Shi, C., Panoutsos, G., Luo, B. et al. (3 more authors) (2019) Using multiple feature spaces-based deep learning for tool condition monitoring in ultra-precision manufacturing. IEEE Transactions on Industrial Electronics, 66 (5). pp. 3794-3803. ISSN 0278-0046
Abstract
Tool condition monitoring is critical in ultra-precision manufacturing in order to optimize the performance of the overall process, while maintaining the desired part quality. Recently, Deep Learning has been successfully applied in numerous classification tasks in manufacturing, often to forecast part quality. In this paper, a novel Deep Learning data-driven modeling framework is presented, which includes fusion of multiple stacked sparse autoencoders for tool condition monitoring in ultra-precision machining. The proposed computational framework consists of two main structures. A training model that is designed with the ability to process multiple parallel feature spaces to learn the lower-level features; and a feature fusion structure that is used to learn the higher-level features and associations to tool wear. To achieve this learning structure, a modified loss function is utilized that enhances the feature extraction and classification tasks. A dataset from a real manufacturing process is used to demonstrate the performance of the proposed framework. Experimental results and simulations show that the proposed method successfully classifies the ultra-precision machining case study with over 96% accuracy, while also outperforms comparable methodologies.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 IEEE. This is an author produced version of a paper subsequently published in IEEE Transactions on Industrial Electronics. Uploaded in accordance with the publisher's self-archiving policy. |
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) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 06 Jul 2018 14:13 |
Last Modified: | 07 Aug 2020 12:36 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Identification Number: | 10.1109/TIE.2018.2856193 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:133032 |