Data-Driven Support Vector Machine to Predict Thin-Walled Tube Energy Absorbers Behavior

Mostafa Ghasemi, Mohammad Silani*, Vahid Yaghoubi, Franco Concli

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)
15 Downloads (Pure)

Abstract

To design a more efficient energy absorber, it is critical to evaluate how changing the design parameters affects its performance, and also determine each one’s order of significance. In this paper, using a new approach, the behavior and response of straight, double-tapered, and triple-tapered thin-walled tubes with rectangular cross sections under axial and dynamic loading are investigated by performing a sensitivity analysis on a support vector machine (SVM) as a surrogate machine learning model. First, a finite element model of the energy absorber is constructed and validated with available experimental and theoretical studies. Next, a design of experiments was developed using the Sobol series sampling method and an appropriate dataset was created. This information is then used to develop an SVM model to predict the initial peak load and mean load of tubes. The accuracy of the machine learning created in this study is then assessed, and it is demonstrated that the developed model can precisely predict the performance of the absorber. The machine learning model is then subjected to a Sobol sensitivity analysis, and the outcomes are compared to those of the parametric study. The results suggest that the thickness of the tube has a stronger effect on the absorber performance than other geometric parameters. Comparing the effects of different material parameters on the behavior of tubes, the results show that yield strength has the greatest impact on the response of the energy absorber. It is also observed that the tapered tubes have a much lower initial peak load compared to straight ones.

Original languageEnglish
Title of host publicationTowards a Smart, Resilient and Sustainable Industry - Proceedings of the 2nd International Symposium on Industrial Engineering and Automation ISIEA 2023
EditorsYuri Borgianni, Dominik T. Matt, Margherita Molinaro, Guido Orzes
PublisherSpringer
Pages642-654
Number of pages13
ISBN (Print)9783031382734
DOIs
Publication statusPublished - 2023
EventTowards a Smart, Resilient and Sustainable Industry - Proceedings of the 2nd International Symposium on Industrial Engineering and Automation ISIEA 2023 - Bolzano, Italy
Duration: 22 Jun 202323 Jun 2023

Publication series

NameLecture Notes in Networks and Systems
Volume745 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

ConferenceTowards a Smart, Resilient and Sustainable Industry - Proceedings of the 2nd International Symposium on Industrial Engineering and Automation ISIEA 2023
Country/TerritoryItaly
CityBolzano
Period22/06/2323/06/23

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Energy absorber
  • Finite element
  • Machine learning
  • Sensitivity analysis
  • Surrogate model
  • SVM
  • Thin-walled tube

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