摘要
为了解决激光超声检测过程中定量识别表面缺陷深度较困难的问题,提出了一种粒子群(PSO)优化BP神经网络表面矩形缺陷深度定量识别方法。基于热弹机制,利用有限元软件COMSOL建立了利用激光超声检测含有表面缺陷铝材料的有限元模型,得到了脉冲激光照射下不同深度缺陷对应的透射波信号,提取透射波信号的时域峰值、中心频率、频域上3 dB带宽、上限截止频率和下限截止频率等多个变量作为神经网络的特征向量,建立了PSO-BP神经网络缺陷深度定量识别模型,实现了0.1~3 mm深度缺陷的定量识别。计算结果表明:经过粒子群算法优化后的BP神经网络能够准确地识别出金属表面缺陷的深度信息,识别相对误差在6%以内,结果证明了该神经网络模型对矩形缺陷深度的识别具有一定的可行性和准确性。
Aiming to solve the problem of difficult quantitative identification of surface defect depth during laser ultrasonic detection,a particle swarm(PSO)optimized quantitative identification method for BP neural network surface rectangular defect depth is proposed.Based on the thermoelastic mechanism,a finite element model for laser ultrasonic detection of aluminium materials containing surface defects was established by using the finite element software COMSOL,the transmission wave signals corresponding to defects of different depths under pulsed laser irradiation were obtained,and then the time domain peak,centre frequency,3 dB bandwidth in the frequency domain,upper cut-off frequency,and lower cut-off frequency of the transmission wave signals were extracted as the feature vectors of the neural network.A quantitative recognition model of PSO-BP neural network defect depth was developed to achieve the quantitative recognition of defects from 0.1 mm to 3 mm in depth.The calculation results show that the BP neural network optimized by the particle swarm algorithm can accurately identify the depth information of metal surface defects,and the relative error of identification is within 6%,which proves that the neural network model has certain feasibility and accuracy for the identification of rectangular defect depth.
作者
陈超
张兴媛
陆思烨
Chen Chao;Zhang Xingyuan;Lu Siye(School of Air Transport,Shanghai University of Engineering Science,Shanghai 201620,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2021年第22期497-507,共11页
Laser & Optoelectronics Progress
基金
国家自然科学基金(51465047)。
关键词
传感器
激光超声
有限元
神经网络
粒子群
表面缺陷
sensors
laser ultrasound
finite element
neural networks
particle swarm
surface defects
