Study on deformation behavior in supercooled liquid region of a Ti-based metallic glassy matrix composite by artificial neural network

Wang, Y.S.; Linghu, R.K.; Zhang, W.; Shao, Yuman; Lan, Aidong; Xu, Jun

doi:10.1016/j.jallcom.2020.155761

Cited by 12 publications

(1 citation statement)

References 43 publications

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“…Deep learning models [16][17][18][19][20] are an effective way of classifying and quantifying fracture characteristics [11][12][13][14][15][16][17][18][19][20][21][22][23][24]. The works presented in the literature show that the machine learning models based on convolutional neural networks (CNN) are very suitable for the detection and classification of microstructures [25][26][27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Swin–UNet++: A Nested Swin Transformer Architecture for Location Identification and Morphology Segmentation of Dimples on 2.25Cr1Mo0.25V Fractured Surface

et al. 2021

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The precise identification of micro-features on 2.25Cr1Mo0.25V steel is of great significance for understanding the mechanism of hydrogen embrittlement (HE) and evaluating the alloy’s properties of HE resistance. Presently, the convolution neural network (CNN) of deep learning is widely applied in the micro-features identification of alloy. However, with the development of the transformer in image recognition, the transformer-based neural network performs better on the learning of global and long-range semantic information than CNN and achieves higher prediction accuracy. In this work, a new transformer-based neural network model Swin‒UNet++ was proposed. Specifically, the architecture of the decoder was redesigned to more precisely detect and identify the micro-feature with complex morphology (i.e., dimples) of 2.25Cr1Mo0.25V steel fracture surface. Swin‒UNet++ and other segmentation models performed state-of-the-art (SOTA) were compared on the dimple dataset constructed in this work, which consists of 830 dimple scanning electron microscopy (SEM) images on 2.25Cr1Mo0.25V steel fracture surface. The segmentation results show Swin‒UNet++ not only realizes the accurate identification of dimples but displays a much higher prediction accuracy and stronger robustness than Swin‒Unet and UNet. Moreover, efforts from this work will also provide an important reference value to the identification of other micro-features with complex morphologies.

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Section: Introductionmentioning

confidence: 99%

Swin–UNet++: A Nested Swin Transformer Architecture for Location Identification and Morphology Segmentation of Dimples on 2.25Cr1Mo0.25V Fractured Surface

et al. 2021

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A review on recent applications of machine learning in mechanical properties of composites

Liang,

Wei,

Peng

et al. 2024

Polymer Composites

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Composites are undergoing extensive research and utilization due to their excellent mechanical properties, driven by human needs. Traditionally, the research methods in materials science predominantly rely on empirical theory or experimental trial and error approaches. However, the increased complexity of composite materials results in a greater intricacy in their mechanical behavior. Consequently, the utilization of traditional research methods may not achieve sufficient efficiency. Materials science is rapidly transitioning into a data‐driven era, with machine learning (ML) emerging as a potent tool to expedite materials development and enhance properties prediction. Significant advancements have been achieved in the application of ML to the study of composite mechanics. In this review article, we elucidate various ML methods employed in the construction of constitutive models for isotropic and anisotropic composites, and delve into the research on construction ML models that leverage input data derived from composite processes, structures, and environmental conditions to predict material mechanical properties. Additionally, we summarize recent noteworthy ML applications in composite design and optimization. Finally, possible prospective viewpoints are proposed for future development, with the aim of providing essential scientific guidance for advancing material science and technology through ML.Highlights Machine learning can address complexity in constitutive model of the anisotropic composites. Machine learning predicts mechanical properties of composites well by process and structure. Machine learning enhances efficiency in inverse design to optimize composites. Limitations, challenges, development trends of ML in composites.

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The Role of Artificial Neural Networks in Prediction of Mechanical and Tribological Properties of Composites—A Comprehensive Review

Paturi¹,

Cheruku²,

Reddy

2022

Arch Computat Methods Eng

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Study on deformation behavior in supercooled liquid region of a Ti-based metallic glassy matrix composite by artificial neural network

Cited by 12 publications

References 43 publications

Swin–UNet++: A Nested Swin Transformer Architecture for Location Identification and Morphology Segmentation of Dimples on 2.25Cr1Mo0.25V Fractured Surface

Swin–UNet++: A Nested Swin Transformer Architecture for Location Identification and Morphology Segmentation of Dimples on 2.25Cr1Mo0.25V Fractured Surface

A review on recent applications of machine learning in mechanical properties of composites

The Role of Artificial Neural Networks in Prediction of Mechanical and Tribological Properties of Composites—A Comprehensive Review

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