GA-BP神经网络结合EDXRF技术实现对中低合金钢中Cr、Mn和Ni元素含量的预测  

Prediction of Cr, Mn, and Ni in Medium and Low Alloy Steels by GA-BP Neural Network Combined with EDXRF Technology

作　　者：宋海声[1] 陈召 徐大诚[2] 徐荣网 Song Haisheng;Chen Zhao;Xu Dacheng;Xu Rongwang(School of Physics and Electronic Engineering,Northwest Normal University,Lanzhou 730070,Gansu,China;School of Electronic Information,Soochow University,Suzhou 215031,Jiangsu,China;Kunshan Soohow Instrument Technology Co.,Ltd.,Suzhou 215300,Jiangsu,China)

机构地区：[1]西北师范大学物理与电子工程学院,甘肃兰州730070 [2]苏州大学电子信息学院,江苏苏州215031 [3]昆山书豪仪器科技有限公司,江苏苏州215300

出　　处：《激光与光电子学进展》2022年第12期534-540,共7页Laser & Optoelectronics Progress

摘　　要：利用能量色散X射线荧光光谱分析(EDXRF)技术与遗传算法优化的反向传播(GA-BP)神经网络对中低合金钢中Cr、Mn和Ni元素进行含量分析。通过能量色散X射线荧光光谱仪对六类中低合金钢标准样品做激发处理,获得X射线荧光光谱,使用两点法扣除背景,求得各元素对应Kα特征峰强度。利用所得108组谱线数据及其对应含量建立GA-BP神经网络,使用训练完成的GA-BP神经网络对另外36组中低合金钢样本含量进行预测,并将所预测结果与基本参数法分析结果和标准样品化学分析结果进行对比,Cr、Mn和Ni元素含量平均误差分别为0.0287%、0.0314%和0.0423%。实验结果表明,遗传算法优化的BP神经网络适用于EDXRF对中低合金钢中Cr、Mn和Ni元素含量的分析。The Cr, Mn, and Ni content of medium and low alloy steel were analyzed using energy dispersive X-ray fluorescence spectroscopy(EDXRF) and black propagation neural network optimized by genetic algorithm(GA-BP).EDXRF was used to excite the six standard samples of medium and low alloy steel and the X-ray fluorescence spectra were obtained. The characteristic peak intensity of each element was obtained by subtracting the background using the two-point method. A total of 108 groups of spectral data and their corresponding content-based GA-BP neural network were obtained. To forecast the contents of 36 low alloy steel samples, the training completion of the GA-BP neural network was used. The predicted results and the fundamental parameter method analysis results were compared. The average errors of the chemical analysis results of the standard samples were 0. 0287%, 0. 0314%, and 0. 0423% for Cr, Mn, and Ni, respectively. The experimental results showed that the BP neural network optimized by the genetic algorithm is suitable for the EDXRF analysis of Cr, Mn, and Ni in medium and low alloy steel.

关 键 词：X射线光学 X射线荧光光谱 中低合金钢 遗传算法 逆向误差传播神经网络 元素含量 

分 类 号：TL82[核科学技术—核技术及应用]