北京科技大学学报2013，Vol.35Issue(5)：626-633,8.
基于声发射信号的铝合金材料损伤表征识别
Damage characterization and recognition of aluminum alloys based on acoustic emission signal
摘要
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.关键词
铝合金/声发射/损伤探测/神经网络/模式识别Key words
aluminum alloys/ acoustic emissions/ damage detection/ neural networks/ pattern recognition分类
矿业与冶金引用本文复制引用
张卫冬,张习文,杨斌,丁贤飞,艾轶博..基于声发射信号的铝合金材料损伤表征识别[J].北京科技大学学报,2013,35(5):626-633,8.基金项目
"十一五"国家科技支撑计划资助项目(2009BAG12A07-D07) （2009BAG12A07-D07）
国家自然科学基金资助项目(61273205,51005014) （61273205,51005014）
教育部中央高校基本科研业务专项(FRF-SD-12-028A) （FRF-SD-12-028A）
