High repetition rate laser-ablation spark-induced breakdown spectroscopy (HRR LA-SIBS) coupled with particle swarm optimization-extreme learning machine (PSO-ELM) was developed to realize quantitative elemental analysis of aluminum alloy accurately. A compact Nd: YAG laser operated at 1 kHz was used as the ablation laser and a spark discharge was utilized to enhance the plasma emission. The characteristic lines of the elements were selected as input variables and the parameters of ELM were optimized by the PSO algorithm. The PSO-ELM calibration models were established. The correlation coefficient of prediction set reached to 0.9996, 0.9972 and 0.9999, the root mean square error of prediction set decreased to 0.0138%, 0.0045% and 0.0095% for Mg, Cr, and Cu analysis, respectively. Better predictive performance has been demonstrated in comparison with the univariate and SVM methods. HRR LA-SIBS coupled with PSO-ELM can give convenient, rapid and accurate elemental analysis of alloy samples. Moreover, it will extend the potential applications of HRR LA-SIBS in intelligent manufacturing and on-line monitoring in the future.

开发了高重复频率的激光烧蚀火花诱导击穿光谱仪（HRR LA-SIBS）并结合了粒子群优化-极限学习机（PSO-ELM）以精确地实现铝合金的定量元素分析。使用在1 kHz下工作的紧凑型Nd：YAG激光器作为消融激光器，并利用火花放电增强等离子体的发射。选择元素的特征线作为输入变量，并通过PSO算法优化ELM的参数。建立了PSO-ELM校准模型。Mg，Cr和Cu分析的预测集相关系数分别为0.9996、0.9972和0.9999，预测集的均方根误差分别降至0.0138％，0.0045％和0.0095％。与单变量和SVM方法相比，已经证明了更好的预测性能。HRR LA-SIBS与PSO-ELM结合可以对合金样品进行便捷，快速和准确的元素分析。此外，它将在将来扩展HRR LA-SIBS在智能制造和在线监控中的潜在应用。