| 基于声发射信号的铝合金材料损伤表征识别 |
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| 作者姓名： | 张卫冬 张习文 杨斌 丁贤飞 艾轶博 |
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| 作者单位： | 北京科技大学国家材料服役安全科学中心, 北京 100083 |
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| 基金项目： | 国家自然科学基金资助项目(61273205,51005014)教育部中央高校基本科研业务专项(FRF-SD-028A)“十一五”国家科技支撑计划资助项目(2009BAG12A07-D07) |
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| 摘 要： | 随着高速铁路的不断提速,高铁轻量化设计中广泛采用高强铝合金材料,但高速列车齿轮箱体服役安全评价亟待完善.本文针对高速列车齿轮箱体使用的铝合金材料服役特性,搭建了声发射检测拉伸试验系统,运用BP神经网络算法对声发射信号进行训练与识别,实现对箱体材料拉伸损伤表征识别与材料服役状态的安全预警.本研究为材料损伤状态的无损实时识别提供了一种识别与预警方法.
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| 关 键 词： | 铝合金 声发射 损伤探测 神经网络 模式识别 |
| 收稿时间： | 2013-02-14 |
Damage characterization and recognition of aluminum alloys based on acoustic emission signal |
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| Affiliation: | National Center for Materials Service Safety,University of Science and Technology Beijing,Beijing 100083,China |
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| Abstract: | With the rapid development of high-speed rails, high-strength aluminum alloys are widely used in the lightweight design, but the service safety assessment of gear boxes in high-speed trains needs to be improved in China. An acoustic emission tensile test system was built for high-speed train gearbox shells made of aluminum alloys. After training and recognition by a BP neural network, acoustic emission signal was used for characterizing tensile damage in the materials and warning the materials service status. The research provides a method of nondestructive real-time characterization and warning for damage in aluminum alloys. |
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