This paper investigates the combination of laser-induced breakdown spectroscopy (LIBS) and deep convolutional neural networks (CNNs) to classify copper concentrate samples using pretrained CNN models through transfer learning. Four pretrained CNN models were compared. The LIBS profiles were augmented into 2D matrices. Three transfer learning methods were tried. All the models got a high classification accuracy of >92%, with the highest at 96.2% for VGG16. These results suggested that the knowledge learned from machine vision by the CNN models can accelerate the training process and reduce the risk of overfitting. The results showed that deep CNN and transfer learning have great potential for the classification of copper concentrates by portable LIBS.

为研究在基于热烧蚀效应的激光清洗中激光参数对清洗效果的影响，根据热烧蚀效应原理、傅里叶热传导方程以及能量守恒定理，使用有限元分析软件建立了热烧蚀动态能量守恒的二维激光清洗仿真模型。在该模型中使用了虚拟第三类边界条件，将薄层-基底系统中的激光烧蚀热功率密度与被烧蚀污渍层边界的法向移动速度进行关联，实现了污渍层烧蚀潜热消耗量与激光烧蚀能量之间的动态能量守恒，明确了质量损失与能量消耗的数学关系，使所建立的模型更加理论自恰和精确可靠。利用该模型理论分析了光斑吸收功率、光斑直径、扫描速度以及扫描能量密度对清洗效果的影响，结果表明：污渍平均残留厚度随光斑吸收功率和扫描能量密度增大而减小，且减小的速率会逐渐变慢，当激光功率过大时，基材会出现损伤；污渍平均残留厚度随光斑直径和扫描速度的增大而变大，但减小光斑直径和扫描速度会降低清洗效率。提出了根据污渍层物性参数与扫描能量密度的对应关系优化激光参数的方法，并对非平整表面污渍层进行了多次不间断扫描的仿真分析，得到了清洗效果随扫描次数的变化规律。该研究结果对激光清洗设备的方案设计、优化改进和使用选型具有一定的指导意义。