liu.seSearch for publications in DiVA
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Adaptive Automation for Customized Products
Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0003-1745-3869
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: urn:nbn:se:liu:diva-203626DOI: 10.3384/9789180756785ISBN: 9789180756778 (print)ISBN: 9789180756785 (electronic)OAI: oai:DiVA.org:liu-203626DiVA, id: diva2:1859311
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
List of papers
1. Multidisciplinary Automation in Design of Turbine Vane Cooling Channels
Open this publication in new window or tab >>Multidisciplinary Automation in Design of Turbine Vane Cooling Channels
Show others...
2024 (English)In: International Journal of Turbomachinery, Propulsion and Power, ISSN 2504-186X, Vol. 9, no 1, article id 7Article in journal (Refereed) Published
Abstract [en]

In the quest to enhance the efficiency of gas turbines, there is a growing demand for innovative solutions to optimize high-pressure turbine blade cooling. However, the traditional methods for achieving this optimization are known for their complexity and time-consuming nature. We present an automation framework to streamline the design, meshing, and structural analysis of cooling channels, achieving design automation at both the morphological and topological levels. This framework offers a comprehensive approach for evaluating turbine blade lifetime and enabling multidisciplinary design analyses, emphasizing flexibility in turbine cooling design through high-level CAD templates and knowledge-based engineering. The streamlined automation process, supported by a knowledge base, ensures continuity in both the mesh and structural simulation automations, contributing significantly to advancements in gas turbine technology.

Place, publisher, year, edition, pages
MDPI, 2024
Keywords
multidisciplinary automation, design automation, mesh automation, knowledge-based engineering, turbine vane cooling design
National Category
Mechanical Engineering
Identifiers
urn:nbn:se:liu:diva-201145 (URN)10.3390/ijtpp9010007 (DOI)001192494000001 ()
Funder
Vinnova, 2020-04251
Note

Funding: VINNOVA

Available from: 2024-02-23 Created: 2024-02-23 Last updated: 2024-05-21Bibliographically approved
2. Autofix – Automated Design of Fixtures
Open this publication in new window or tab >>Autofix – Automated Design of Fixtures
Show others...
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
Keywords
design automation, design optimisation, knowledge-based engineering (KBE), fixtures, robotic simulation
National Category
Other Mechanical Engineering
Identifiers
urn:nbn:se:liu:diva-195445 (URN)10.1017/pds.2022.56 (DOI)2-s2.0-85131360012 (Scopus ID)
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
3. Automation of unstructured production environment by applying reinforcement learning
Open this publication in new window or tab >>Automation of unstructured production environment by applying reinforcement learning
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
Keywords
Reinforcement Learning, Unity Game Engine, Mobile Robot, Mannequin, Production Environment
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:liu:diva-195616 (URN)10.3389/fmtec.2023.1154263 (DOI)
Funder
Vinnova, 2020-05173
Available from: 2023-06-22 Created: 2023-06-22 Last updated: 2024-05-21Bibliographically approved

Open Access in DiVA

fulltext(8091 kB)43 downloads
File information
File name FULLTEXT02.pdfFile size 8091 kBChecksum SHA-512
e5605bb54f66798f1a06f50226bef4ef3e628e29da021219e19249c8facf3091abbf94fe8d7c300371ecce73b43516f65ef0fe3d3c10756e5c56ce429891b48c
Type fulltextMimetype application/pdf
Order online >>

Other links

Publisher's full text

Authority records

Nambiar, Sanjay

Search in DiVA

By author/editor
Nambiar, Sanjay
By organisation
Product RealisationFaculty of Science & Engineering
Production Engineering, Human Work Science and Ergonomics

Search outside of DiVA

GoogleGoogle Scholar
Total: 43 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 347 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf