Papananias, M., McLeay, T.E., Mahfouf, M. orcid.org/0000-0002-7349-5396 et al. (1 more author) (2022) A Bayesian information fusion approach for end product quality estimation using machine learning and on-machine probing. Journal of Manufacturing Processes, 76. pp. 475-485. ISSN 1526-6125
Abstract
There is an increasing demand for manufacturing processes to improve product quality and production rates while minimising the costs. The quality of the products is influenced by several sources of errors introduced during the series of manufacturing operations. These errors accumulate over these multiple stages of manufacturing. Therefore, monitoring systems for product health utilising data and information from different sources and manufacturing stages is a key factor to meet these growing demands. This paper addresses the process of combining new measurement data or information with machine learning-based prediction information obtained as each product goes through a series of manufacturing steps to update the conditional probability distribution of the end product quality during manufacturing. A Bayesian approach is adopted in obtaining an updated posterior distribution of the end product quality given new information from subsequent measurements, and, in particular, On-Machine Probing (OMP). Following the steps of heat treatment, machining, and OMP, the posterior distribution of the previous step can be considered as the new prior distribution to obtain an updated posterior distribution of the product condition as new metrological information becomes available. It is demonstrated that the resulting posterior estimates can lead to more efficient product condition monitoring in multistage manufacturing.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 Published by Elsevier Ltd on behalf of The Society of Manufacturing Engineers. This is an author produced version of a paper subsequently published in Journal of Manufacturing Processes. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | Bayesian inference; Machine learning; Information fusion; Multistage manufacturing process (MMP); On-machine probing (OMP); Uncertainty of measurement |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Funding Information: | Funder Grant number Engineering and Physical Sciences Research Council EP/P006930/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 11 May 2022 13:37 |
Last Modified: | 25 Feb 2023 01:13 |
Status: | Published |
Publisher: | Elsevier BV |
Refereed: | Yes |
Identification Number: | 10.1016/j.jmapro.2022.01.020 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:186737 |
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Filename: MM Probabilistic Framework JMP 2022.pdf
Licence: CC-BY-NC-ND 4.0