CN112489039B - 基于深度学习的铝合金微米级第二相定量统计表征方法 - Google Patents
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Abstract
Description
元素 | Zn | Mg | Cu | Fe | Si | Mn | Cr | Zr | Ti |
T4-6 | 4.53 | 1.1 | 0.23 | 0.17 | 0.088 | 0.34 | 0.18 | 0.12 | 0.046 |
T4-15 | 4.39 | 1.38 | 0.022 | 0.16 | 0.067 | 0.35 | 0.084 | 0.071 | 0.02 |
T5-10 | 4.31 | 1.01 | 0.15 | 0.17 | 0.062 | 0.37 | 0.23 | 0.097 | 0.05 |
T5-15 | 4.23 | 1.09 | 0.16 | 0.17 | 0.058 | 0.37 | 0.22 | 0.11 | 0.048 |
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CN202011500498.XA CN112489039B (zh) | 2020-12-17 | 2020-12-17 | 基于深度学习的铝合金微米级第二相定量统计表征方法 |
US17/229,531 US20230184703A1 (en) | 2020-12-17 | 2021-04-13 | Quantitative statistical characterization method of micron-level second phase in aluminum alloy based on deep learning |
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CN114708269B (zh) * | 2022-06-08 | 2022-08-23 | 武汉理工大学 | 一种基于图像识别预测轴承钢第二相颗粒最大尺寸的方法 |
CN116130037B (zh) * | 2023-01-28 | 2023-10-10 | 钢研纳克检测技术股份有限公司 | 一种材料高通量制备-统计映射表征一体化研发系统 |
CN117542048B (zh) * | 2024-01-05 | 2024-03-22 | 中信戴卡股份有限公司 | 一种亚共晶铝硅合金显微组织特征、缺陷特征的自动识别方法 |
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