基于激光诱导击穿光谱技术的废旧金属分类辨识  

Classification and identification of scrap metals based on laser-induced breakdown spectroscopy

作　　者：郭美亭 孙兰香[1,2,3] 董伟 王金池[1,2,3] 丛智博 郑黎明[1,2,3] GUO Mei-ting;SUN Lan-xiang;DONG Wei;WANG Jin-chi;CONG Zhi-bo;ZHENG Li-ming(State Key Laboratory of Robotics,Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang110016,China;Key Laboratory of Networked Control Systems,Chinese Academy of Sciences,Shenyang 110016,China;Institutes for Robotics and Intelligent Manufacturing,Chinese Academy of Sciences,Shenyang 110169,China)

机构地区：[1]中国科学院沈阳自动化研究所机器人学国家重点实验室,辽宁沈阳110016 [2]中国科学院网络化控制系统重点实验室,辽宁沈阳110016 [3]中国科学院机器人与智能制造创新研究院,辽宁沈阳110169

出　　处：《冶金分析》2020年第12期72-78,共7页Metallurgical Analysis

基　　金：中国科学院前沿科学重点研究(QYZDJ-SSW-JSC037);中国科学院青年创新促进会;国家重点研发计划(2016YFF0102502);辽宁省兴辽英才计划青年拔尖人才(XLYC1807110)。

摘　　要：废旧金属回收是工业中金属的重要来源之一,是发展循环经济的重要内容。废旧金属产量巨大,通常表面覆盖杂质,凹凸不平,因此对分类方法的判别能力和计算速度提出较高要求。采用激光诱导击穿光谱技术研究分析了7种废旧金属分类识别问题,包括生铝、熟铝、镁、不锈钢、锌、黄铜与红铜。为了符合现场应用条件,实验中每个样本点只激发一次建立并分析了多种分类模型,包括支持向量机(SVM)分类模型,主成分分析方法结合支持向量机(PCA-SVM)分类模型,遗传算法结合支持向量机(GA-SVM)分类模型,遗传算法选择特征光谱结合主成分分析方法和支持向量机(GA-PCA-SVM)分类模型,以及遗传算法选择特征光谱结合主成分分析方法和人工神经网络(GA-PCA-BP)分类模型。通过遗传算法选取包含丰富特征的谱段组合与支持向量机方法相结合建立GA-SVM分类模型,490组验证样本分类准确率为93.47%。为了判断该模型的鲁棒性,对一批新样品,在自研的分选系统上以传送带匀速运行的方式进行测试,获取的750组光谱测试数据,分类准确率为88.27%,证明了该分类模型具有很好的移植性和应用性。Scrap metal recycling was one of the important sources of metals in industry and an important part of developing recycling economy.The production of scrap metal was huge.Usually the surface was covered with impurity and the shape was irregularity.The classification method with strong discriminant ability and fast calculation speed was required.Seven kinds of classification and identification problems for scrap metal,including cast aluminum,wrought aluminum,magnesium,stainless steel,zinc,brass and red copper,were studied and analyzed by means of laser-induced breakdown spectroscopy(LIBS).In order to meet the field application conditions,samples were classified based on optical emission following a single laser pulse in the experiment.Support vector machine(SVM)model,support vector machine combined with principal component analysis(PCA-SVM)model,support vector machine combined with genetic algorithm(GA-SVM)model,support vector machine combined with genetic algorithm and principal component analysis(GA-PCA-SVM)model and back-propagation neural network combined with genetic algorithm and principal component analysis(GA-PCA-BP)model were established and compared.The characteristic spectral segments with ample feature information and less interference were extracted by genetic algorithm.A novel method combining genetic algorithm and support vector machine was proposed,resulting the percentage of 490 validation samples correctly identified was 93.47%.In order to assess the robustness and versatility of the model,a group of new samples were tested on a self-developed sorting system with a conveyor belt running at a uniform speed.The 750 test samples could be classified with 88.27%correct assignments,which proved that the GA-SVM model had good portability and applicability.

关 键 词：废旧金属分类 激光诱导击穿光谱(LIBS) 分选系统 支持向量机 遗传算法 

分 类 号：O433.54[机械工程—光学工程] O657.38[理学—光学]