| 基于卷积神经网络模型的Gd2O3/6061Al中子屏蔽材料的力学性能预测 |
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| 引用本文： | 张鹏,李靖,王文先,贾程鹏,马颖峰,徐文瑞. 基于卷积神经网络模型的Gd2O3/6061Al中子屏蔽材料的力学性能预测[J]. 原子能科学技术, 2020, 54(8): 1513-1518. DOI: 10.7538/yzk.2020.youxian.0208 |
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| 作者姓名： | 张鹏 李靖 王文先 贾程鹏 马颖峰 徐文瑞 |
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| 作者单位： | 太原理工大学 物理与光电工程学院，山西 太原030024；新型传感器与智能控制教育部重点实验室，山西 太原030024；太原理工大学 材料科学与工程学院，山西 太原030024 |
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| 摘 要： | 提出了一种卷积神经网络模型来预测Gd2O3/6061Al中子屏蔽材料的力学性能。以Gd2O3/6061Al中子屏蔽材料的EBSD微观形貌及其相应的拉伸性能作为数据集来训练及验证卷积神经网络模型。结果表明：使用多个显微图像，不需任何人工图像处理，卷积神经网络可得到良好的训练结果，其性能优于传统的测试方法；卷积神经网络捕捉到晶粒的存在和晶粒的一些统计信息；晶粒数目和晶粒大小之间具有很强的相关性。
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| 关 键 词： | 2O3/6061Al')" >Gd2O3/6061Al, 卷积神经网络, 力学性能, 预测 |
Prediction of Mechanical Property of Gd2O3/6061Al Neutron Shielding Material Based on Convolutional Neural Network Model |
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| ZHANG Peng,LI Jing,WANG Wenxian,JIA Chengpeng,MA Yingfeng,XU Wenrui. Prediction of Mechanical Property of Gd2O3/6061Al Neutron Shielding Material Based on Convolutional Neural Network Model[J]. Atomic Energy Science and Technology, 2020, 54(8): 1513-1518. DOI: 10.7538/yzk.2020.youxian.0208 |
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| Authors: | ZHANG Peng LI Jing WANG Wenxian JIA Chengpeng MA Yingfeng XU Wenrui |
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| Affiliation: | College of Physics and Optoelectronics, Taiyuan University of Technology, Taiyuan 030024, China; Key Laboratory of Advanced Transducers and Intelligent Control System, Ministry of Education, Taiyuan 030024, China; College of Materials Science and Engineering, Taiyuan University of Technology, Taiyuan 030024, China |
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| Abstract: | A convolutional neural network model was proposed to predict the mechanical properties of Gd2O3/6061Al neutron shielding material. The EBSD microstructure of Gd2O3/6061Al neutron shielding material and its corresponding tensile properties were used as data set to train and verify the convolutional neural network model. The results show that using multiple microscopic images without any artificial image processing, the convolutional neural network can get good training results and its performance is better than that of traditional testing methods. The convolutional neural network can capture the existence of grains and some statistical informations of grains. There is a strong correlation between grain number and grain size. |
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| Keywords: | Gd2O3/6061Al convolutional neural network mechanical property prediction |
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