Fiber-LIBS技术结合SVM鉴定铝合金牌号

Identification of Aluminum Alloy Grades by Fiber-Laser Induced Breakdown Spectroscopy Combined with Support Vector Machine

相比于传统的固体激光器,光纤激光器有利于设备的小型化和激光诱导击穿光谱(LIBS)技术的推广。将光纤激光器LIBS(Fiber-LIBS)技术应用于铝合金牌号的识别,采用数据筛选、归一化、支持向量机和主成分分析相结合的分类算法,对6种牌号共24块铝合金样品按牌号分类。结果表明:与单纯应用支持向量机的分类算法相比,数据筛选、归一化、支持向量机和主成分分析相结合的分类算法能够将平均预测准确率从92.34%提高到99.83%,并且可将建模时间缩短一个数量级以上。实验结果表明了光纤激光器应用于LIBS系统中进行金属牌号识别的可行性。

Compared with the traditional solid state lasers, the fiber lasers is conducive to the miniaturization of devices and the promotion of laser induced breakdown spectroscopy （LIBS） technology. In this paper, the fiber lasers LIBS （Fiber-LIBS） technology is applied to grade identification of aluminum alloy. The data classification, normalization, support vector machine, and principal component analysis are used to classify the grades of 24 samples of 6 kinds of aluminum alloys. The results show that, compared with the simple classification algorithm based on the support vector machine classification algorithm, the data filtering, normalization, and support vector machine combined with the principal component analysis can make the average prediction accuracy rate increase from 92.34G to 99.83G, and can decrease the modeling time more than one order of magnitude. The experimental results show the feasibility of fiber lasers used in LIBS system for the metal grade recognition.