An application of machine learning for material crack diagnosis using nonlinear ultrasonics

Crack diagnosis in non-destructive testing often requires reference data from the structure before damage or a considerable amount of response data. Also, detecting compression cracks is challenging. In this study, a machine learning-based method is proposed for diagnosing cracks in structures under compression. The method consists of convolutional neural networks (CNN) and fully connected networks (FCN). The CNN extracts features from nonlinear ultrasonic signal data, and the features determine the occurrence of fatigue cracks in a target specimen. Four types of input data are defined in accordance with the number of input frequency combinations. The performance of the proposed method is investigated using each data type to secure efficiency and accuracy in diagnosing aluminum specimens under various compression conditions. As a result, the F1 score, a measure of accuracy, of the proposed method depends on the number of input frequency combinations. The method detects high-compression cracks with high accuracy compared to the present technology specialized for compression cracks in a certain data type. A high accuracy of more than 96% is achieved with less computation time. The proposed method will provide an accurate crack diagnosis for compression cracks with reduced time and effort.