Scrap Metal Classification Using Magnetic Induction Spectroscopy and Machine Vision
Scrap Metal Classification Using Magnetic Induction Spectroscopy and Machine Vision
复制标题
DOI：
10.1109/tim.2023.3284930
复制
发表时间：
2023-01-01
影响因子：
5.6
通讯作者：
Peyton, Anthony J.
中科院分区：
文献类型：
--
作者：
Williams, Kane C.;O'Toole, Michael D.;Peyton, Anthony J.
回收和再循环材料以建立循环经济的需要越来越成为全球的当务之急。特别是有色金属是高度可回收的，可以使用涡流分离等工艺进行提取。然而，将它们进一步分成基于金属或合金的可回收组仍然是一个挑战。最近，我们提出了一种新的技术来区分有色金属：磁感应光谱（MIS）测量金属碎片如何在不同频率上散射激发磁场。MIS与导电性有关，可用于根据该性质对碎片进行分类。在这篇文章中，我们首次展示了使用MIS与机器学习对从商业废物流中提取的有色金属废料进行分类。探索了两种方法：1）在3至90 kHz的带宽上的MIS和2）MIS与金属样品的物理颜色的组合。我们表明，MIS单独可以获得纯度和回收率>80%的大多数金属族和废物流，上升到>93%的不锈钢。例外情况是Zorba废物流，其中样品组中铝合金的混合导致电导率对比度差。颜色的引入大大改善了这种情况下的结果，将纯度和回收率提高了20%-35%。在测试的机器学习模型中，我们发现随机森林（RF），额外树和支持向量机（SVM）算法始终达到最高性能。
The need to recover and recycle material toward building a circular economy is increasingly a global imperative. Nonferrous metals in particular are highly recyclable and can be extracted using processes such as eddy current separation. However, their further separation into recyclable groups based on metal or alloy continues to pose a challenge. Recently, we proposed a new technique to discriminate between nonferrous metals: magnetic induction spectroscopy (MIS) measures how a metal fragment scatters an excitation magnetic field over different frequencies. MIS is related to conductivity, which can be used to classify the fragment according to this property. In this article, we demonstrate for the first time the use of MIS with machine learning to classify nonferrous scrap metals drawn from commercial waste streams. Two approaches are explored: 1) MIS over a bandwidth from 3 to 90 kHz and 2) the combination of MIS with the physical color of the metal samples. We show that MIS alone can obtain purity and recovery rates >80% for most metal groups and waste streams, rising to >93% for stainless steel. The exception was the Zorba waste stream where the mix of aluminum alloys within the sample set led to poor conductivity contrasts. The introduction of color substantially improved results in this case, increasing purity and recovery rates by 20%-35% points. Of the machine-learning models tested, we found that random forest (RF), extra trees, and support vector machine (SVM) algorithms consistently achieved the highest performance.