Deep neural networks for damage localization in composite materials with ultrasonic guided waves
department or school: Applied Science, Faculty of
department or school: Engineering, School of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Yun, Hongguang
issued: 2024-05-30T16:31:44Z
issued: 2024
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: Composite materials are widely used in aerospace, wind energy, and shipbuilding, offering significant benefits such as superior strength-to-weight ratios. However, they are prone to damage from impacts, necessitating advanced methods for damage localization to ensure safety and reduce maintenance costs. Deep learning-based ultrasonic guided waves (UGW) methods are increasingly recognized for their effectiveness in locating damage in composites. This study aims to develop deep neural networks (DNN) models for damage localization, focusing on two main challenges: limited dataset sizes and the integration of physical knowledge. Three specific application scenarios were identified: independent and identically distributed ($i.i.d.$) damage localization, domain generalized damage localization, and out-of-distribution (OOD) damage localization. Firstly, to achieve accurate damage localization, incorporating transducer coordinates with UGW signals is essential. This study developed a multi-dimensional data fusion DNN model that integrates a convolutional neural network-based encoder for high-dimensional wave signals and a novel random Fourier projection head for transducer coordinates. The proposed method outperformed existing DNN-based methods, achieving an average absolute distance error of approximately 2 millimeters in damage localization on plate-like composite structures. Secondly, distribution discrepancies often occur between training and testing data, leading to reduced model performance. This study proposed a domain generalization method to mitigate this issue in a multi-class classification task. A feature distance maximization mechanism was introduced to improve model’s resistance to such distribution discrepancies. The proposed method outperformed leading DNN-based models, achieving an average accuracy of around 90%, compared to less than 85% for other methods. Thirdly, this study proposes a physics-informed neural network model to address the challenging OOD d…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/88389
note: Deep neural networks for damagelocalization in composite materialswith ultrasonic guided wavesbyHongguang YunB.Sc., Xi’an Jiaotong University, 2016M.Sc., Xi’an Jiaotong University, 2019A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFDOCTOR OF PHILOSOPHYinThe College of Graduate Studies(Electrical Engineering)THE UNIVERSITY OF BRITISH COLUMBIA(Okanagan)May 2024© Hongguang Yun, 2024The following individuals certify that they have read, and recommendto the College of Graduate Studies for acceptance, a thesis/dissertation en-titled:Deep neural networks for damage localization in compositematerials with ultrasonic guided wavessubmitted by Hongguang Yun in partial fulfillment of the requirementsof the degree of Doctor of PhilosophyDr. Zheng Liu, School of EngineeringSupervisorDr. Abbas Milani, School of EngineeringSupervisory Committee MemberDr. Shashank Pant, National Research Council CanadaSupervisory Committee MemberDr. Hadi Mohammadi, School of EngineeringUniversity ExaminerDr. Yuu Ono, Carleton UniversityExternal ExamineriiAbstractComposite materials are widely used in aerospace, wind energy, andshipbuilding, offering significant benefits such as superior strength-to-weightratios. However, they are prone to damage from impacts, necessitating ad-vanced methods for damage localization to ensure safety and reduce mainte-nance costs. Deep learning-based ultrasonic guided waves (UGW) methodsare increasingly recognized for their effectiveness in locating damage in com-posites. This study aims to develop deep neural networks (DNN) modelsfor damage localization, focusing on two main challenges: limited datasetsizes and the integration of physical knowledge. Three specific applicationscenarios were identified: independent and identically distributed (xIxIsI)damage localization, domain generalized damage localization, and out-of-distribution (OOD) damage localization.Firstly, to achieve accurate damage localization, incorporating trans-ducer coord…
has type: Thesis/Dissertation
date issued: 2024-09
is shown at: 10.14288/1.0443818
language: eng
degree discipline: Electrical Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Liu, Zheng (Professor of engineering)
type: Text
identifier URI: http://hdl.handle.net/2429/88389