. The verification work of the Framework has been carried out in a finely-control laboratory environment with environmental control. The robustness of the Framework has been evaluated, indicating the potential of deploying statistical methods for measurement uncertainty analysis, which extends the utilization of measurement uncertainty for decision-making processes. The contributions to knowledge of the research include: (1) Verification of the performance of a digital CMM model under meaningful measurement scenarios; (2) Development of a metrology-based process modeling framework to integrate digital and physical measurement environments through quantitative measurement uncertainty analysis.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.648940  DOI: Not available
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