【摘要】 为准确识别铝合金的弯曲损伤,通过铝合金7N01三点弯曲实验,根据所提取声发射信号特征,采用三层BP神经网络对铝合金进行损伤识别。结果表明,铝合金弯曲损伤检测正确率达87.5%。BP神经网络技术与声发射方法能够准确识别7N01的弯曲损伤,为多参数、大数据量智能检测技术提供了参考依据。更多还原

【Abstract】 Aimed at an accurate identification of the bending damage of aluminum alloy,this paper discusses the extraction of acoustic emission signals through three-point bending experiments on aluminum alloy 7N01 and the identification of the bending damage of aluminum alloy according to the signal characteristics and using BP neural network.The results show the correct detection rate of 87.5%.BP neural network technology,combined with acoustic emission method,enables accurate identification of 7N01 bending damage,significantly contributing to intelligent detection technologies designed for large amount of data and multiple parameters.更多还原