摘要
冶金行业是我国国民经济的支柱型产业。为了更好地保证钢的生产质量,将激光诱导击穿光谱技术（LIBS）引用到钢水成分实时在线检测。在线分析过程中,将光谱仪接收到的光谱数据传输到上位机,利用LabVIEW完成数据的采集存储。同时,LabVIEW可以通过ActiveX技术实现与MATLAB的混合编程,为了提高液态钢测量的精确度,将RBF神经网络运用到激光诱导击穿光谱技术数据分析中进行算法模型优化,在优化的模型下对元素进行定性以及定量分析。本系统基于LabVIEW框架实现数据采集以及数据分析,并通过实验证明此设计的可行性。
Metallurgical industry is the pillar industry of China ＇s national economy. Laser induced breakdown spectroscopy（ LIBS） is used to detect the molten steel composition to ensure the quality of steel production. In the online analysis process,the spectrometer received spectrum data and transmitted them to the host computer,and then stored the data through LabVIEW. At the same time,LabVIEW and MATLAB mixed programming could be achieved through Active X technology. In order to improve the accuracy of liquid steel measurement,the RBF neural network was applied for data analysis to optimize the algorithm model which could help us do well in qualitative and quantitative analysis. The system is based on LabVIEW framework to achieve data acquisition and data analysis,and the feasibility of this design can be proved through experiment.
作者
马翠红
赵月华
孟凡伟
MA Cuihong;ZHAO Yuehua;MENG Fanwei(College of Electrical Engineering, North China University of Science and Technology, Tangshan Hebei 063210, China)
出处
《激光杂志》
北大核心
2018年第4期30-33,共4页
Laser Journal
基金
国家自然科学基金项目(No.61271402)
