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Comparison of Design Automation and Machine Learning algorithms for creation of easily modifiable splines
Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering.
2020 (English)In: Proceedings of NordDesign 2020, Lyngby, Denmark, 12th - 14th August 2020 / [ed] Mortensen, N.H.; Hansen, C.T. and Deininger, M., The Design Society, 2020Conference paper, Published paper (Refereed)
Abstract [en]

In order to enable easy modification of results from a design optimization process in a CAD tool, a flexible representation of the geometry is needed. This is not always trivial however, since many file formats are not importable as modifiable geometry into the CAD tool, and if they are, they might not represent the geometry in a way that enables easy modification. To mitigate this problem a design automation (DA) and a machine learning (ML) approach are developed and compared using a test case from an optimization process used to optimize hose routing in tight spaces. In the test case used, the geometry from the optimization process consists of center curves represented as a large number of points. To enable easy modification a more flexible representation is needed such as a spline with a few well-placed control points. Both the DA and ML approach can approximate center curves from the optimization process as splines containing a varying number of control points but do show different properties. The DA approach is considerably slower than the ML but adds a lot of flexibility regarding accuracy and the number of control points used.

Place, publisher, year, edition, pages
The Design Society, 2020.
Series
DS ; 101
Keywords [en]
Design Automation, Machine Learning, Computer aided Design, Optimization
National Category
Mechanical Engineering
Identifiers
URN: urn:nbn:se:liu:diva-184173DOI: 10.35199/NORDDESIGN2020.55ISBN: 9781912254088 (print)OAI: oai:DiVA.org:liu-184173DiVA, id: diva2:1650159
Conference
NordDesign
Available from: 2022-04-06 Created: 2022-04-06 Last updated: 2022-04-07
In thesis
1. Exploring Data-Driven Methods to Enhance Usability of Design Optimization
Open this publication in new window or tab >>Exploring Data-Driven Methods to Enhance Usability of Design Optimization
2022 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Developing high-performing products at a low cost while keeping development time down is increasingly important in today’s competitive market. The current state presents a need for efficient product development processes. One of the challenges is knowledge often being limited in early stages where the cost of making changes is still relatively low. As the process progresses more knowledge is gained to better support decisions; however the cost of making changes increases, limiting the design freedom. To increase knowledge while retaining design freedom, several computer-based tools are available to both generate and evaluate designs in order to make iterations faster and more accurate.

Design Optimization (DO) can be utilized to explore the design space and find optimal designs. A Computer-Aided Design (CAD) model is often required as input to analysis tools evaluating the designs. By utilizing Design Automation (DA) several tasks involved in creation and modification of CAD models can be automated. For this reason, DA is sometimes considered an enabler for DO although its use is far wider, covering several aspects of the design process mainly focusing on automating repetitive and routine tasks.

Machine Learning and other data-driven methods are becoming increasingly viable in the context of DO and DA. This thesis explores the use of data-driven methods to enhance the usability of DO in different ways such as a faster process, new use-cases, or a more integrated and automated process.  

Literature in the area is reviewed, identifying applications, trends and challenges. Furthermore, two support tools are developed, incorporating data-driven methods tied to an industrial case. The applications focus on parameterizing geometry and predicting design performance respectively. Potential benefits, limitations, and challenges are discussed based on the literature review and insights from the two support tools. The focus of the thesis is mainly on how data-driven methods can facilitate automation and integration in the design process, specifically for complex products requiring significant engineering efforts.  

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2022. p. 41
Series
Linköping Studies in Science and Technology. Licentiate Thesis, ISSN 0280-7971 ; 1931
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:liu:diva-184201 (URN)10.3384/9789179292782 (DOI)9789179292775 (ISBN)9789179292782 (ISBN)
Presentation
2022-04-08, ACAS, A-building, Campus Valla, Linköping, 10:15 (English)
Opponent
Supervisors
Note

Funding agencies: The work is part the research project AutoPack (2017-03065) which is funded by Sweden’s innovation agency Vinnova and the partnership program Strategic Vehicle Research and Innovation (FFI).

Available from: 2022-04-07 Created: 2022-04-07 Last updated: 2022-04-12Bibliographically approved

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Gustafsson, ErikPersson, JohanÖlvander, Johan

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