By Topic

A Comprehensive Multi-Modal NDE Data Fusion Approach for Failure Assessment in Aircraft Lap-Joint Mimics

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

10 Author(s)
Soumya De ; Department of Electrical and Computer Engineering, Missouri University of Science and Technology (S&T), Rolla, MO, USA ; Kapil Gupta ; R. Joe Stanley ; Mohammad T. Ghasr
more authors

Multi-modal data fusion techniques are commonly used to enhance decision-making processes. In previous research, a comprehensive structural analysis process was developed for quantizing and evaluating characteristics of defects in aircraft lap-joint mimics using eddy current (EC) nondestructive evaluation (NDE) data collected for structural health monitoring. In this research, a comprehensive multi-modal structural analysis process is presented that includes intra- and inter-modal NDE data fusion based on EC, millimeter wave (MW), and ultrasonic (UT) data obtained from five lap-joint mimic test panels. The process includes defect detection, defect characterization, and finite-element modeling-based simulated fatigue loading for structural analysis. The multi-modal structural analysis process is evaluated using four test panels with corroded patches at different layers of the lap joints and one painted pristine panel used as a reference. The test panels are subjected to two rounds of mechanical loading, preceded by multi-modal NDE data obtained before each round. Different NDE modality combinations are examined for test panel modeling, including: 1) EC, 2) UT, 3) MW, 4) EC and UT, 5) EC and MW, and 6) EC, UT, and MW. Experiments are performed to compare the simulated fatigue loading, based on models determined from the different modality combinations, and the mechanical loading results to find susceptible-to-failure areas in the test panels. Experimental results showed that the EC and UT modality combination yielded a correct vulnerable (crack) location recognition rate of 98.8%, an improvement of 14.7% over any individual modality, demonstrating the potential for multi-modal data fusion for characterizing corrosion and defects.

Published in:

IEEE Transactions on Instrumentation and Measurement  (Volume:62 ,  Issue: 4 )