Thickness Measurement of a Curved Multilayered Polymer System by Using an Ultrasonic Pulse-Echo Method

Maev, Roman Gr.; Shao, H.; Maeva, E.

doi:10.1016/s1044-5803(98)00030-8

Cited by 13 publications

(5 citation statements)

References 5 publications

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“…Because the ultrasonic has advantages such as good directivity, strong penetrating power, high energy, and it can not be limited by checked material, so the ultrasonic detection method has become the focus in the coating thickness nondestructive testing.But due to the coating thickness is too thin, generally in micron, and it lead to the ratio between the ultrasonic wave and thickness is small, the coating ultrasonic of different interface echo affects each other, and make it more difficult to process the signal, therefore the applied base material thickness method is no longer applicable, it must design special ultrasonic testing system and information processing system.The ultrasonic thickness of coating methods mainly include pulse echo method [1], lamb wave method [2] and surface wave method [3], and so on. Pulse echo method is affected by the material surface roughness and the acoustic attenuation, and when the medium thickness is smaller than the double wavelength, the echo of medium surface will be mixed up together, so it is not easy to distinguish.…”

Section: The Thickness Of the Coatingmentioning

confidence: 99%

Nondestructive Testing Methods of Coating Performance

Zhang

2014

AMM

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Section: The Thickness Of the Coatingmentioning

confidence: 99%

Nondestructive Testing Methods of Coating Performance

Zhang

2014

AMM

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“…In addition to detecting structural defects, the ultrasonic non-destructive testing methods also can be used to measure the thickness [ 5 , 6 , 7 ] or elasticity [ 8 ] of the material. Furthermore, ultrasonic flaw detection technique has been applied to food [ 9 ] and medical fields [ 10 , 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Optimal Transducer Placement for Deep Learning-Based Non-Destructive Evaluation

Kim

Han

2023

Sensors

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In this study, the Convolution Neural Network (CNN) algorithm is applied for non-destructive evaluation of aluminum panels. A method of classifying the locations of defects is proposed by exciting an aluminum panel to generate ultrasonic Lamb waves, measuring data with a sensor array, and then deep learning the characteristics of 2D imaged, reflected waves from defects. For the purpose of a better performance, the optimal excitation location and sensor locations are investigated. To ensure the robustness of the training model and extract the feature effectively, experimental data are collected by slightly changing the excitation frequency and shifting the location of the defect. The high classification accuracy for each defect location can be achieved. It is found that the proposed algorithm is also successfully applied even when a bar is attached to the panel.

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“…Of the many available NDT techniques, ultrasonic measurements are the most widely accepted method for evaluating adhesive joints. [18][19][20] In De Sousa et al 20 study, a multilayered system was investigated to extract the layer thickness between acoustically dissimilar materials, and the authors found a typical error of less than 7% for their isotropic materials. In the present study, acoustically similar materials are utilized, a fiber-filled thermoset bonded with an unfilled thermoset, causing difficulty in signal interpretation.…”

Section: Introductionmentioning

confidence: 99%

“…Extensive studies have been conducted on various nondestructive testing (NDT) techniques to evaluate and analyze the adhesive bond quality. Of the many available NDT techniques, ultrasonic measurements are the most widely accepted method for evaluating adhesive joints 18–20 . In De Sousa et al 20 study, a multilayered system was investigated to extract the layer thickness between acoustically dissimilar materials, and the authors found a typical error of less than 7% for their isotropic materials.…”

Section: Introductionmentioning

confidence: 99%

Automated bondline thickness quantification in adhesively joined composites and metals using ultrasound

Pulipati,

Kokkada Ravindranath,

Jack

2023

Polymer Composites

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The use of adhesively bonded joints between composite panels, metal panels, or between composites and metals are used in the aerospace and automotive industries to avoid stress concentrations due to fasteners while reducing weight. Quantifying the bond thickness allows for confidence that loading is being properly transferred. This study presents a methodology to measure bondline thickness using the data obtained from an immersion quality ultrasound scan using an out‐of‐tank, field‐portable, utlrasonic testing (UT) scanner. Eighteen bonded coupons were fabricated using a paste‐type adhesive filling a range of gaps between substrates of 0.254 and 1.27 mm. Three adhesively bonded coupon types were investigated: carbon fiber laminates, fiber glass laminates, and aluminum plates. A semiautomated algorithm is presented to quantify bondline thickness from the ultrasound data for each coupon type, generating a spatially varying 3D point cloud of adhesive thickness. Bondline measurements obtained using ultrasound are then validated over two orthogonal planes against measurements taken from x‐ray computed tomography reconstructions. Results indicate a typical accuracy of 0.081 mm for bondline thickness quantification for all samples studied, with the results from the inspection of the composite coupons yielding an error of 0.078 mm, whereas the accuracy when scanning the aluminum coupons yielded an error of 0.086 mm.Highlights Field‐portable, inspection station is presented for full waveform ultrasonics. Mathematical procedure to characterize adhesive bondline thickness. Results are presented for a variety of material systems and adhesive thicknesses. Results are validated against x‐ray computed tomography with a typical accuracy of 0.081 mm.

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Thickness Measurement of a Curved Multilayered Polymer System by Using an Ultrasonic Pulse-Echo Method

Cited by 13 publications

References 5 publications

Nondestructive Testing Methods of Coating Performance

Nondestructive Testing Methods of Coating Performance

Optimal Transducer Placement for Deep Learning-Based Non-Destructive Evaluation

Automated bondline thickness quantification in adhesively joined composites and metals using ultrasound

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