Classification of Non-ferrous 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 non-ferrous metals however, is not straightforward. Common methods range from laborious hand-sorting to expensive and environmentally deleterious wet processes. The goal is to move towards dry processes, such as induction sensors and vision systems, which can identify and sort non-ferrous 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 non-ferrous fraction - copper, aluminium 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 MIS only. 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.