Zotov, E., Tiwari, A. and Kadirkamanathan, V. orcid.org/0000-0002-4243-2501 (2021) Conditional StyleGAN modelling and analysis for a machining digital twin. Integrated Computer-Aided Engineering, 28 (4). pp. 399-415. ISSN 1069-2509
Abstract
Manufacturing digitalisation is a critical part of the transition towards Industry 4.0. Digital twin plays a significant role as the instrument that enables digital access to precise real-time information about physical objects and supports the optimisation of the related processes through conversion of the big data associated with them into actionable information. A number of frameworks and conceptual models has been proposed in the research literature that addresses the requirements and benefits of digital twins, yet their applications are explored to a lesser extent. A time-domain machining vibration model based on a generative adversarial network (GAN) is proposed as a digital twin component in this paper. The developed conditional StyleGAN architecture enables (1) the extraction of knowledge from existing models and (2) a data-driven simulation applicable for production process optimisation. A novel solution to the challenges in GAN analysis is then developed, where the comparison of maps of generative accuracy and sensitivity reveals patterns of similarity between these metrics. The sensitivity analysis is also extended to the mid-layer network level, identifying the sources of abnormal generative behaviour. This provides a sensitivity-based simulation uncertainty estimate, which is important for validation of the optimal process conditions derived from the proposed model.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 The Author(s). This is an author-produced version of a paper subsequently published in Integrated Computer-Aided Engineering. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Generative adversarial networks; digital twin; machining; simulation; time-domain signals |
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 ROYAL ACADEMY OF ENGINEERING (THE) nan ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL EP/P006930/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 09 Sep 2021 15:13 |
Last Modified: | 09 Sep 2021 15:13 |
Status: | Published |
Publisher: | IOS Press |
Refereed: | Yes |
Identification Number: | 10.3233/ica-210662 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:178037 |