A Data-Driven Framework for Early-Stage Fatigue Damage Detection in Aluminum Alloys Using Ultrasonic Sensors

Susheel Dharmadhikari,Asok Ray,Amrita Basak,Chandrachur Bhattacharya

doi:10.3390/machines9100211

Abstract

The paper presents a coupled machine learning and pattern recognition algorithm to enable early-stage fatigue damage detection in aerospace-grade aluminum alloys. U- and V-notched Al7075-T6 specimens are instrumented with a pair of ultrasonic sensors and, thereafter, tested on an MTS apparatus integrated with a confocal microscope and a digital microscope. The confocal microscope is focused on the notch root of the specimens, whereas the digital microscope is focused on the side of the notch. Two features, viz., the crack opening displacement (COD) and the crack length, are extracted during the tests in addition to the ultrasonic signal data. These signal data are analyzed using a machine learning framework that is built upon a symbolic time-series algorithm. This framework is interrogated for crack detection in the crack coalescence (CC) regime defined by COD of ~3 μm and detected through the confocal microscope. Additionally, the framework is probed in the crack propagation (CP) regime characterized by a crack length of ~0.2 mm and detected via the digital microscope. For the CC regime, training accuracies of 79.82% and 81.94% are achieved, whereas testing accuracies of 68.18% and 74.12% are observed for the U- and V-notched specimens, respectively. For the CP regime, overall training accuracies of 88.3% and 91.85% are observed, and accordingly, testing accuracies of 81.94% and 85.62% are obtained for the U- and V-notched specimens, respectively. The results show that a combined machine learning and pattern recognition algorithm enables robust and reliable fatigue damage detection in aerospace structural components.

Highlights

High-strength aluminum alloys such as Al2024, Al6061, and Al7075 are widely used in the fabrication of critical structural parts of aircraft components encompassing fuselage, fittings, gears, shafts, valves, etc
Fatigue failures are challenging to predict and control due to their occurrence at seemingly safe loads where the structure operates well below the yield strength or the ultimate tensile strength of the material [1]. The mechanisms behind such failures are attributed to the cumulative accumulation of damage that leads to fatigue crack initiation and eventually to fracture
Fatigue damage detection using a sensor-based approach, presents an alternative to ensure the reliable operation of critical aerospace components

Summary

Introduction

High-strength aluminum alloys such as Al2024, Al6061, and Al7075 are widely used in the fabrication of critical structural parts of aircraft components encompassing fuselage, fittings, gears, shafts, valves, etc. Fatigue damage detection using a sensor-based approach, presents an alternative to ensure the reliable operation of critical aerospace components. With the recent developments, several machine learning algorithms, such as long short-term memory, recurrent neural networks, and symbolic time series analysis (STSA) have been implemented for timeseries classification [17]. Among these methods, STSA has shown a successful damage detection capability with ultrasonic signals [3].

Specimen Design

Machine Learning Framework

Ultrasonic Time-Series Signal

Regime-Specific Fatigue Crack Detection

Findings

Fatigue Crack Detection in the CC Regime