Study on intelligent and visualization method of ultrasonic testing of composite materials based on deep learning

Abstract

Intelligent ultrasonic testing technology of composite materials can greatly reduce the dependence on people and improve the efficiency of ultrasonic testing. The combination of ultrasonic testing and visual positioning technology can realize strong robust visual interpretation of ultrasonic testing results. In this paper, the DiMP tracking model is improved by using the Wasserstein distance, and the intelligent tracking and positioning of ultrasonic probe is realized. At the same time, an ultrasonic signal classification network based on 1DCNN depth neural network is built to realize the intelligent detection of ultrasonic signals, and an effective data connection mode is designed to make the two networks work together, so that the intelligent interpretation and visual display of internal defects of composite materials can be realized. The experimental results show that the interpretation accuracy of the method proposed in this paper reaches 98.74%, and the Kappa coefficient reaches 0.97. The comparison results with other models show that the improved model in this paper is more excellent, and the AUC and Precision values are increased by 6.4% and 8.32% respectively compared with the benchmark.