Papananias, M. orcid.org/0000-0001-7121-9681, McLeay, T.E., Mahfouf, M. et al. (1 more author) (2019) An Intelligent Metrology Informatics System based on Neural Networks for Multistage Manufacturing Processes. Procedia CIRP, 82. pp. 444-449. ISSN 2212-8271
Abstract
The ability to gather manufacturing data from various workstations has been explored for several decades and the advances in sensory and data acquisition techniques have led to the increasing availability of high-dimensional data. This paper presents an intelligent metrology informatics system to extract useful information from Multistage Manufacturing Process (MMP) data and predict part quality characteristics such as true position and circularity using neural networks. The input data include the tempering temperature, material conditions, force and vibration while the output data include comparative coordinate measurements. The effectiveness of the proposed method is demonstrated using experimental data from a MMP.
Metadata
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Copyright, Publisher and Additional Information: | © 2019 The Author(s). Published by Elsevier B.V. Available under the terms of the CC by-NC-ND license 4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) | ||||
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: |
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Depositing User: | Symplectic Sheffield | ||||
Date Deposited: | 18 Jul 2019 09:20 | ||||
Last Modified: | 18 Jul 2019 12:00 | ||||
Status: | Published | ||||
Publisher: | Elsevier BV | ||||
Refereed: | Yes | ||||
Identification Number: | https://doi.org/10.1016/j.procir.2019.04.148 |