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Autofix – Automated Design of Fixtures
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.
Linköping University.
Linköping University.
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2022 (English)Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a framework to develop the automated design of fixtures using the combination ofdesign automation (DA), multidisciplinary optimization and robotic simulation. MDO necessitates the useof concurrent and parametric designs which are created by DA and knowledge-based engineering tools. Thisapproach is designed to decrease the time and cost of the fixture design process by increasing the degree ofautomation. AutoFix provides methods and tools for automatically optimizing resource-intensive fixturedesign utilizing digital tools from different disciplines.

Place, publisher, year, edition, pages
Cambridge University Press, 2022. Vol. 2, p. 543-552
Keywords [en]
design automation, design optimisation, knowledge-based engineering (KBE), fixtures, robotic simulation
National Category
Other Mechanical Engineering
Identifiers
URN: urn:nbn:se:liu:diva-195445DOI: 10.1017/pds.2022.56Scopus ID: 2-s2.0-85131360012OAI: oai:DiVA.org:liu-195445DiVA, id: diva2:1771387
Conference
International Design Conference - Design 2022, 23 - 26 May, 2022
Available from: 2023-06-20 Created: 2023-06-20 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, SanjayTarkian, Mehdi

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