Classification of Nonferrous Metals Using Magnetic Induction Spectroscopy

Recycling automotive, electronic, and other end-of-life waste liberates large quantities of metals, which can be returned to the supply chain. Sorting the nonferrous metals, however, is not straightforward. Common methods range from laborious hand-sorting to expensive and environmentally deleterious wet processes. The goal is to move toward dry processes, such as induction sensors and vision systems, which can identify and sort nonferrous scrap efficiently and economically. In this paper, we present a new classification method using magnetic induction spectroscopy (MIS) to sort three high-value metals that make up the majority of the nonferrous fraction—copper, aluminum, and brass. Two approaches are investigated: the first uses MIS with a set of geometric features returned by a vision system, where metal fragments are matched to known test pieces from a training set. The second approach uses MISonly. A surprisingly effective classifier can be constructed by combining the MIS frequency components in a manner determined by how eddy currents circulate in the metal fragment. An average precision and recall (purity and recovery rate) of around 92% was shown. This has significant industrial relevance, as the MIS-only classifier is simple, scalable, and straightforward to implement on existing commercial sorting lines.