The analysis of ultrasonic NDE data has traditionally been addressed by a trained operator
manually interpreting data with the support of rudimentary automation tools. Recently, many …

Supervised machine learning techniques are increasingly being combined with ultrasonic
sensor measurements owing to their strong performance. These techniques also offer …

Defect depth is an essential indicator in magnetic flux leakage (MFL) detection and
estimation. The quantification errors for defect depth are closely related to length and width …

将比例边界有限元法(Scaled Boundary Finite Element Method, SBFEM)
和机器学习算法相结合, 基于Lamb 波在含缺陷薄板结构中传播时结构的动力响应变化定量反演 …

Recent years have seen a substantial increase in the application of machine learning (ML)
for automated analysis of nondestructive examination (NDE) data. One of the applications of …

Zn vapour is easily generated on the surface by fusion welding galvanized steel sheet,
resulting in the formation of defects. The present study develops a novel method for …

It is challenging to apply deep learning in professional fields that lack big data support,
especially in industrial structure health assessments using ultrasonic guided wave …

This paper presents a complete procedure for the non-destructive analysis of composite
laminates, taking advantage of the step-heating infrared thermography and the latest …

Magnetic flux leakage testing (MFL) is the most widely used nondestructive testing
technology in the safety inspection of oil and gas pipelines. The analysis of MFL test data is …

High precision classification of ultrasonic signals is helpful to improve the identification and
evaluation accuracy for detecting defects. In the previous research, the deep neural network …