In Situ Capture of High-Temperature Precipitate Phases in Ti-48Al-2Cr-2Nb Alloy Using Convolutional Neural Networks
TiAl intermetallic alloy is a crucial high-performance material, and its microstructure evolution at high temperatures is closely related to the process parameters. Observing the lamellar structure is key to exploring growth kinetics, and the feature extraction of precipitate phases can provide an e...
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2025-06-01
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| Series: | Crystals |
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| Online Access: | https://www.mdpi.com/2073-4352/15/6/577 |
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| author | Xiaolei Li Chuanqing Huang Sen Zhao Linlin Cui Shirui Guo Bo Zheng Yinghao Cui Yongqian Chen Yue Zhao Lujun Cui Chunjie Xu |
| author_facet | Xiaolei Li Chuanqing Huang Sen Zhao Linlin Cui Shirui Guo Bo Zheng Yinghao Cui Yongqian Chen Yue Zhao Lujun Cui Chunjie Xu |
| author_sort | Xiaolei Li |
| collection | DOAJ |
| description | TiAl intermetallic alloy is a crucial high-performance material, and its microstructure evolution at high temperatures is closely related to the process parameters. Observing the lamellar structure is key to exploring growth kinetics, and the feature extraction of precipitate phases can provide an effective basis for subsequent evolution studies and process parameter settings. Traditional observation methods struggle to promptly grasp the growth state of lamellar structures, and conventional object detection has certain limitations for clustered lamellar structures. This paper introduces a novel method for high-temperature precipitate phase feature extraction based on the YOLOv5-obb rotational object detection network, and a corresponding precipitate phase dataset was created. The improved YOLOv5-obb network was compared with other detection networks. The results show that the proposed YOLOv5-obb network model achieved a precision rate of 93.6% on the validation set for detecting and identifying lamellar structures, with a detection time of 0.02 s per image. It can effectively and accurately identify γ lamellar structures, providing a reference for intelligent morphology detection of alloy precipitate phases under high-temperature conditions. This method achieved good detection performance and high robustness. Additionally, the network can obtain precise positional information for target structures, thus determining the true length of the lamellar structure, which provides strong support for subsequent growth rate calculations. |
| format | Article |
| id | doaj-art-32cddfa3ec774501b76eeb823582fd4b |
| institution | OA Journals |
| issn | 2073-4352 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Crystals |
| spelling | doaj-art-32cddfa3ec774501b76eeb823582fd4b2025-08-20T02:24:34ZengMDPI AGCrystals2073-43522025-06-0115657710.3390/cryst15060577In Situ Capture of High-Temperature Precipitate Phases in Ti-48Al-2Cr-2Nb Alloy Using Convolutional Neural NetworksXiaolei Li0Chuanqing Huang1Sen Zhao2Linlin Cui3Shirui Guo4Bo Zheng5Yinghao Cui6Yongqian Chen7Yue Zhao8Lujun Cui9Chunjie Xu10School of Mechanical & Electronic, Zhongyuan University of Technology, Zhengzhou 450007, ChinaSchool of Mechanical & Electronic, Zhongyuan University of Technology, Zhengzhou 450007, ChinaSchool of Mechanical & Electronic, Zhongyuan University of Technology, Zhengzhou 450007, ChinaSchool of Mechanical & Electronic, Zhongyuan University of Technology, Zhengzhou 450007, ChinaSchool of Mechanical & Electronic, Zhongyuan University of Technology, Zhengzhou 450007, ChinaSchool of Mechanical & Electronic, Zhongyuan University of Technology, Zhengzhou 450007, ChinaSchool of Mechanical & Electronic, Zhongyuan University of Technology, Zhengzhou 450007, ChinaSchool of Mechanical & Electronic, Zhongyuan University of Technology, Zhengzhou 450007, ChinaSchool of Mechanical & Electronic, Zhongyuan University of Technology, Zhengzhou 450007, ChinaSchool of Mechanical & Electronic, Zhongyuan University of Technology, Zhengzhou 450007, ChinaSchool of Materials Science and Engineering, Xi’an University of Technology, Xi’an 710048, ChinaTiAl intermetallic alloy is a crucial high-performance material, and its microstructure evolution at high temperatures is closely related to the process parameters. Observing the lamellar structure is key to exploring growth kinetics, and the feature extraction of precipitate phases can provide an effective basis for subsequent evolution studies and process parameter settings. Traditional observation methods struggle to promptly grasp the growth state of lamellar structures, and conventional object detection has certain limitations for clustered lamellar structures. This paper introduces a novel method for high-temperature precipitate phase feature extraction based on the YOLOv5-obb rotational object detection network, and a corresponding precipitate phase dataset was created. The improved YOLOv5-obb network was compared with other detection networks. The results show that the proposed YOLOv5-obb network model achieved a precision rate of 93.6% on the validation set for detecting and identifying lamellar structures, with a detection time of 0.02 s per image. It can effectively and accurately identify γ lamellar structures, providing a reference for intelligent morphology detection of alloy precipitate phases under high-temperature conditions. This method achieved good detection performance and high robustness. Additionally, the network can obtain precise positional information for target structures, thus determining the true length of the lamellar structure, which provides strong support for subsequent growth rate calculations.https://www.mdpi.com/2073-4352/15/6/577γ lamellar microstructurerotated boxobject detectionYOLOv5computer vision |
| spellingShingle | Xiaolei Li Chuanqing Huang Sen Zhao Linlin Cui Shirui Guo Bo Zheng Yinghao Cui Yongqian Chen Yue Zhao Lujun Cui Chunjie Xu In Situ Capture of High-Temperature Precipitate Phases in Ti-48Al-2Cr-2Nb Alloy Using Convolutional Neural Networks Crystals γ lamellar microstructure rotated box object detection YOLOv5 computer vision |
| title | In Situ Capture of High-Temperature Precipitate Phases in Ti-48Al-2Cr-2Nb Alloy Using Convolutional Neural Networks |
| title_full | In Situ Capture of High-Temperature Precipitate Phases in Ti-48Al-2Cr-2Nb Alloy Using Convolutional Neural Networks |
| title_fullStr | In Situ Capture of High-Temperature Precipitate Phases in Ti-48Al-2Cr-2Nb Alloy Using Convolutional Neural Networks |
| title_full_unstemmed | In Situ Capture of High-Temperature Precipitate Phases in Ti-48Al-2Cr-2Nb Alloy Using Convolutional Neural Networks |
| title_short | In Situ Capture of High-Temperature Precipitate Phases in Ti-48Al-2Cr-2Nb Alloy Using Convolutional Neural Networks |
| title_sort | in situ capture of high temperature precipitate phases in ti 48al 2cr 2nb alloy using convolutional neural networks |
| topic | γ lamellar microstructure rotated box object detection YOLOv5 computer vision |
| url | https://www.mdpi.com/2073-4352/15/6/577 |
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