Damage detection of CFRP laminates by using ultrasonic Lamb wave with a sparse array

Since stringers are often applied in engineering constructions to improve thin-walled structures’ strength, methods for damage detection at the joints between the stringer and the thin-walled structure are necessary. A 2D mathematical model was employed to simulate Lamb wave excitation and sensing via rectangular piezoelectric-wafer active transducers mounted on the surface of an elastic plate with rectangular surface-bonded obstacles (stiffeners) with interface defects. The results of a 2D simulation using the finite element method and the semi-analytical hybrid approach were validated experimentally using laser Doppler vibrometry for fully bonded and semi-debonded rectangular obstacles. A numerical analysis of fundamental Lamb wave scattering via rectangular stiffeners in different bonding states is presented. Two kinds of interfacial defects between the stiffener and the plate are considered: the partial degradation of the adhesive at the interface and an open crack. Damage indices calculated using the data obtained from a sensor are analyzed numerically. The choice of an input impulse function applied at the piezoelectric actuator is discussed from the perspective of the development of guided-wave-based structural health monitoring techniques for damage detection.

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Damage detection in composite structures using Lamb wave analysis and time-frequency approach

10.1117/12.880664 ◽

2011 ◽

Cited By ~ 2

Author(s):

Yingtao Liu ◽

Masoud Yekani Fard ◽

Seung B. Kim ◽

Aditi Chattopadhyay ◽

Derek Doyle

Keyword(s):

Damage Detection ◽

Lamb Wave ◽

Composite Structures ◽

Wave Analysis ◽

Time Frequency

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A Damage Detection Method Using Neural Network Optimized by Multiple Particle Collision Algorithm

Journal of Sensors ◽

10.1155/2021/9998187 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Sergio V. Farias ◽

Osamu Saotome ◽

Haroldo F. Campos Velho ◽

Elcio H. Shiguemori

Keyword(s):

Neural Network ◽

Damage Detection ◽

Lamb Wave ◽

Learning Strategy ◽

Damage Identification ◽

Particle Collision ◽

Learning Approaches ◽

Propagation Methods ◽

Artificial Neural Network Ann ◽

Multiple Particle

A critical task of structural health monitoring is damage detection and localization. Lamb wave propagation methods have been successfully applied for damage identification in plate-like structures. However, Lamb wave processing is still a challenging task due to its multimodal and dispersive characteristics. To address this issue, data-driven machine learning approaches as artificial neural network (ANN) have been proposed. However, the effectiveness of ANN can be improved based on its architecture and the learning strategy employed to train it. The present paper proposes a Multiple Particle Collision Algorithm (MPCA) to design an optimum ANN architecture to detect and locate damages in plate-like structures. For the first time in the literature, the MPCA is applied to find damages in plate-like structures. The present work uses one piezoelectric transducer to generate Lamb wave signals on an aluminum plate structure and a linear array of four transducers to capture the scattered signals. The continuous wavelet transform (CWT) processes the captured signals to estimate the time-of-flight (ToF) that is the ANN inputs. The ANN output is the damage spatial coordinates. In addition to MPCA optimization, this paper uses a quantitative entropy-based criterion to find the best mother wavelet and the scale values. The presented experimental results show that MPCA is capable of finding a simple ANN architecture with good generalization performance in the proposed damage localization application. The proposed method is compared with the 1-dimensional convolutional neural network (1D-CNN). A discussion about the advantages and limitations of the proposed method is presented.

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Development of Laboratory-scale Lamb Wave-based Health Monitoring System for Laminated Composites

Journal of Engineering and Technological Sciences ◽

10.5614/j.eng.technol.sci.2021.53.4.7 ◽

2021 ◽

Vol 53 (4) ◽

pp. 210407

Author(s):

Leonardo Gunawan ◽

Muhammad Hamzah Farrasamulya ◽

Andi Kuswoyo ◽

Tatacipta Dirgantara

Keyword(s):

Damage Detection ◽

Health Monitoring ◽

Lamb Wave ◽

Laminated Composite ◽

Composite Plates ◽

Laboratory Scale ◽

Optimum Number ◽

Detection Scheme ◽

Damage Location ◽

Location Detection

This paper presents the development process of a laboratory-scale Lamb wave-based structural health monitoring (SHM) system for laminated composite plates. Piezoelectric patches are used in pairs as actuator/sensor to evaluate the time of flight (TOF), i.e. the time difference between the transmitted/received signals of a damaged plate and those of a healthy plate. The damage detection scheme is enabled by means of evaluating the TOF from at least three actuator/receiver pairs. In this work, experiments were performed on two GFRP plates, one healthy and the other one with artificial delamination. Nine piezoelectric transducers were mounted on each plate and the detection of the delamination location was demonstrated, using 4 pairs and 20 pairs of actuators/sensors. The combinations of fewer and more actuators/sensor pairs both provided a damage location that was in good agreement with the artificial damage location. The developed SHM system using simple and affordable equipment is suitable for supporting fundamental studies on damage detection, such as the development of an algorithm for location detection using the optimum number of actuator/sensor pairs.

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