Neural Network-Based Characterization of Flaws Tested by Ultrasoni

Journal of South China University of Technology (Natural Science Edition) ›› 2005, Vol. 33 ›› Issue (4): 5-9.

Previous Articles Next Articles

Neural Network-Based Characterization of Flaws Tested by Ultrasoni

Luo Xiong-biao Chen Tie-qun

College of Mechanical Engineering,South China Univ.of Tech.,Guangzhou 510640,Guangdong,China

Received:2004-01-09 Online:2005-04-25 Published:2005-04-25
Contact: Luo Xiong—biao(born in 1979)，male，graduate student，mainly researches on the inspection of flaws tested by ultrasonic an d signal processing． E-mail:lxbxl_0207@ sohu.com
About author:Luo Xiong—biao(born in 1979)，male，graduate student，mainly researches on the inspection of flaws tested by ultrasonic an d signal processing．
Supported by:
Suppofled by the Project of Science and Technology of Guangdong Province(2004A1 1 303001)

Abstract

Abstract:

This paper proposes a method for flaw characterization on the basis of neural networks.In this me-thod ,a selection of the shape parameters defining the pulse-echo envelope reflected from a flaw is carried out by Fischer linear discriminant analysis.The selected parameters are then used as the inputs of neural networks to train the propo sed intelligent flaw characterization system. Moreover,probabilistic neural networks and back pmpagation neural networks are respe ctively adopted to determine the sizes and numbers of flaws.Experimental results for 1 35 systematic weld flaws(crack,slag and porosity)indicate that the proposed method is effective in the flaw characterization with great classification rate.

Key words: ultrasonic testing, flaw characterization, nondestructive evaluation, neural network

Luo Xiong-biao Chen Tie-qun. Neural Network-Based Characterization of Flaws Tested by Ultrasoni[J]. Journal of South China University of Technology (Natural Science Edition), 2005, 33(4): 5-9.

[1]	FANG Gang, YUAN Longhua, WANG Xiaoming, et al. Soft-Sensor Modeling Method Based on Ensemble Kalman Filter-Elman Neural Network [J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(8): 126-136.
[2]	MA Xiaoliang, AN Lingling, DENG Congjian, et al. Translation Optimization Technology of Automatic Speech Recognition Based on Industry-Specific Vocabulary [J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(8): 118-125.
[3]	ZHU Zhengyu, LUO Chao, HE Qianhua, et al. Multi-View Lip Motion and Voice Consistency Judgment Based on Lip Reconstruction and Three-Dimensional Coupled CNN [J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(5): 70-77.
[4]	YE Feng, CHEN Biao, LAI Yizong. Contrastive Knowledge Distillation Method Based on Feature Space Embedding [J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(5): 13-23.
[5]	LIU Yijun, CAO Yu, YE Wujian, et al. Design and Implementation of Hardware Structure for Online Learning of Spiking Neural Networks Based on FPGA Parallel Acceleration [J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(5): 104-113.
[6]	LENG Sheng, FU Youwei, MA Wantai, et al. Study on the Morphology Control Technology of Spray Forming Ingot Billets Based on GA-BP Neural Network [J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(2): 27-34.
[7]	LIU Zeqing, YANG Xufeng, ZHANG Yi. Estimation of P-S-N Curve of Metal Materials Based on Bayesian Neural Network [J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(11): 82-92.
[8]	XU Lunhui, YU Jiaxin, PEI Mingyang, et al. Repositioning Strategy for Ride-Hailing Vehicles Based on Geometric Road Network Structure and Reinforcement Learning [J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(10): 99-109.
[9]	LAN Fengchong, ZHANG Yue, CHEN Jiqing, et al. Correlation Analysis and Prediction of Pedestrian Casualty Risk in Car-Pedestrian Collision Accident [J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(5): 1-10.
[10]	YANG Chunling YANG Yajing. A Deep Neural Network Based on Layer-by-Layer Fusion of Multi-Scale Features for No-Reference Image Quality Assessment [J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(4): 81-89,141.
[11]	FU Xinsha, ZENG Yanjie, MA Li, et al. Weather Recognition of Highway Surveillance Scenes Based on Light-Weight Deep Neural Network [J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(3): 1-8.
[12]	QIU Zhibin, LU Zuwen, WANG Haixiang, et al. Recognition of Bird Sounds Related to Power Grid Faults Based on Mel Spectrogram and Convolutional Neural Network [J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(2): 129-136.
[13]	DU Qiliang, XIANG Zhaoyi, TIAN Lianfang, et al. Two-Stream Adaptive Attention Graph Convolutional Networks for Action Recognition [J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(12): 20-29.
[14]	XING Xiaofen, LI Minsheng, XU Xiangmin . Image Sentiment Transformation Based on Adaptive Brightness Adjustment [J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(12): 1-12.
[15]	CAI Xiaodong, ZENG Zhiyang . AFGSRec: A Social Recommendation Model Based on Adaptive Fusion of Global Collaborative Features [J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(12): 71-79.

Neural Network-Based Characterization of Flaws Tested by Ultrasoni

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments