Evaluation of subsurface defects in metallic structures using laser ultrasonic technique and genetic algorithm-back propagation neural network

Zhang，Kaixing1,6; Lv，Gaolong1,2,3; Guo，Shifeng2,3,4; Chen，Dan2,3; Liu，Yanjun5; Feng，Wei2,3,4

2020-12-01

10.1016/j.ndteint.2020.102339

NDT & E INTERNATIONAL   影响因子和分区

0963-8695

1879-1174

An effective nondestructive evaluation technique that enables the detection and quantification of subsurface defects is highly demanded for assuring safety and reliability of safety-critical structures. In this work, an improved genetic algorithm-back propagation neural network (GA-BPNN) model and non-contact laser ultrasonic technique are combined to quantify the width of subsurface defects. An experimentally validated numerical model that simulates the interaction of laser-generated Rayleigh ultrasonic waves with subsurface defects is firstly established, which is further utilized to generate a large number of labeled laser ultrasonic signals for training the GA-BPNN model. A total number of 189 data are obtained from simulation and experiments, with 173 simulated signals for training the GA-BPNN model and the remaining 13 simulated signals together with 3 experimental signals for verifying the performance of the trained GA-BPNN model. Five features including three time-domain features (maximum, minimum and peak-to-peak value of the Rayleigh ultrasonic waves) and two frequency-domain features (F, BW), which are identified sensitive to the width of subsurface defects by both experiments and simulation, are extracted as inputs to train the machine learning algorithm. The result demonstrates that the GA-BPNN model trained with the combination of time and frequency features has the average error of 2.15%, which is substantially smaller than the errors obtained from the model trained with only time-domain features and frequency-domain features, with the average errors of 4.43% and 21.81%, respectively. This work proves the feasibility and reliability to quantify the width of subsurface defects in metallic structures using laser ultrasonic technique and the improved GA-BPNN algorithm.

Laser ultrasonics Machine learning NDE Neural network Rayleigh ultrasonic wave Subsurface defect

SCI ; EI

英语

其他

National Natural Science Foundation of Guangdong[2016A030313177] ; Guangdong Frontier and Key Technological Innovation[2017B090910013] ; Science and Technology Innovation Commission of Shenzhen[ZDSYS20190902093209795][JCYJ20170818153048647][JCYJ 20180507182239617] ; China Postdoctoral Science Foundation[2020M672891]

Materials Science

Materials Science, Characterization & Testing

WOS:000579406300026

ELSEVIER SCI LTD

20203409064382

Ultrasonic testing ; Ultrasonic waves ; Defects ; Genetic algorithms ; Machine learning ; Nondestructive examination ; Errors ; Time domain analysis ; Frequency domain analysis ; Learning algorithms ; Safety engineering

Artificial Intelligence:723.4 ; Machine Learning:723.4.2 ; Ultrasonic Waves:753.1 ; Ultrasonic Applications:753.3 ; Safety Engineering:914 ; Mathematics:921 ; Mathematical Transformations:921.3 ; Materials Science:951

MATERIALS SCIENCE

2-s2.0-85089417244

Scopus

1.College of Mechanical and Electronic Engineering,Shandong Agricultural University,Tai'an,271018,China
2.Shenzhen Key Laboratory of Smart Sensing and Intelligent Systems,Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen,518055,China
3.Guangdong Provincial Key Lab of Robotics and Intelligent System,Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen,518055,China
4.CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems,Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen,518055,China
5.Department of Electrical and Electronic Engineering,Southern University of Science and Technology,Shenzhen,518055,China
6.Shandong Agricultural Equipment Intelligent Engineering Laboratory,Tai'an,271018,China

Zhang，Kaixing,Lv，Gaolong,Guo，Shifeng,et al. Evaluation of subsurface defects in metallic structures using laser ultrasonic technique and genetic algorithm-back propagation neural network[J]. NDT & E INTERNATIONAL,2020,116.

Zhang，Kaixing,Lv，Gaolong,Guo，Shifeng,Chen，Dan,Liu，Yanjun,&Feng，Wei.(2020).Evaluation of subsurface defects in metallic structures using laser ultrasonic technique and genetic algorithm-back propagation neural network.NDT & E INTERNATIONAL,116.

Zhang，Kaixing,et al."Evaluation of subsurface defects in metallic structures using laser ultrasonic technique and genetic algorithm-back propagation neural network".NDT & E INTERNATIONAL 116(2020).