首页|A Novel On-Site-Real-Time Method for Identifying Characteristic Parameters Using Ultrasonic Echo Groups and Neural Network

A Novel On-Site-Real-Time Method for Identifying Characteristic Parameters Using Ultrasonic Echo Groups and Neural Network

扫码查看
原文链接
  • 万方数据
  • 维普
On-site and real-time non-destructive measurement of elastic constants for materials of a component in a in-service structure is a challenge due to structural complexities,such as ambiguous boundary,variable thickness,nonuniform material properties.This work develops for the first time a method that uses ultrasound echo groups and artificial neural network(ANN)for reliable on-site real-time identification of material parameters.The use of echo groups allows the use of lower frequencies,and hence more accommodative to structural complexity.To train the ANNs,a numerical model is established that is capable of computing the waveform of ultrasonic echo groups for any given set of material properties of a given structure.The waveform of an ultrasonic echo groups at an interest loca-tion on the surface the structure with material parameters varying in a predefined range are then computed using the numerical model.This results in a set of dataset for training the ANN model.Once the ANN is trained,the mate-rial parameters can be identified simultaneously using the actual measured echo waveform as input to the ANN.Intensive tests have been conducted both numerically and experimentally to evaluate the effectiveness and accuracy of the currently proposed method.The results show that the maximum identification error of numerical example is less than 2％,and the maximum identification error of experimental test is less than 7％.Compared with currently prevailing methods and equipment,the proposefy the density and thickness,in addition to the elastic constants.Moreover,the reliability and accuracy of inverse prediction is significantly improved.Thus,it has broad applications and enables real-time field measurements,which has not been fulfilled by any other available methods or equipment.

Parameter identificationUltrasonic echo groupHigh-precision modelingArtificial neural networkNDT

Shuyong Duan、Jialin Zhang、Heng Ouyang、Xu Han、Guirong Liu

展开 >

State Key Laboratory of Reliability and Intelligence of Electrical Equipment,Hebei University of Technology,Tianjin 300401,China

Department of Aerospace Engineering and Engineering Mechanics,University of Cincinnati,Cincinnati 45221,USA

国家自然科学基金Funds for Creative Research Groups of Hebei Province of ChinaTianjin Municipal Science and Technology Plan Project of ChinaKey R& D Program of Hebei Province of China国家重点研发计划

51805141E202020214219ZXZNGX0010019227208D2020YFB2009400

2024

10.1186/s10033-023-00989-0

中国机械工程学报
中国机械工程学会

中国机械工程学报

CSTPCD
影响因子：0.765
ISSN：1000-9345
年,卷(期)：2024.37(1)