基于改进UNet网络与晶界优化算法的晶粒度评级方法

doi:10.13251/j.issn.0254-6051.2024.07.025

金属热处理 ›› 2024, Vol. 49 ›› Issue (7): 161-167.DOI: 10.13251/j.issn.0254-6051.2024.07.025

基于改进UNet网络与晶界优化算法的晶粒度评级方法

綦雪倩^1,2, 黄晓红^1,2, 宋月³, 刘彦平^1,2, 张露月^1,2, 张庆军⁴

1.华北理工大学人工智能学院, 河北唐山 063210;
2.河北省工业智能感知重点实验室, 河北唐山 063210;
3.河钢材料技术研究院, 河北石家庄 050023;
4.华北理工大学综合测试分析中心, 河北唐山 063210

收稿日期:2024-02-06 修回日期:2024-05-07 出版日期:2024-07-25 发布日期:2024-08-29
通讯作者: 黄晓红,教授,博士,E-mail:tshxh@163.com
作者简介:綦雪倩(1998—),女,硕士研究生,主要研究方向为深度学习和图像处理,E-mail:2277015560@qq.com。
基金资助:
国家自然科学基金区域创新发展联合基金重点项目(U21A20114)

Grain size grading method based on improved UNet network and optimized algorithm of grain boundary

Qi Xueqian^1,2, Huang Xiaohong^1,2, Song Yue³, Liu Yanping^1,2, Zhang Luyue^1,2, Zhang Qingjun⁴

1. College of Artificial Intelligence, North China University of Science and Technology, Tangshan Hebei 063210, China;
2. Hebei Key Laboratory of Industrial Intelligent Perception, Tangshan Hebei 063210, China;
3. HBIS Material Technology Research Institute, Shijiazhuang Hebei 050023, China;
4. Comprehensive Testing and Analyzing Center, North China University of Science and Technology, Tangshan Hebei 063210, China

Received:2024-02-06 Revised:2024-05-07 Online:2024-07-25 Published:2024-08-29

摘要/Abstract

摘要： 晶粒大小对金属材料的性能有着不容忽视的影响,人工评级晶粒度难以满足当前金属材料检测需求,因此针对奥氏体组织,提出了基于改进UNet网络及晶界优化算法的晶粒度自动评级方法,并与人工评级结果对比,分析了该方法计算所得奥氏体评级结果的准确率。结果表明,通过改进后的UNet网络对奥氏体晶界进行分割,再结合基于霍夫变换的晶界优化算法对孪晶界以及分支毛刺进行检测并去除,可有效优化晶界提取效果,提高后续晶粒度计算的精度。本文提出的算法所得奥氏体评级结果与人工评级结果的绝对误差在0.25以内,该算法可高效、便捷、准确地完成奥氏体晶粒度的评级。

关键词: UNet网络, 霍夫变换, 优化算法, 晶粒度评级

Abstract: Grain size has an undeniable impact on the properties of metal materials, and the manual grading methods of grain size is difficult to meet the current detection needs of metal materials. Therefore, for austenite microstructure, a grain size automatic grading method based on improved UNet network and grain boundary optimized algorithm was proposed, and the accuracy of the austenite grading results calculated by the method was analyzed by comparing with the manual grading results. The results show that when the improved UNet network is used to segment austenite grain boundaries, and then the Hough transform based grain boundary optimization algorithm is used to detect and remove twin grain boundaries and branch burr, the grain boundary extraction effect can be effectively optimized, and the accuracy of subsequent grain size calculation can be improved. The absolute error between the austenite grading results obtained by the proposed algorithm and that by the manual method is within 0.25, indicating that the algorithm can efficiently, conveniently and accurately complete the grading of austenite grain size.

Key words: UNet network, Hough transform, optimized algorithm, grain size grading

中图分类号:

TF31

綦雪倩, 黄晓红, 宋月, 刘彦平, 张露月, 张庆军. 基于改进UNet网络与晶界优化算法的晶粒度评级方法[J]. 金属热处理, 2024, 49(7): 161-167.

Qi Xueqian, Huang Xiaohong, Song Yue, Liu Yanping, Zhang Luyue, Zhang Qingjun. Grain size grading method based on improved UNet network and optimized algorithm of grain boundary[J]. Heat Treatment of Metals, 2024, 49(7): 161-167.

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基于改进UNet网络与晶界优化算法的晶粒度评级方法

Grain size grading method based on improved UNet network and optimized algorithm of grain boundary

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