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Correlation-based feature extraction from computer-aided design, case study on curtain airbags design
Department of Product Development, School of Engineering, Jönköping University, Sweden.
University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. (Virtual Manufacturing Processes)ORCID iD: 0000-0003-0899-8939
Department of Product Development, School of Engineering, Jönköping University, Sweden.
Autoliv AB, Vårgårda, Sweden.
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2022 (English)In: Computers in industry (Print), ISSN 0166-3615, E-ISSN 1872-6194, Vol. 138, article id 103634Article in journal (Refereed) Published
Abstract [en]

Many high-level technical products are associated with changing requirements, drastic design changes, lack of design information, and uncertainties in input variables which makes their design process iterative and simulation-driven. Regression models have been proven to be useful tools during design, altering the resource-intensive finite element simulation models. However, building regression models from computer-aided design (CAD) parameters is associated with challenges such as dealing with too many parameters and their low or coupled impact on studied outputs which ultimately requires a large training dataset. As a solution, extraction of hidden features from CAD is presented on the application of volume simulation of curtain airbags concerning geometric changes in design loops. After creating a prototype that covers all aspects of a real curtain airbag, its CAD parameters have been analyzed to find out the correlation between design parameters and volume as output. Next, using the design of the experiment latin hypercube sampling method, 100 design samples are generated and the corresponding volume for each design sample was assessed. It was shown that selected CAD parameters are not highly correlated with the volume which consequently lowers the accuracy of prediction models. Various geometric entities, such as the medial axis, are used to extract several hidden features (referred to as sleeping parameters). The correlation of the new features and their performance and precision through two regression analyses are studied. The result shows that choosing sleeping parameters as input reduces dimensionality and the need to use advanced regression algorithms, allowing designers to have more accurate predictions (in this case approximately 95%) with a reasonable number of samples. Furthermore, it was concluded that using sleeping parameters in regressionbased tools creates real-time prediction ability in the early development stage of the design process which could contribute to lower development lead time by eliminating design iterations. 

Place, publisher, year, edition, pages
Elsevier, 2022. Vol. 138, article id 103634
Keywords [en]
Feature extraction, CAD/CAE, Parametric models, Medial Axis, Design Automation, Machine Learning, Regression Analysis, Curtain Airbag
National Category
Vehicle Engineering Computational Mathematics Other Mechanical Engineering
Research subject
Virtual Manufacturing Processes
Identifiers
URN: urn:nbn:se:his:diva-20923DOI: 10.1016/j.compind.2022.103634ISI: 000772755800002Scopus ID: 2-s2.0-85124806561OAI: oai:DiVA.org:his-20923DiVA, id: diva2:1639275
Note

CC BY 4.0

Corresponding author: E-mail address: mohammad.rad@ju.se (A.R. Mohammad).

Available from: 2022-02-21 Created: 2022-02-21 Last updated: 2022-04-25Bibliographically approved

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Salomonsson, Kent

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