Scrap Metal Classification Using Magnetic Induction Spectroscopy and Machine Vision

Abstract

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.