Correlating fracture toughness and fracture surface roughness via correlation length scale

Barak, Yahel; Srivastava, Ankit; Osovski, S.

doi:10.1007/s10704-019-00377-7

Cited by 16 publications

(4 citation statements)

References 32 publications

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“…4(b)]. This is significantly lower than the values of the actual roughness exponent observed for cracked aluminum alloys [38][39][40]. There is very little difference between the exponent values in the two directions ζ x and ζ y , although initially ζ x is slightly larger.…”

Section: B Roughnessmentioning

confidence: 83%

Striation lines in intermittent fatigue crack growth in an Al alloy

Kinnunen

Lomakin

Mäkinen

et al. 2023

Phys. Rev. Materials

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Fatigue failure of crystalline materials is a difficult problem in science and engineering, and recent results have shown that fatigue crack growth can occur in intermittent jumps that have fat-tailed distributions. As fatigue crack propagation is known to leave markings-called striations-on the fracture surface, the distances between these should also have fat-tailed distributions if the crack propagation is intermittent. Here, we combine macroscale crack tip tracking in fatigue crack growth measurements of aluminum 5005 samples with postmortem scanning electron microscopy imaging of the striation lines. We introduce two different methods for extracting the striation line spacing from the images. What we find is a similar distribution of striation spacings as jump sizes using one of our methods, but the average striation spacing does not correlate with the crack growth rate. We conclude that we observe avalanchelike crack propagation, reflected in both the macroscale crack tip tracking as well as the analysis of the fracture surfaces. Our results show that the fracture surfaces can be used to study the intermittency of fatigue crack propagation and in development of crack-resistant materials. The advantages and disadvantages of the two methods introduced are discussed.

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Section: B Roughnessmentioning

confidence: 83%

Striation lines in intermittent fatigue crack growth in an Al alloy

Kinnunen

Lomakin

Mäkinen

et al. 2023

Phys. Rev. Materials

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“…The aim of quantitative fractography is to find well defined mathematical descriptors which represent the complex topographic morphology of fracture surfaces. These complex morphologies encode the interlacing of the material's microstructure and intrinsic mechanical properties with the externally applied loads and environmental conditions 30 . For brittle materials, such as ceramics, the fracture process is often observed to take place using two alternating micromechanisms: transgranular and intergranular crack growth.…”

Section: Resultsmentioning

confidence: 99%

Toward quantitative fractography using convolutional neural networks

Tsopanidis

Moreno²,

Osovski

2020

Engineering Fracture Mechanics

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The science of fractography revolves around the correlation between topographic characteristics of the fracture surface and the mechanisms and external conditions leading to their creation. While being a topic of investigation for centuries, it has remained mostly qualitative to date. A quantitative analysis of fracture surfaces is of prime interest for both the scientific community and the industrial sector, bearing the potential for improved understanding on the mechanisms controlling the fracture process and at the same time assessing the reliability of computational models currently being used for material design. With new advances in the field of image analysis, and specifically with machine learning tools becoming more accessible and reliable, it is now feasible to automate the process of extracting meaningful information from fracture surface images. Here, we propose a method of identifying and quantifying the relative appearance of intergranular and transgranular fracture events from scanning electron microscope images. The newly proposed method is based on a convolutional neural network algorithm for semantic segmentation. The proposed method is extensively tested and evaluated against two ceramic material systems (Al 2 O 3 ,M gAl 2 O 4 ) and shows high prediction accuracy, despite being trained on only one material system (M gAl 2 O 4 ). While here attention is focused on brittle fracture characteristics, the method can be easily extended to account for other fracture morphologies, such as dimples, fatigue striations, etc.The fracture process of materials is governed by both extrinsic (e.g. imposed loading, environmental conditions) and intrinsic (microstructure) characteristics. One may thus expect, that the fracture surface will contain evidence regarding the influence of both the intrinsic and extrinsic characteristics of the fracture process. Fractography is a powerful tool employed to study fracture

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“…They found a correspondence between the Charpy impact absorption and the fractal dimension by the fractal analysis of this contour structure [2]. Another study revealed that K Ic can be estimated by extracting the roughness of a fracture surface measured by stereo imaging using an electron microscope [2,3]. From an engineering perspective, it is time-consuming and expensive to obtain the 3D information of a fracture surface; thus, it is desirable to estimate parameters of fracture process such as K Ic from two-dimensional (2D) images, which can be acquired at a relatively low cost.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Prediction of Fracture Toughness $(K_{{\rm I}c})$ of Polymer by Fractography Using Deep Neural Networks

Mototake¹,

Ito²,

Demura³

2022

Preprint

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Fracture surfaces provide various types of information about fracture. The fracture toughness KIc, which represents the resistance to fracture, can be estimated using the three-dimensional (3D) information of a fracture surface, i.e., its roughness. However, this is time-consuming and expensive to obtain the 3D information of a fracture surface; thus, it is desirable to estimate KIc from a two-dimensional (2D) image, which can be easily obtained. In recent years, methods of estimating a 3D structure from its 2D image using deep learning have been rapidly developed. In this study, we propose a framework for fractography that directly estimates KIc from a 2D fracture surface image using deep neural networks (DNNs). Typically, image recognition using a DNN requires a tremendous amount of image data, which is difficult to acquire for fractography owing to the high experimental cost. To compensate for the limited data, in this study, we used the transfer learning (TL) method, and constructed high-performance prediction models even with a small dataset by transferring machine learning models trained using other large datasets. We found that the regression model obtained using our proposed framework can predict KIc in the range of approximately 1-5 [MPa √ m] with a standard deviation of the estimation error of approximately ±0.37 [MPa √ m]. The present results demonstrate that the DNN trained with TL opens a new route for quantitative fractography by which parameters of fracture process can be estimated from a fracture surface even with a small dataset. The proposed framework also enables the building of regression models in a few hours. Therefore, our framework enables us to screen a large number of image datasets available in the field of materials science and find candidates that are worth expensive machine learning analysis.

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Correlating fracture toughness and fracture surface roughness via correlation length scale

Cited by 16 publications

References 32 publications

Striation lines in intermittent fatigue crack growth in an Al alloy

Striation lines in intermittent fatigue crack growth in an Al alloy

Toward quantitative fractography using convolutional neural networks

Quantitative Prediction of Fracture Toughness $(K_{{\rm I}c})$ of Polymer by Fractography Using Deep Neural Networks

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