激光诱导击穿光谱铝合金在线分类识别系统研究

Research on On-Line Classification System of Aluminum Alloy for Laser-Induced Breakdown Spectrum

铝合金材料凭借其易加工、耐腐蚀、可循环利用等良好性能,在众多工业领域都得到了广泛的应用,已成为仅次于钢铁的第二大金属材料。面对矿产资源的日益匮乏以及大量铝产品到达服役年限,因此铝合金的回收利用就尤为重要,再生循环铝对经济、环境和能源的可持续发展都具有重要意义。目前,由于废旧铝合金种类混杂、形态各异,很难高效对其分类,导致优质铝合金降级使用直接铸造成铝锭。航空航天用铝以2xxx系列和7xxx系列铝合金为主,由于特殊的使用环境,其产品质量好、价值高,降级使用会造成巨大的浪费。本文以航空用铝2xxx系列、 7xxx系列以及铸铝A356三个系列的铝合金自动分类为研究目标,基于激光诱导击穿光谱技术搭建自动化分类检测实验平台,通过图像识别方式完成对动态样品的定位,使其准确被激光诱导击穿光谱(LIBS)捕获完成测试,对于单脉冲LIBS光谱信号,结合密度分布函数思想,分别建立三个系列铝合金的多维高斯概率密度分布判别函数,实现了对2xxx系、 7xxx系以及铸铝A356的高效、高精度、连续分类检测。实验结果表明:系统完成对1.2 m·s^-1传送过程中物料的识别时间为18 ms,定位激光激发控制偏差小于20.83 ms,传送中测试样品的最小尺寸为25 mm;对于高度差异3 mm以内的三个系列铝合金样品,多维高斯概率密度分布方法的平均预测分类识别准确率可达到99.15%,平均建模时间仅需7 ms,与应用广泛的支持向量机(SVM)分类方法相比,预测准确率相当,建模时间提高了一个数量级,该方法分类预测的泛化能力较好,建模效率高。该研究验证了基于激光诱导击穿光谱技术对铝合金进行自动化快速分类检测的有效性,为全自动废旧金属分拣系统的建立提供了理论和技术基础。

Aluminum alloy materials have been widely used in many industrial fields due to their advantages of easy processing,corrosion resistance and recyclability,and has become the second largest metal material after steel.In the face of the scarcity of mineral resources and the large number of aluminum products reaching the service life,the recycling of aluminum alloy is particularly important.Recycled aluminum is of great significance to the sustainable development of economy,environment and energy.At present,it is difficult to classify scrap aluminium alloys efficiently because of their various types and shapes,which leads to the degradation of high-quality aluminium alloys and the direct casting of aluminium ingots.Aluminum for aerospace is mainly made of 2xxx and 7xxx aluminum alloys.Due to the special use environment,aviation aluminum products have good quality and high value,and degraded use will cause huge waste.This paper automatically classifies aluminum alloys of aluminum 3xxx,7xxx and A356 into a research target.Based on laser induced breakdown spectroscopy technology,an automated classification detection experimental platform was built.Image recognition is used to locate the dynamic sample,which is accurately captured by laser induced breakdown spectroscopy(LIBS).For the single-pulse LIBS spectral signal,the multi-dimensional Gaussian probability density distribution discriminant function of three series of aluminum alloys is established.Completed high-efficiency,high-precision continuous classification detection of 2xxx,7xxx and A356 aluminum alloys.The experimental results show that the recognition time of the material in the 1.2 m·s-1 transmission process is 18 ms,the laser excitation control deviation is less than 20.83 ms,and the minimum size of the test sample is 25 mm.For the three series of aluminum alloy samples with a height difference of less than 3 mm,the average prediction classification accuracy of the multi-dimensional Gaussian probability density distribution method can reach 99.15%,and the average modeling time only takes 7 ms.Compared with the widely used support vector machine(SVM)classification method,the prediction accuracy is equivalent,and the modeling time is increased by order of magnitude.The generalization ability of the classification prediction is good and the modeling efficiency is high.This study validated the effectiveness of automated rapid classification and detection of aluminum alloys based on laser-induced breakdown spectroscopy.It provides a theoretical and technical basis for the establishment of a fully automated scrap metal sorting system.