Delamination localization in the composite thin plates using ensemble learning: Bagging and boosting techniques

Document Type : Article

Authors

1 Department of Aeronautics Sciences, Air NCO Higher Vocational School, National Defence University, Gaziemir, Izmir, Turkiye

2 Department of Computer Programming, Ege University, Bornova, Izmir, Turkiye

Abstract

Localization of the delamination is an essential task that is conducted by various destructive and non-destructive approaches, which may require time, experts, and cost. Various intelligent non-destructive techniques are utilized to reduce time consumption, the need for expertise, and expenditures for localizing compositing delaminations. Yet, developing an accurate, robust, and low-cost intelligent delamination identification technique becomes a challenging task due to the anisotropy and the variation in the fiber orientation of the composites. Based on those issues, it is aimed to develop an effective intelligent model to localize delaminations in composite plates. This study measures the performance of the Bagging and Boosting techniques on delamination localization in thin composite plates. To validate the effectiveness of the proposed approaches; cross-ply, angle-ply, and quasi-isotropic composite plates having 2400 different delamination cases are considered. The bagging and boosting models are trained with the vibrational characteristics of the healthy and delaminated composite structures. The free vibration analysis is conducted for those structures to obtain the first five natural frequencies and the corresponding mode shapes. For this purpose, Classical Plate Theory is employed by using Finite Element Analysis. It is concluded that both bagging and boosting techniques are robust, precise, and accurate in localizing delamination.

Keywords

Main Subjects

References

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Scientia Iranica
Volume 31, Issue 4 - Serial Number 4
Transactions on Mechanical Engineering (B)
March and April 2024
Pages 310-329

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History

  • Receive Date: 24 September 2021
  • Revise Date: 06 December 2022
  • Accept Date: 05 April 2023

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