Ultrasonic testing of rivet in multilayer structure using a convolutional neural network on edge device

Abstract

Rivets are used to assemble layers in the air intakes, fuselages, and wings of an aircraft. After a long time of working under extreme conditions, pitting corrosion could appear in the rivets of the aircraft. The rivets could be broken down and thread the safety of the aircraft. In this paper, we proposed an ultrasonic testing method integrated with convolutional neural network (CNN) for the detection of corrosion in the rivets. The CNN model was designed to be lightweight enough to be able to run on edge devices. The CNN model was trained with a very limited sample of rivets, from 3 to 9 artificial pitting corrosive rivets. The results show that the proposed approach could detect up to 95.2% of pitting corrosion using experimental data with three training rivets. The detection accuracy could be improved to 99% by nine training rivets. The CNN model was implemented and ran on an edge device (Jetson Nano) in real-time with a small latency of 1.65 ms.