Guided Wave-Based Early-Stage Debonding Detection and Assessment in Stiffened Panel Using Machine Learning With Deep Auto-Encoded Features

Abstract

Debonding between stiffener and base plate is a very common type of damage in stiffened panels. Numerous efforts have been made for debonding assessment in the stiffened panel structure using guided wave-based techniques. However, these studies are limited to the detection of through-the-flange-width debonding (i.e., full debonding). This paper attempts to develop a methodology for the detection and assessment of early-stage debonding (i.e., partial debonding) in the stiffened panel using machine learning (ML) algorithms. An experimentally validated finite element (FE) simulation model is used to create an initial guided wave dataset containing several debonding scenarios. This dataset is processed through a data augmentation process, followed by feature extraction involving higher harmonics of guided waves. Thereafter, the extracted feature is compressed using a deep autoencoder model. The compressed feature is used for hyperparameter tuning, training, and testing of several supervised ML algorithms, and their performance in the identification of debonding zone and prediction of its size is analyzed. Finally, the trained ML algorithms are tested with experimental data showing that the ML algorithms closely predict the zones of debonding and their sizes. The proposed methodology is an advancement in debonding assessment, specifically addressing early-stage debonding in stiffened panels.