Y
Ye Lu
Researcher at Monash University, Clayton campus
Publications - 93
Citations - 3758
Ye Lu is an academic researcher from Monash University, Clayton campus. The author has contributed to research in topics: Lamb waves & Finite element method. The author has an hindex of 25, co-authored 83 publications receiving 2928 citations. Previous affiliations of Ye Lu include Tongji University & Shanghai Jiao Tong University.
Papers
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Guided Lamb waves for identification of damage in composite structures: A review
Zhongqing Su,Lin Ye,Ye Lu +2 more
TL;DR: A comprehensive review on the state of the art of Lamb wave-based damage identification approaches for composite structures, addressing the advances and achievements in these techniques in the past decades, is provided in this paper.
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Quantitative assessment of through-thickness crack size based on Lamb wave scattering in aluminium plates
TL;DR: In this paper, the interaction of Lamb wave modes at varying frequencies with a through-thickness crack of different lengths in aluminium plates was analyzed in terms of finite element method and experimental study.
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Functionalized composite structures for new generation airframes: a review
TL;DR: An AI technique-based composite structure with the capability of structural health monitoring was developed and results indicate excellent performance of AI techniques in functionalized composite structures.
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Crack identification in aluminium plates using Lamb wave signals of a PZT sensor network
Ye Lu,Lin Ye,Zhongqing Su +2 more
TL;DR: In this article, a Lamb wave-based crack identification technique for aluminium plates was developed with an integrated active piezoelectric sensor network, and a correlation function was further established, which helped identify the crack position based on a triangulation approach with the aid of a nonlinear least-squares optimization algorithm.
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Concrete crack detection with handwriting script interferences using faster region‐based convolutional neural network
TL;DR: It is demonstrated that faster R‐CNN can automatically locate crack from raw images, even with the presence of handwriting scripts, as well as compared with You Only Look Once v2 detection technique.