Intelligent Evaluation of Crack detection with Laser Ultrasonic technique

Li, Kesi; Sui, Hao; Dong, Xun; Guo, Liheng; Gao, Hong-Li

doi:10.1088/1755-1315/514/2/022014

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…PCA is known for its low noise sensitivity and high efficiency in smaller dimensions. Additionally, it is often applied to reduce the feature vector of laser ultrasonic signals [27]. In this study, since the proposed data frame contains 1000 features, applying PCA to reduce the feature dimensionality can lead to enhanced accuracy.…”

Section: Principal Component Analysismentioning

confidence: 99%

ML-Enabled Piezoelectric-Driven Internal Defect Assessment in Metal Structures

Adeleye,

Seyedi,

Ferdowsi

et al. 2023

Machines

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With the growth of 3D printing in the production space, it is inevitable that quality assurance will be needed to keep final products within the constraints of requirements. Also, the variety of materials that can be used with 3D printing has increased over the years. Testing also must consider the process of manufacturing. This paper focuses its efforts on the finished product and not the process of manufacturing. Ultrasonic testing is a type of nondestructive testing. The experiments performed in this study aim to explore the usefulness of ultrasonic testing in materials that are 3D printed. The two materials used in this study are steel alloy metals and aluminum blocks of the same dimensions—120 mm × 40 mm × 15 mm. These materials represent common choices in additive manufacturing processes. The chosen alloys, such as Aluminum (6063T6) and grade-304 stainless steel, possess distinct properties crucial for validating the proposed testing method. Metal 3D-printed materials play a pivotal role in diverse industries, since ensuring their structural integrity is imperative for reliability and safety. Testing is crucial to identify and mitigate defects that could compromise the functionality and longevity of the final products, especially in applications with demanding performance requirements. An ultrasonic transducer is used to scan for subsurface defects within the samples and an oscilloscope is used to analyze the signals. Furthermore, several Machine Learning (ML) techniques are used to estimate the severity of the defects. The application of Machine Learning methods in the manufacturing industry has proven advantageous in terms of detecting defects due to its practicality and wide application. Due to their distinct benefits in processing image information, convolutional neural networks (CNNs) are the preferred method when working with picture data. In order to perform binary and multi-class classification, support vector machines that employ the alternative kernel function are a viable option for processing sensor signals and picture data. The study reveals that ultrasonic tests are viable for metallic materials. The primary objective of this work is to evaluate and validate the application of ultrasonic testing for the inspection of 3D-printed steel alloy metals and aluminum blocks. The novelty lies in the integration of Machine Learning techniques to estimate defect severity, offering a comprehensive and non-invasive approach to quality assessment in 3D-printed materials. The proposed method can successfully detect the presence of internal defects in objects, as well as estimate the location and severity of the defects.

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Section: Principal Component Analysismentioning

confidence: 99%

ML-Enabled Piezoelectric-Driven Internal Defect Assessment in Metal Structures

Adeleye,

Seyedi,

Ferdowsi

et al. 2023

Machines

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show abstract

“…Machine learning, including support vector machines (SVMs) and neural networks, has been a useful tool for fast, intelligent evaluation. Li [11,12] used a particle swarm optimization neural network algorithm and a quantized particle swarm optimized support vec-tor regression algorithm to intelligently detect and quantify the depth of surface-breaking cracks. Shukla [13] introduced an optimized physics-informed neural network trained to solve the problem of identifying a surface breaking crack in a metal plate.…”

Section: Introductionmentioning

confidence: 99%

Depth and Angle Evaluation of Oblique Surface Cracks Using a Support Vector Machine Based on Seven Parameters

Liu

Deng

et al. 2022

Applied Sciences

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To intelligently and quickly verify the depths and angles of oblique surface cracks using the laser ultrasound technique, a crack geometry information evaluation method using a support vector machine (SVM) is proposed. The relationships between several parameters—which are based on transmitted waves and reflected waves containing crack geometry information in the time domain, frequency domain and time–frequency domain, and crack size—are presented in detail. To decide which feature parameters are involved in the SVM model, two selection standards are proposed according to the correlations between feature parameters and crack size and between the feature parameters themselves. Finally, a seven-feature-parameter SVM model which has an excellent ability to classify oblique crack depths and angles is built. The results of simulations using the finite element method (FEM) and experimental data show that seven parameters based on the SVM model are available for the oblique surface crack depth and angle evaluation and can be used to nondestructively detect natural cracks.

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Intelligent Evaluation of Crack detection with Laser Ultrasonic technique

Cited by 2 publications

References 16 publications

ML-Enabled Piezoelectric-Driven Internal Defect Assessment in Metal Structures

ML-Enabled Piezoelectric-Driven Internal Defect Assessment in Metal Structures

Depth and Angle Evaluation of Oblique Surface Cracks Using a Support Vector Machine Based on Seven Parameters

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