Adhesive Single-Lap Joints Evaluation Using Ultrasonic Guided Waves

Santos, Mário; Santos, Jaime

doi:10.20944/preprints202305.0557.v1

2023

DOI: 10.20944/preprints202305.0557.v1

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Adhesive Single-Lap Joints Evaluation Using Ultrasonic Guided Waves

Mário Santos¹,

Jaime Santos²

Abstract: This work deals with the evaluation of adhesive single-lap joints using ultrasonic guided waves. An adhesive bonded lap joint can be modelled as a three-layer system made up of two adherents and an adhesive. The propagation of guided waves in the joint is developed from matrix formulations. The behavior of the different guided wave modes that propagate in the bonded region is characterized and it is found that their relative amplitudes can be estimated based on the properties of the incoming wave that propagat… Show more

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Cited by 4 publications

References 64 publications

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Convolutional Neural Network for Interface Defect Detection in Adhesively Bonded Dissimilar Structures

Smagulova,

Samaitis,

Jasiuniene

2024

Applied Sciences

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This study presents an ultrasonic non-destructive method with convolutional neural networks (CNN) used for the detection of interface defects in adhesively bonded dissimilar structures. Adhesive bonding, as the weakest part of such structures, is prone to defects, making their detection challenging due to various factors, including surface curvature, which causes amplitude variations. Conventional non-destructive methods and processing algorithms may be insufficient to enhance detectability, as some influential factors cannot be fully eliminated. Even after aligning signals reflected from the sample surface and interface, in some cases, due to non-parallel interfaces, persistent amplitude variations remain, significantly affecting defect detectability. To address this problem, a proposed method that integrates ultrasonic NDT and CNN, and which is able to recognize complex patterns and non-linear relationships, is developed in this work. Traditional ultrasonic pulse-echo testing was performed on adhesive structures to collect experimental data and generate C-scan images, covering the time gate from the first interface reflection to the time point where the reflections were attenuated. Two classes of datasets, representing defective and defect-free areas, were fed into the neural network. One subset of the dataset was used for model training, while another subset was used for model validation. Additionally, data collected from a different sample during an independent experiment were used to evaluate the generalization and performance of the neural network. The results demonstrated that the integration of a CNN enabled high prediction accuracy and automation of the analysis process, enhancing efficiency and reliability in detecting interface defects.

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Convolutional Neural Network for Interface Defect Detection in Adhesively Bonded Dissimilar Structures

Smagulova,

Samaitis,

Jasiuniene

2024

Applied Sciences

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Evaluation of Adhesive Joints Using Ultrasonic Rayleigh Waves

Kowalczyk,

Ulbrich

2024

Materials

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Adhesive joints are non-separable connections that are used in numerous ways in vehicle construction, particularly in buses. The widespread use of adhesive joints makes it necessary to assess their quality, especially under production conditions. The main goal of this study was to develop a mathematical model to estimate the width of the adhesive path in a plywood-adhesive-closed-profile joint based on selected parameters of the ultrasonic surface wave. A digital ultrasonic flaw detector and Rayleigh wave probes were applied. The test involved evaluating different widths of hybrid adhesive and two-component epoxy adhesive. The tests were conducted on a steel profile from a bus construction. The attenuation of the ultrasonic waves on the steel profile (0.026 db/mm) and the adhesive (0.264 dB/mm) was determined. A one-size-fits-all model for estimating adhesive path width for specific conditions is proposed.

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Ultrasonic Features for Evaluation of Adhesive Joints: A Comparative Study of Interface Defects

Smagulova,

Yilmaz,

Jasiuniene

2023

Sensors

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Ultrasonic non-destructive evaluation in pulse-echo mode is used for the inspection of single-lap aluminum adhesive joints, which contain interface defects in bonding area. The aim of the research is to increase the probability of defect detection in addition to ensuring that the defect sizes are accurately estimated. To achieve this, this study explores additional ultrasonic features (not only amplitude) that could provide more accurate information about the quality of the structure and the presence of interface defects. In this work, two types of interface defects, namely inclusions and delaminations, were studied based on the extracted ultrasonic features in order to evaluate the expected feasibility of defect detection and the evaluation of its performance. In addition, an analysis of multiple interface reflections, which have been proved to improve detection in our previous works, was applied along with the extraction of various ultrasonic features, since it can increase the probability of defect detection. The ultrasonic features with the best performance for each defect type were identified and a comparative analysis was carried out, showing that it is more challenging to size inclusion-type defects compared to delaminations. The best performance is observed for the features such as peak-to-peak amplitude, ratio coefficients, absolute energy, absolute time of flight, mean value of the amplitude, standard deviation value, and variation coefficient for both types of defects. The maximum relative error of the defect size compared to the real one for these features is 16.9% for inclusions and 3.6% for delaminations, with minimum errors of 11.4% and 2.2%, respectively. In addition, it was determined that analysis of the data from repetitive reflections from the sample interface, namely, the aluminum-adhesive second and third reflections, that these contribute to an increase in the probability of defect detection.

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Adhesive Single-Lap Joints Evaluation Using Ultrasonic Guided Waves

Cited by 4 publications

References 64 publications

Convolutional Neural Network for Interface Defect Detection in Adhesively Bonded Dissimilar Structures

Convolutional Neural Network for Interface Defect Detection in Adhesively Bonded Dissimilar Structures

Evaluation of Adhesive Joints Using Ultrasonic Rayleigh Waves

Ultrasonic Features for Evaluation of Adhesive Joints: A Comparative Study of Interface Defects

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