Predicting local material thickness from steady-state ultrasonic wavefield measurements using a convolutional neural network

Eckels, Joshua D.; Jacobson, Erica M.; Cummings, Ian; Fernandez, Isabel F.; Ho, Kelly; Dervilis, Nikolaos; Flynn, Eric B.; Wachtor, Adam J.

doi:10.1016/j.ultras.2021.106661

Cited by 4 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The CNN can be used with acoustic steady-state excitation spatial spectroscopy for identifying the damage location, size, and shape based on predicting the plate thickness at each plate pixel. The results indicate the ability of the proposed CNN to precisely predict the plate thickness at a zone where the dispersion of Lamb waves is complex [48].…”

Section: Introductionmentioning

confidence: 83%

Delamination Depth Detection in Composite Plates Using the Lamb Wave Technique Based on Convolutional Neural Networks

Migot,

Saaudi,

Giurgiutiu

2024

Sensors

View full text Add to dashboard Cite

Delamination represents one of the most significant and dangerous damages in composite plates. Recently, many papers have presented the capability of structural health monitoring (SHM) techniques for the investigation of structural delamination with various shapes and thickness depths. However, few studies have been conducted regarding the utilization of convolutional neural network (CNN) methods for automating the non-destructive testing (NDT) techniques database to identify the delamination size and depth. In this paper, an automated system qualified for distinguishing between pristine and damaged structures and classifying three classes of delamination with various depths is presented. This system includes a proposed CNN model and the Lamb wave technique. In this work, a unidirectional composite plate with three samples of delamination inserted at different depths was prepared for numerical and experimental investigations. In the numerical part, the guided wave propagation and interaction with three samples of delamination were studied to observe how the delamination depth can affect the scattered and trapped waves over the delamination region. This numerical study was validated experimentally using an efficient ultrasonic guided waves technique. This technique involved piezoelectric wafer active sensors (PWASs) and a scanning laser Doppler vibrometer (SLDV). Both numerical and experimental studies demonstrate that the delamination depth has a direct effect on the trapped waves’ energy and distribution. Three different datasets were collected from the numerical and experimental studies, involving the numerical wavefield image dataset, experimental wavefield image dataset, and experimental wavenumber spectrum image dataset. These three datasets were used independently with the proposed CNN model to develop a system that can automatically classify four classes (pristine class and three different delamination classes). The results of all three datasets show the capability of the proposed CNN model for predicting the delamination depth with high accuracy. The proposed CNN model results of the three different datasets were validated using the GoogLeNet CNN. The results of both methods show an excellent agreement. The results proved the capability of the wavefield image and wavenumber spectrum datasets to be used as input data to the CNN for the detection of delamination depth.

show abstract

Section: Introductionmentioning

confidence: 83%

Delamination Depth Detection in Composite Plates Using the Lamb Wave Technique Based on Convolutional Neural Networks

Migot,

Saaudi,

Giurgiutiu

2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Alguri and Harley used a neural network to learn low-dimensional representations of wave propagation from numerical simulations [208]. Eckels et al [267] used a simulation-trained convolutional neural network to process a steady-state wavefield measurement into a thickness map. Melville et al used deep learning (DL) to achieve accurate detection of damage using only partial (0.1%) wavefield measurement [268].…”

Section: • Resonant Frequency Identificationmentioning

confidence: 99%

“…Their study showed that thickness estimation after optimizing wavenumber sensitivity is more appropriate. Imaging damage with gradual thickness change is also available in [42,267].…”

Section: Damage With Smooth Boundarymentioning

confidence: 99%

Guided ultrasonic waves propagation imaging: a review

Chia¹,

Lee²,

Harmin³

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

This article presents a comprehensive review of the laser-based guided ultrasonic waves propagation imaging (G-UPI) system and respective signal/data processing methods related to the nondestructive testing and evaluation of thin-walled structures. The primary goal of this study is to review and recognize various processing methods, explain the working principles of the most influential methods, and highlight outstanding capabilities. In addition, the suitability of the methods for multiple types of damage and defect in various materials and structures are presented. At the same time, success stories of difficult-to-inspect cases are highlighted. Its secondary goal is to compare and discuss the merits and demerits of the laser-scanning part of the system for ultrasound generation and acquisition to provide a guideline for scanning scheme or hardware selection. Finally, the potential challenges and prospects of the G-UPI are discussed. It is expected that this review would serve as an entrance key for newcomers and a reference point for researchers to explore the opportunities for further improvement in the laser ultrasound-based evaluation of critical engineering structures.

show abstract