Research on the classification algorithm and operation parameters optimization of the system for separating non-ferrous metals from end-of-life vehicles based on machine vision

Abstract

In recent years, there has been a significant increase in the number of end-of-life vehicles (ELVs) in China. The traditional methods that rely primarily on manual sorting are hard to meet the requirements anymore. To solve the low intelligence and efficiency of separating non-ferrous metals, a machine vision based system was made to separate non-ferrous metals from ELVs, and the influences of the classification algorithm and operation parameters on the separation efficiency of the system were investigated. With the use of a principle component analysis/support vector machine (PCA-SVM) algorithm and decrease the number of features to three, the achieved recognition accuracy was 96.64%, and the computational speed was sufficiently high. Response surface methodology and FLUENT numerical simulation were employed to study the influence of operation parameters by evaluating the separation distance between copper and aluminum. The results indicated that the separation distance decreased in accordance with an increase in the speed of the conveyor belt (v), and increased in accordance with an increase in the air pressure of the nozzle (P) and separation height (H). With an increase in the angle of nozzle (α), there was a decrease in the separation distance after an initial increase, and the maximum value was reached at a nozzle angle 40°. The optimal operation parameters in this study were v = 1.4 m/s, P = 0.6 MPa, H = 0.6 m, α = 40°. The separation accuracy and purity of the system were greater than 85% using the proposed optimal classification algorithm and abovementioned operation parameters.