Computer Science ›› 2023, Vol. 50 ›› Issue (11A): 230200150-6.doi: 10.11896/jsjkx.230200150

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Study on Ultrasonic Phased Array Defect Detection Based on Machine Vision

ZOU Chenwei, YAO Rao   

  1. College of Aviation and Transportation,Shanghai University of Engineering Science,Shanghai 201620,China
  • Published:2023-11-09
  • About author:ZOU Chenwei,born in 1996,postgra-duate.His main research interests include Image processing and non-destructive testing.
    YAO Rao,born in 1976,Ph.D.Her main research interests include automated intelligent bionic non-destructive testing and 3D visualization health monitoring.
  • Supported by:
    National Natural Science Foundation of China(62171271).

Abstract: Ultrasonic phased array inspection is a commonly used non-destructive testing (NDT) technique for workpiece defect detection and evaluation.In order to realize the modern industrial big data and automation,to solve the problem of missing information and scattering noise of ultrasonic phased array images generated during defect detection,and to realize the accurate recognition of various types of defects,a defect recognition method based on machine vision is proposed,which extracts the image features as the experimental data of BP neural network for particle swarm optimization after image denoising process using improved PM differential equations.The experimental results show that the proposed method has an accuracy rate of 99.43% for the trai-ning set,which is 1.833% higher than the traditional BP network model,and can accurately achieve defect recognition while maintaining good model performance.

Key words: Ultrasonic phased array, Defect detection, BP neural network, PM differential equation, Particle swarm optimization

CLC Number: 

  • TP391
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