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Automation of unstructured production environment by applying reinforcement learning
Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering. (Design Automation Laboratory)ORCID iD: 0000-0003-1745-3869
Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-7210-0209
Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering.
2023 (English)In: Frontiers in Manufacturing Technology, E-ISSN 2813-0359, Vol. 3Article in journal (Refereed) Published
Abstract [en]

Implementation of Machine Learning (ML) to improve product and production development processes poses a significant opportunity for manufacturing industries. ML has the capability to calibrate models with considerable adaptability and high accuracy. This capability is specifically promising for applications where classical production automation is too expensive, e.g., for mass customization cases where the production environment is uncertain and unstructured. To cope with the diversity in production systems and working environments, Reinforcement Learning (RL) in combination with lightweight game engines can be used from initial stages of a product and production development process. However, there are multiple challenges such as collecting observations in a virtual environment which can interact similar to a physical environment. This project focuses on setting up RL methodologies to perform path-finding and collision detection in varying environments. One case study is human assembly evaluation method in the automobile industry which is currently manual intensive to investigate digitally. For this case, a mannequin is trained to perform pick and place operations in varying environments and thus automating assembly validation process in early design phases. The next application is path-finding of mobile robots including an articulated arm to perform pick and place operations. This application is expensive to setup with classical methods and thus RL enables an automated approach for this task as well.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2023. Vol. 3
Keywords [en]
Reinforcement Learning, Unity Game Engine, Mobile Robot, Mannequin, Production Environment
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
URN: urn:nbn:se:liu:diva-195616DOI: 10.3389/fmtec.2023.1154263OAI: oai:DiVA.org:liu-195616DiVA, id: diva2:1773124
Funder
Vinnova, 2020-05173Available from: 2023-06-22 Created: 2023-06-22 Last updated: 2024-05-21Bibliographically approved
In thesis
1. Adaptive Automation for Customized Products
Open this publication in new window or tab >>Adaptive Automation for Customized Products
2024 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

In today’s fast-paced industrial landscape, the drive for greater efficiency and flexibility in product development has sparked significant interest in innovative automation technologies. This thesis explores the usefulness of various automation techniques for customized products such as Knowledge-Based Engineering (KBE), Multidisciplinary Optimization (MDO) and machine learning frameworks.

The research begins by establishing an automated framework for fixture design, combining design automation and MDO to streamline the design process. It then moves to optimizing gas turbines, introducing an automation framework that merges CAD templates with KBE principles.

For complex and unstructured production, this thesis explores the use of Reinforcement Learning (RL) to tackle challenges in unstructured manufacturing. By utilizing lightweight physics-based engines and RL, the research advances automated assembly validation and mobile robot operations, pushing the boundaries of adaptive production automation. Furthermore, a framework is developed, which integrates smoothly with industrial robotic platforms showcases practical automation solutions and highlights the adaptability and applicability of digital twin technology in real-world situations.

This thesis contributes to the field of product development by providing innovative solutions that are rooted in multidisciplinary research. It bridges the theoretical and practical aspects of automation with solutions that overcomes the obstacles to realize seamless integration between digital and physical realities in a manufacturing context.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2024. p. 46
Series
Linköping Studies in Science and Technology. Licentiate Thesis, ISSN 0280-7971 ; 1997
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:liu:diva-203626 (URN)10.3384/9789180756785 (DOI)9789180756778 (ISBN)9789180756785 (ISBN)
Presentation
2024-06-14, ACAS, A Building, Campus Valla, Linköping, 10:15 (English)
Opponent
Supervisors
Available from: 2024-05-21 Created: 2024-05-21 Last updated: 2024-05-29Bibliographically approved

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Nambiar, SanjayWiberg, AntonTarkian, Mehdi

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