The criteria for microstructure evolution of Cu and Cu-Al alloys induced by cyclic loading

Liu, R.; Zhang, Z.J.; Zhang, Z.F.

doi:10.1016/j.msea.2016.04.069

Cited by 17 publications

(6 citation statements)

References 72 publications

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“…The (A) start and (B) end images are taken by the OM with the fatigue cyclic numbers of 808,162 and 2,559,034 in Cu–Al alloys 32,33 for study and (C) using of mesh morphing process to generate a larger dataset from the start image and end image (red circle indicates the changes of the facture region after morphing.) [Colour figure can be viewed at wileyonlinelibrary.com]…”

Section: Resultsmentioning

confidence: 99%

“…The details of the proposed fatigue fracture image segmentation framework are shown in Figure 3. With enough fatigue fracture images, 32,33 the accuracy of fatigue fracture recognition using deep neural networks can be improved.…”

Section: Methodsmentioning

confidence: 99%

“…The details of pre-treatment and testing condition can be found in the previous study. 32,33,36 The dataset for training and testing will be generated using these metal samples, based on which the AI-based image segmentation system will be designed.…”

Section: Sample Preparationmentioning

confidence: 99%

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Artificial intelligence‐assisted fatigue fracture recognition based on morphing and fully convolutional networks

Lyu

Yang

Liang

et al. 2022

Fatigue Fract Eng Mat Struct

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Fatigue fracture is one of the most common metallic component failure cases in manufacturing industries. The observation on fractography can provide direct evidence for failure analysis. In this study, an image semantic segmentation method based on fully convolutional networks (FCNs) was proposed to figure out the boundary between fatigue crack propagation and fast fracture regions from optical microscope (OM) fractography images. Furthermore, a novel morphing‐based data augmentation method was adopted to enable few‐shot learning of sample images. The proposed framework can successfully segment two categories, namely, the crack propagation and fast fracture regions, thus differentiating the boundary of two regions in one image. This artificial intelligence (AI)‐assisted fatigue analysis architecture can complete the failure analysis procedure in 0.5 s and prove the feasibility of fatigue failure analysis. The segmentation accuracy of self‐developed network achieves 95.4% for the fatigue crack propagation region, as well as 97.2% for the fast fracture region. Not only for semantic segmentation DNN, we also prove that our novel data augmentation method can applied at the instance segmentation DNN, such as mask regional convolutional neural network (mask R‐CNN), one state‐of‐the‐art deep learning network for instance segmentation, to achieve similar accuracy.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Artificial intelligence‐assisted fatigue fracture recognition based on morphing and fully convolutional networks

Lyu

Yang

Liang

et al. 2022

Fatigue Fract Eng Mat Struct

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“…The microstructure evolution during cyclic loading and the different configurations of dislocations that could occur depend on the SFE. Usually, metals with high SFE, as the AA7050 alloy, have a tendency to present heterogeneous distribution of dislocations [27]. Thus, Fig.…”

Section: Discussionmentioning

confidence: 99%

Effect of the interrupted aging heat treatment T6I4 on the tensile properties and fatigue resistance of AA7050 alloy

Antunes

Baptista

Barboza

et al. 2019

J Braz. Soc. Mech. Sci. Eng.

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The tensile response and the fatigue behavior of age-hardenable aluminum alloys are strongly influenced by factors like the size, spacing and volume fraction of strengthening precipitates. Previous researches have shown that the interrupted aging (T6I4) could provide an improved combination of mechanical properties for some alloys. In this study, the tensile properties and high-cycle fatigue behavior of two tempers of AA7050 alloy (the commercial T7451 and the interrupted aging T6I4) were investigated. Transmission electron microscopy analyses of strengthening precipitates were performed, and the results showed that AA7050-T6I4 had a higher volume fraction of strengthening precipitates with smaller size. This microstructural feature was responsible by the higher ductility and toughness shown by T6I4 temper condition while maintaining yield stress and ultimate tensile strength similar to T7451. The smooth samples rotating bending fatigue curves of both material conditions were similar. Nevertheless, the interrupted aging leads to improved notched fatigue resistance and hence lower notch sensitivity. These results were related to the higher activity of screw dislocations and improved dislocation pinning effect during deformation promoted by T6I4 temper.

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“…Crystal orientation influence on the dislocation arrangements of FCC single crystals is presented in Figure 6 [ 121 ]. Comprehensive discussions are referred to the works of [ 93 , 115 , 119 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 ].…”

Section: Self-organization Evidence During Fatiguementioning

confidence: 99%

On the Evidence of Thermodynamic Self-Organization during Fatigue: A Review

Naderi

2020

Entropy

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In this review paper, the evidence and application of thermodynamic self-organization are reviewed for metals typically with single crystals subjected to cyclic loading. The theory of self-organization in thermodynamic processes far from equilibrium is a cutting-edge theme for the development of a new generation of materials. It could be interpreted as the formation of globally coherent patterns, configurations and orderliness through local interactivities by “cascade evolution of dissipative structures”. Non-equilibrium thermodynamics, entropy, and dissipative structures connected to self-organization phenomenon (patterning, orderliness) are briefly discussed. Some example evidences are reviewed in detail to show how thermodynamics self-organization can emerge from a non-equilibrium process; fatigue. Evidences including dislocation density evolution, stored energy, temperature, and acoustic signals can be considered as the signature of self-organization. Most of the attention is given to relate an analogy between persistent slip bands (PSBs) and self-organization in metals with single crystals. Some aspects of the stability of dislocations during fatigue of single crystals are discussed using the formulation of excess entropy generation.

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The criteria for microstructure evolution of Cu and Cu-Al alloys induced by cyclic loading

Cited by 17 publications

References 72 publications

Artificial intelligence‐assisted fatigue fracture recognition based on morphing and fully convolutional networks

Artificial intelligence‐assisted fatigue fracture recognition based on morphing and fully convolutional networks

Effect of the interrupted aging heat treatment T6I4 on the tensile properties and fatigue resistance of AA7050 alloy

On the Evidence of Thermodynamic Self-Organization during Fatigue: A Review

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