Employing Deep Neural Networks and High‐Throughput Computing for the Recognition and Prediction of Vein‐Like Structures
In this investigation, convolutional neural networks (CNNs) are leveraged to engineer a simple segmentation and recognition algorithm specialized for the delineation of complex, network‐like morphologies—often termed “vein‐like structures (VLSs)”—in scanning electron microscopy (SEM) imagery. These...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | Advanced Intelligent Systems |
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| Online Access: | https://doi.org/10.1002/aisy.202400260 |
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| _version_ | 1846111050591633408 |
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| author | Junbo Niu Zhiyu Chi Feilong Wang Bin Miao Jiaxu Guo ZhiFeng Ding Yin He XinXin Ma |
| author_facet | Junbo Niu Zhiyu Chi Feilong Wang Bin Miao Jiaxu Guo ZhiFeng Ding Yin He XinXin Ma |
| author_sort | Junbo Niu |
| collection | DOAJ |
| description | In this investigation, convolutional neural networks (CNNs) are leveraged to engineer a simple segmentation and recognition algorithm specialized for the delineation of complex, network‐like morphologies—often termed “vein‐like structures (VLSs)”—in scanning electron microscopy (SEM) imagery. These intricate formations frequently appear during the nitriding treatment of medium‐ to high‐carbon alloy steels. To navigate the multifaceted characteristics of such architectures, CNN‐based methodologies are synergized with high‐throughput thermodynamic computations via Thermo‐Calc. This integration aims to quantify both the theoretical upper bounds and the actual values of the VLSs. By establishing deep neural network models for both theoretical upper bounds and actual measurements, the gap between thermodynamics and thermokinetics in the nitriding process is bridged. Applying this amalgamated predictive schema to 8Cr4Mo4V steel, a groundbreaking departure from conventional paradigms that exclusively depend on thermodynamic calculation‐based diffusion models is effectuated. The emergent model yields transformative implications for the metallurgical sector, paving the way for the refinement of future nitriding algorithms and enhancements in nitriding methodologies. |
| format | Article |
| id | doaj-art-818537bfa0594da48e362046b4d8aee0 |
| institution | Kabale University |
| issn | 2640-4567 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Intelligent Systems |
| spelling | doaj-art-818537bfa0594da48e362046b4d8aee02024-12-23T13:10:42ZengWileyAdvanced Intelligent Systems2640-45672024-12-01612n/an/a10.1002/aisy.202400260Employing Deep Neural Networks and High‐Throughput Computing for the Recognition and Prediction of Vein‐Like StructuresJunbo Niu0Zhiyu Chi1Feilong Wang2Bin Miao3Jiaxu Guo4ZhiFeng Ding5Yin He6XinXin Ma7School of Material Science & Engineering Harbin Institute of Technology Harbin 150001 ChinaSchool of Material Science & Engineering Harbin Institute of Technology Harbin 150001 ChinaSchool of Material Science & Engineering Harbin Institute of Technology Harbin 150001 ChinaSchool of Material Science & Engineering Harbin Institute of Technology Harbin 150001 ChinaSchool of Material Science & Engineering Harbin Institute of Technology Harbin 150001 ChinaSchool of Material Science & Engineering Harbin Institute of Technology Harbin 150001 ChinaSchool of Material Science & Engineering Harbin Institute of Technology Harbin 150001 ChinaSchool of Material Science & Engineering Harbin Institute of Technology Harbin 150001 ChinaIn this investigation, convolutional neural networks (CNNs) are leveraged to engineer a simple segmentation and recognition algorithm specialized for the delineation of complex, network‐like morphologies—often termed “vein‐like structures (VLSs)”—in scanning electron microscopy (SEM) imagery. These intricate formations frequently appear during the nitriding treatment of medium‐ to high‐carbon alloy steels. To navigate the multifaceted characteristics of such architectures, CNN‐based methodologies are synergized with high‐throughput thermodynamic computations via Thermo‐Calc. This integration aims to quantify both the theoretical upper bounds and the actual values of the VLSs. By establishing deep neural network models for both theoretical upper bounds and actual measurements, the gap between thermodynamics and thermokinetics in the nitriding process is bridged. Applying this amalgamated predictive schema to 8Cr4Mo4V steel, a groundbreaking departure from conventional paradigms that exclusively depend on thermodynamic calculation‐based diffusion models is effectuated. The emergent model yields transformative implications for the metallurgical sector, paving the way for the refinement of future nitriding algorithms and enhancements in nitriding methodologies.https://doi.org/10.1002/aisy.202400260convolutional neural networksdeep learningnitridingThermo‐Calc simulations |
| spellingShingle | Junbo Niu Zhiyu Chi Feilong Wang Bin Miao Jiaxu Guo ZhiFeng Ding Yin He XinXin Ma Employing Deep Neural Networks and High‐Throughput Computing for the Recognition and Prediction of Vein‐Like Structures Advanced Intelligent Systems convolutional neural networks deep learning nitriding Thermo‐Calc simulations |
| title | Employing Deep Neural Networks and High‐Throughput Computing for the Recognition and Prediction of Vein‐Like Structures |
| title_full | Employing Deep Neural Networks and High‐Throughput Computing for the Recognition and Prediction of Vein‐Like Structures |
| title_fullStr | Employing Deep Neural Networks and High‐Throughput Computing for the Recognition and Prediction of Vein‐Like Structures |
| title_full_unstemmed | Employing Deep Neural Networks and High‐Throughput Computing for the Recognition and Prediction of Vein‐Like Structures |
| title_short | Employing Deep Neural Networks and High‐Throughput Computing for the Recognition and Prediction of Vein‐Like Structures |
| title_sort | employing deep neural networks and high throughput computing for the recognition and prediction of vein like structures |
| topic | convolutional neural networks deep learning nitriding Thermo‐Calc simulations |
| url | https://doi.org/10.1002/aisy.202400260 |
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