Classification and identification of scrap metals based on laser-induced breakdown spectroscopy
GUO Mei-ting1,2,3, SUN Lan-xiang*1,2,3, DONG Wei1,2,3, WANG Jin-chi1,2,3, CONG Zhi-bo1,2,3, ZHENG Li-ming1,2,3
1. State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang110016, China; 2. Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016,China; 3. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
Abstract：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.
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