Papananias, M. orcid.org/0000-0001-7121-9681, McLeay, T., Mahfouf, M. et al. (1 more author) (2019) An intelligent metrology informatics system based on neural networks for multistage manufacturing processes. In: Procedia CIRP. 17th CIRP Conference on Modelling of Machining Operations, 13-14 Jun 2019, Sheffield, UK. Elsevier , pp. 444-449.
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
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 The Authors. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | Multistage Manufacturing; Intelligent/Smart Manufacturing; Manufacturing Informatics; Artificial Neural Networks |
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 Science Research Council (EPSRC) EP/P006930/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 25 Jun 2019 13:31 |
Last Modified: | 03 May 2024 16:14 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.procir.2019.04.148 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:147763 |