Energy Dissipation Metrics for Fatigue Damage Detection in the Short Crack Regime for Aluminum Alloys

Dharmadhikari, Susheel; Basak, Amrita

doi:10.1115/gt2021-58787

Cited by 3 publications

(4 citation statements)

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“…Figure 1A shows the design of the specimen with the V‐notch. The one‐sided notch design, which conforms to the ASTM standard E466, 30 imposes a stress concentration near its base thus allowing controlled initiation of fatigue cracks 29,31 . The notch geometries are designed in Solidworks to achieve a stress concentration of 7.1.…”

Section: Experimental Methodsmentioning

confidence: 99%

“…The onesided notch design, which conforms to the ASTM standard E466, 30 imposes a stress concentration near its base thus allowing controlled initiation of fatigue cracks. 29,31 The notch geometries are designed in Solidworks to achieve a stress concentration of 7.1. Waterjet machining is used to manufacture specimens from sheets.…”

Section: Specimen Designmentioning

confidence: 99%

“…To develop a damage detection strategy using the force and displacement data (and the resulting hysteresis behavior), four features are defined 31 . The classification models are built using those features.…”

Section: Dataset Descriptionmentioning

confidence: 99%

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A random forest‐based fatigue damage classification framework using global loading and displacement information

Lee

Dharmadhikari

Basak

2023

Fatigue Fract Eng Mat Struct

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The article presents a random forest‐based damage classification methodology built upon the force and displacement data obtained via fatigue testing of Al7075‐T6 specimens. Four features, namely, average displacement (D), material stiffness (ST), energy dissipation rate (EDR), and cumulative energy dissipation (CED), are defined from the collected data. These features are classified into healthy and cracked states based on the information obtained from a high‐resolution confocal microscope and a digital microscope. Random forest‐based classifiers are, thereafter, trained and tested using the individual as well as different combinations of the four features. The results reveal that, when used individually, CED shows the best accuracy of 80% in detecting a fatigue crack. When two or more pairs of features are used, the accuracy increases significantly to beyond 95%. CED is observed to be the most important feature for developing efficient classifiers for fatigue crack detection. The results demonstrate that the random forest classifier is a viable option to accurately detect fatigue damage based on the four features using only global information.

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Section: Experimental Methodsmentioning

confidence: 99%

Section: Specimen Designmentioning

confidence: 99%

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A random forest‐based fatigue damage classification framework using global loading and displacement information

Lee

Dharmadhikari

Basak

2023

Fatigue Fract Eng Mat Struct

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“…The experimental protocol has been described in detail by [21,[31][32][33] and is briefly summarized in the present work. Figure 5a,b depict the fatigue testing apparatus.…”

Section: Fatigue Testingmentioning

confidence: 99%

A Unified Mixed Deep Neural Network for Fatigue Damage Detection in Components with Different Stress Concentrations

et al. 2023

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The article presents a mixed deep neural network (DNN) approach for detecting micron-scale fatigue damage in high-strength polycrystalline aluminum alloys. Fatigue testing is conducted using a custom-designed apparatus integrated with a confocal microscope and a moving stage to accurately pinpoint the instance of micron-scale crack emergence. The specimens are monitored throughout the duration of the experiment using a pair of high-frequency ultrasonic transducers. The mixed DNN is trained with ultrasonic time-series data that are obtained from two sets of specimens categorized by different stress concentration factors. To understand the effects of mixing the data from both types of specimens, a parametric analysis is performed by varying the amount of training data from each specimen to develop a series of mixed DNNs. The mixed DNN, when tested on unseen data from both specimens, exhibits an accuracy of over 95%. This article, therefore, demonstrates a successful alternative to customized DNNs for new types, geometries, or stress concentration factors in the materials under consideration.

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

et al. 2021

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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.

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Energy Dissipation Metrics for Fatigue Damage Detection in the Short Crack Regime for Aluminum Alloys

Cited by 3 publications

References 0 publications

A random forest‐based fatigue damage classification framework using global loading and displacement information

A random forest‐based fatigue damage classification framework using global loading and displacement information

A Unified Mixed Deep Neural Network for Fatigue Damage Detection in Components with Different Stress Concentrations

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

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