Abstract
An aircraft is a multilayer structure that is assembled by rivets. Under extreme working conditions, corrosion appears and quickly propagates at the rivet sites of the layers; thus, it threads the integrity and safety of the aircraft. Corrosion usually occurs at the hidden layer around the rivet, making it difficult to detect. This paper proposes a machine learning approach incorporating an electromagnetic testing system to detect the hidden corrosion at the riveting site effectively. Several machine learning methods will be investigated for the detection of different sizes and locations of corrosion. The training strategy of the machine-learning models on the small numbers of data will also be investigated. The result shows that the proposed approach could effectively detect 89.48% of the hidden corrosion having from 2.8 to 195.4 mm3 with only 20% of training data and could be increased to 99.0% with 60–80% of the training data.
Funder
This study was supported by research fund from Chosun University, 2020
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference26 articles.
1. Aircraft Corrosion – Review of Corrosion Processes and its Effects in Selected Cases
2. The Aloha-airlines accident—A new era for aging aircraft;Hendricks,1991
3. Multiple-site and widespread fatigue damage in aging aircraft
4. Ultrasonic Total Focusing Imaging Method of Multilayer Composite Structures Using the Root-Mean-Square (RMS) Velocity;Xufei;Adv. Mater. Sci. Eng.,2021
5. Nondestructive Testing for Corrosion Evaluation of Metal under Coating;Ruikun;Adv. Mater. Sci. Eng.,2021
Cited by
11 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献