Damage Detection in Lightweight Structures Using Artificial Intelligence Techniques

Tavares, A. C.; Lorenzo, Emilio Di; Peeters, Bart; Coppotelli, Giuliano; Silvestre, Nuno

doi:10.1007/s40799-020-00421-5

Cited by 8 publications

(2 citation statements)

References 29 publications

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“…Tavares et al (11) propose a novel integration of SLDV measurements and Artificial Intelligence for Non-Destructive Evaluation (NDE). The paper proposes an algorithm for identifying the defects based on the Local Defect Resonance (LDR) concept, which looks to the high frequency vibrations to get a localized resonant activation of the defect.…”

Section: Scanning Laser Vibrometrymentioning

confidence: 99%

Special Issue on Computer Vision and Scanning Laser Vibrometry Methods

Maio

Harvey

2021

Exp Tech

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Section: Scanning Laser Vibrometrymentioning

confidence: 99%

Special Issue on Computer Vision and Scanning Laser Vibrometry Methods

Maio

Harvey

2021

Exp Tech

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“…This research used simulated frequency shift data on composite materials generated by Finite Element Analysis (FEA) to feed the proposed ANNs and achieved a prediction accuracy of up to 95% [21]. Another study from Tavares et al [22] employed both simulation and experimental data to perform damage detection on CFRP structures. The obtained Frequency Response Functions (FRFs) and time signals from inspections were processed with the K-means Clustering and Multivariate Anomaly Detection and thus created a defects detection model.…”

Section: Introductionmentioning

confidence: 99%

Classification of barely visible impact damage in composite laminates using deep learning and pulsed thermographic inspection

Deng

Liu

Yang

et al. 2023

Neural Comput & Applic

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With the increasingly comprehensive utilisation of Carbon Fibre-Reinforced Polymers (CFRP) in modern industry, defects detection and characterisation of these materials have become very important and draw significant research attention. During the past 10 years, Artificial Intelligence (AI) technologies have been attractive in this area due to their outstanding ability in complex data analysis tasks. Most current AI-based studies on damage characterisation in this field focus on damage segmentation and depth measurement, which also faces the bottleneck of lacking adequate experimental data for model training. This paper proposes a new framework to understand the relationship between Barely Visible Impact Damage features occurring in typical CFRP laminates to their corresponding controlled drop-test impact energy using a Deep Learning approach. A parametric study consisting of one hundred CFRP laminates with known material specification and identical geometric dimensions were subjected to drop-impact tests using five different impact energy levels. Then Pulsed Thermography was adopted to reveal the subsurface impact damage in these specimens and recorded damage patterns in temporal sequences of thermal images. A convolutional neural network was then employed to train models that aim to classify captured thermal photos into different groups according to their corresponding impact energy levels. Testing results of models trained from different time windows and lengths were evaluated, and the best classification accuracy of 99.75% was achieved. Finally, to increase the transparency of the proposed solution, a salience map is introduced to understand the learning source of the produced models.

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