Development of data models and adaptation strategy for self-configuring production systems
Manufacturing intelligence is the ability to gather and analyse data for decision-making in production systems towards reaching an objective. This project involves introducing intelligence in production systems for achieving self-configuration. This is done by conceptualising and developing the inte...
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| Format: | Thesis (University of Nottingham only) |
| Language: | English |
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2024
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| Online Access: | https://eprints.nottingham.ac.uk/79521/ |
| _version_ | 1848801124423827456 |
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| author | Rehman, Hamood Ur |
| author_facet | Rehman, Hamood Ur |
| author_sort | Rehman, Hamood Ur |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Manufacturing intelligence is the ability to gather and analyse data for decision-making in production systems towards reaching an objective. This project involves introducing intelligence in production systems for achieving self-configuration. This is done by conceptualising and developing the intelligent components that act as building blocks of the self-configuring production systems and their application in the system. The approach taken for the project is iterative and built from the bottom-up. A classification tool is developed to study the capability of self-configuration in the current industrial production system, followed by insight gathered through surveys. The theoretical aspects involving self-configuration are discussed, and a general adaptation strategy is developed that integrates self-configuration capabilities in the intelligent components. These components are then manipulated and controlled through the use of technologies. Tools and techniques involving asset administration shell, state charts/state machines, multi-agent system, information model and machine learning approaches were studied. These technologies were implemented to achieve self-configuration, leveraging data gathered during operation. This research is applied to use cases of an industrial leak test equipment MALT and on a force testing station of the PRIME assembly system. The dissemination of work is highlighted, and future possibilities are expanded. |
| first_indexed | 2025-11-14T21:02:28Z |
| format | Thesis (University of Nottingham only) |
| id | nottingham-79521 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T21:02:28Z |
| publishDate | 2024 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-795212024-12-10T04:40:06Z https://eprints.nottingham.ac.uk/79521/ Development of data models and adaptation strategy for self-configuring production systems Rehman, Hamood Ur Manufacturing intelligence is the ability to gather and analyse data for decision-making in production systems towards reaching an objective. This project involves introducing intelligence in production systems for achieving self-configuration. This is done by conceptualising and developing the intelligent components that act as building blocks of the self-configuring production systems and their application in the system. The approach taken for the project is iterative and built from the bottom-up. A classification tool is developed to study the capability of self-configuration in the current industrial production system, followed by insight gathered through surveys. The theoretical aspects involving self-configuration are discussed, and a general adaptation strategy is developed that integrates self-configuration capabilities in the intelligent components. These components are then manipulated and controlled through the use of technologies. Tools and techniques involving asset administration shell, state charts/state machines, multi-agent system, information model and machine learning approaches were studied. These technologies were implemented to achieve self-configuration, leveraging data gathered during operation. This research is applied to use cases of an industrial leak test equipment MALT and on a force testing station of the PRIME assembly system. The dissemination of work is highlighted, and future possibilities are expanded. 2024-12-10 Thesis (University of Nottingham only) NonPeerReviewed application/pdf en cc_by https://eprints.nottingham.ac.uk/79521/1/Rehman%2C%20Hamood%20%5B20240963%5D%2C%20Accepted%20PhD%20Thesis.pdf Rehman, Hamood Ur (2024) Development of data models and adaptation strategy for self-configuring production systems. PhD thesis, University of Nottingham. Manufacturing intelligence; Production systems; Decision-making; Adaptation strategy; Intelligent components |
| spellingShingle | Manufacturing intelligence; Production systems; Decision-making; Adaptation strategy; Intelligent components Rehman, Hamood Ur Development of data models and adaptation strategy for self-configuring production systems |
| title | Development of data models and adaptation strategy for self-configuring production systems |
| title_full | Development of data models and adaptation strategy for self-configuring production systems |
| title_fullStr | Development of data models and adaptation strategy for self-configuring production systems |
| title_full_unstemmed | Development of data models and adaptation strategy for self-configuring production systems |
| title_short | Development of data models and adaptation strategy for self-configuring production systems |
| title_sort | development of data models and adaptation strategy for self-configuring production systems |
| topic | Manufacturing intelligence; Production systems; Decision-making; Adaptation strategy; Intelligent components |
| url | https://eprints.nottingham.ac.uk/79521/ |