AI-driven digital twin for autonomous web tension control in Roll-to-Roll manufacturing system
Abstract While the roll-to-roll manufacturing process plays a key role in high-throughput and cost-effective production, precise web tension control remains a critical challenge due to the dynamic interaction of web materials and roller mechanics. To address these challenges, this study proposes an...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-09813-2 |
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| author | Anton Nailevich Gafurov Sooyoung Lee Uzair Ali Muhammad Irfan Inyoung Kim Taik-Min Lee |
| author_facet | Anton Nailevich Gafurov Sooyoung Lee Uzair Ali Muhammad Irfan Inyoung Kim Taik-Min Lee |
| author_sort | Anton Nailevich Gafurov |
| collection | DOAJ |
| description | Abstract While the roll-to-roll manufacturing process plays a key role in high-throughput and cost-effective production, precise web tension control remains a critical challenge due to the dynamic interaction of web materials and roller mechanics. To address these challenges, this study proposes an AI-driven digital twin framework for autonomous web tension control optimization. The proposed method integrates Bayesian optimization with Gaussian process modeling to efficiently explore and adjust proportional and integral control parameters. While the operation of the roll-to-roll system is managed through a real-time client-server communication, system responses are designed to be iteratively refined by the proposed surrogate model. Experimental validation on an actual roll-to-roll manufacturing system demonstrates that the optimized control strategy significantly reduces tension variation and improves system stability. The proposed method highlights the potential of AI-integrated digital twins in autonomous manufacturing, which can offer a scalable solution for a variety of industrial applications. |
| format | Article |
| id | doaj-art-54e6972b625542a6b9ddc1e2ab97dbf2 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-54e6972b625542a6b9ddc1e2ab97dbf22025-08-20T04:01:26ZengNature PortfolioScientific Reports2045-23222025-07-0115111710.1038/s41598-025-09813-2AI-driven digital twin for autonomous web tension control in Roll-to-Roll manufacturing systemAnton Nailevich Gafurov0Sooyoung Lee1Uzair Ali2Muhammad Irfan3Inyoung Kim4Taik-Min Lee5Department of Advanced Battery Manufacturing Systems, Korea Institute of Machinery and MaterialsSchool of Mechanical Engineering, Chung-Ang UniversityDepartment of Advanced Battery Manufacturing Systems, Korea Institute of Machinery and MaterialsDepartment of Advanced Battery Manufacturing Systems, Korea Institute of Machinery and MaterialsDepartment of Advanced Battery Manufacturing Systems, Korea Institute of Machinery and MaterialsDepartment of Advanced Battery Manufacturing Systems, Korea Institute of Machinery and MaterialsAbstract While the roll-to-roll manufacturing process plays a key role in high-throughput and cost-effective production, precise web tension control remains a critical challenge due to the dynamic interaction of web materials and roller mechanics. To address these challenges, this study proposes an AI-driven digital twin framework for autonomous web tension control optimization. The proposed method integrates Bayesian optimization with Gaussian process modeling to efficiently explore and adjust proportional and integral control parameters. While the operation of the roll-to-roll system is managed through a real-time client-server communication, system responses are designed to be iteratively refined by the proposed surrogate model. Experimental validation on an actual roll-to-roll manufacturing system demonstrates that the optimized control strategy significantly reduces tension variation and improves system stability. The proposed method highlights the potential of AI-integrated digital twins in autonomous manufacturing, which can offer a scalable solution for a variety of industrial applications.https://doi.org/10.1038/s41598-025-09813-2AI-driven digital twinRoll-to-roll processAutonomous manufacturingBayesian optimization |
| spellingShingle | Anton Nailevich Gafurov Sooyoung Lee Uzair Ali Muhammad Irfan Inyoung Kim Taik-Min Lee AI-driven digital twin for autonomous web tension control in Roll-to-Roll manufacturing system Scientific Reports AI-driven digital twin Roll-to-roll process Autonomous manufacturing Bayesian optimization |
| title | AI-driven digital twin for autonomous web tension control in Roll-to-Roll manufacturing system |
| title_full | AI-driven digital twin for autonomous web tension control in Roll-to-Roll manufacturing system |
| title_fullStr | AI-driven digital twin for autonomous web tension control in Roll-to-Roll manufacturing system |
| title_full_unstemmed | AI-driven digital twin for autonomous web tension control in Roll-to-Roll manufacturing system |
| title_short | AI-driven digital twin for autonomous web tension control in Roll-to-Roll manufacturing system |
| title_sort | ai driven digital twin for autonomous web tension control in roll to roll manufacturing system |
| topic | AI-driven digital twin Roll-to-roll process Autonomous manufacturing Bayesian optimization |
| url | https://doi.org/10.1038/s41598-025-09813-2 |
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