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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.16933 |
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| _version_ | 1866929693372973056 |
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| author | Chen, Zhiyi Maske, Harshal Upadhyay, Devesh Shui, Huanyi Huan, Xun Ni, Jun |
| author_facet | Chen, Zhiyi Maske, Harshal Upadhyay, Devesh Shui, Huanyi Huan, Xun Ni, Jun |
| contents | This paper presents a modeling-control synthesis to address the quality control challenges in multistage manufacturing systems (MMSs). A new feedforward control scheme is developed to minimize the quality variations caused by process disturbances in MMSs. Notably, the control framework leverages a stochastic deep Koopman (SDK) model to capture the quality propagation mechanism in the MMSs, highlighted by its ability to transform the nonlinear propagation dynamics into a linear one. Two roll-to-roll case studies are presented to validate the proposed method and demonstrate its effectiveness. The overall method is suitable for nonlinear MMSs and does not require extensive expert knowledge. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_16933 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Koopman-based Control of Quality Variation in Multistage Manufacturing Systems Chen, Zhiyi Maske, Harshal Upadhyay, Devesh Shui, Huanyi Huan, Xun Ni, Jun Systems and Control Machine Learning This paper presents a modeling-control synthesis to address the quality control challenges in multistage manufacturing systems (MMSs). A new feedforward control scheme is developed to minimize the quality variations caused by process disturbances in MMSs. Notably, the control framework leverages a stochastic deep Koopman (SDK) model to capture the quality propagation mechanism in the MMSs, highlighted by its ability to transform the nonlinear propagation dynamics into a linear one. Two roll-to-roll case studies are presented to validate the proposed method and demonstrate its effectiveness. The overall method is suitable for nonlinear MMSs and does not require extensive expert knowledge. |
| title | Deep Koopman-based Control of Quality Variation in Multistage Manufacturing Systems |
| topic | Systems and Control Machine Learning |
| url | https://arxiv.org/abs/2407.16933 |