Laser ultrasonics and machine learning for automatic defect detection in metallic components
This paper develops an automatic and reliable nondestructive evaluation (NDE) technique that enables quantification of the width and depth of subsurface defects of metallic components simultaneously by using non-contact laser ultrasonic technique and identified machine learning (ML) algorithm. Twenty-two specimens with various subsurface defect dimensions are designed and fabricated for laser ultrasonic experiments, and a total of 220 labeled laser ultrasonic signals are obtained for training and verifying ML models. Twelve features, including four time-domain features (maximum, minimum, peak-to-peak, and |Neg|/Pos value of the laser generated Rayleigh ultrasonic waves) and eight wavelet energy features, are identified and extracted as sensitive feature vectors for establishing the dataset. The principal component analysis (PCA) is implemented as dimensionality reduction method of feature vectors to optimize the recognition algorithm and improve the detection accuracy. Three widely used ML models in NDE, adaptive boosting (Adaboost), extreme gradient boosting (XGBboost), and support vector machine (SVM), combined with the PCA are proposed and compared for detecting both the width and depth of subsurface defects. The PCA-XGBoost achieves the highest recognition rate of 98.48%, and is therefore identified as the most effective approach for analyzing laser-ultrasonic signals. Unlike published reports, the proposed model is trained and evaluated with experimental data covered various classification labels, which is more adaptive and reliable in practical application than the models established using simulated data or limited experimental data. In other applications, as long as sufficient laser ultrasonic data with regards to various defect properties (dimensions, orientations, locations, shapes, etc.) can be acquired, the developed approach can realize accurate detection of corresponding defects.
Guangdong Science and Technology Department[2019QN01H430];Guangdong Science and Technology Department[2019TQ05Z654];Reuter Foundation[2020A1515110218];National Natural Science Foundation of China;Shenzhen Institutes of Advanced Technology Innovation Program for Excellent Young Researchers[E1G048];Shenzhen Graduate School, Peking University[JCYJ 20180507182239617];Shenzhen Graduate School, Peking University[JCYJ20210324101200002];National Natural Science Foundation of China[U1813222];National Natural Science Foundation of China[U20A20283];Shenzhen Graduate School, Peking University[ZDSYS20190902093209795];
|WOS Research Area|
Materials Science, Characterization & Testing
|WOS Accession No|
|ESI Research Field|
Cited Times [WOS]:10
|Document Type||Journal Article|
|Department||Department of Electrical and Electronic Engineering|
1.Shenzhen Key Laboratory of Smart Sensing and Intelligent Systems,Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen,518055,China
2.Guangdong Provincial Key Lab of Robotics and Intelligent System,Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen,518055,China
3.University of Chinese Academy of Sciences,Beijing,100049,China
4.College of Mechanical and Electronic Engineering,Shandong Agricultural University,Tai'an,271018,China
5.Department of Electrical and Electronic Engineering,Southern University of Science and Technology,Shenzhen,518055,China
Lv，Gaolong,Guo，Shifeng,Chen，Dan,et al. Laser ultrasonics and machine learning for automatic defect detection in metallic components[J]. NDT & E INTERNATIONAL,2023,133.
Lv，Gaolong.,Guo，Shifeng.,Chen，Dan.,Feng，Haowen.,Zhang，Kaixing.,...&Feng，Wei.(2023).Laser ultrasonics and machine learning for automatic defect detection in metallic components.NDT & E INTERNATIONAL,133.
Lv，Gaolong,et al."Laser ultrasonics and machine learning for automatic defect detection in metallic components".NDT & E INTERNATIONAL 133(2023).
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