## 1 Introduction  
Every year, tens of millions of cars in the world reach the end of their service life, which produces tens of millions of tons of automotive scrap; in recent years, countries have been devoted to research on automotive recycling [1, 2]. In China, there will be approximately 15 million end-of-life vehicles in 2022, which will generate a large amount of metal scrap. As an essential source of raw materials in the recycling economy, the metal of end-of-life vehicles has the characteristics of large stock, high resource value, and high plasticity. There are steel, nonferrous metals, precious metals, and other renewable materials in automotive scrap. The separation and sorting of different metals in the scrap are the focus of research at home and abroad. One study [3] used deep learning and transfer learning to identify and separate small samples of nonferrous metals, and the improved YOLOv3 algorithm model was used to identify and classify nonferrous metals in copper and aluminum scrap, with accuracy rates of 91.4% and 95.3%, respectively; another study [4] combined a neural network with automatic sorting of metal scraps, which reduced the data analysis time; to improve the sorting efficiency, researchers proposed scrap aluminum robot sorting technology and evaluated its economics [5]. Most of the current studies on the classification of metal scrap remain on discrimination between different metals and classifying them by their color, shape, and other characteristics, and there are fewer studies on the classification of similar metals. Aluminum is an important nonferrous metal, and the recycling classification of aluminum scrap is one of the research focuses [6]. In this paper, systematic studies were conducted for different grades of aluminum pieces in order to identify higher grade aluminum scrap chips, of which the main components are cast aluminum alloy, alloy aluminum, pure aluminum, etc., the first two of which have numerous grades, and it is still difficult to classify them by grade. In the pretreatment of aluminum scrap, large recycled aluminum plants generally only remove mixed soil and other impurities by sieving, and then directly melt the scrap in the furnace; while for small recycled aluminum plants, this kind of scrap aluminum should be manually divided into foundry aluminum alloy, alloy aluminum, and pure aluminum, and then utilized separately. One study [7] combined deep learning and computer vision for aluminum scrap separation. Some researchers also stratified and separated non-recyclable aluminum scrap using the hydrothermal method [8]. The purpose of advanced aluminum scrap pretreatment technology is to realize the mechanization and automation of aluminum scrap sorting, maximize the removal of metallic and non-metallic impurities, and effectively classify aluminum scrap by alloy composition. The ideal sorting method is to divide aluminum scrap into several categories based on main alloy composition, such as aluminum alloy, aluminum-magnesium alloy, aluminum-copper alloy, aluminum-zinc alloy, aluminum-silicon alloy, etc. This can alleviate the technological difficulty of removing impurities and adjusting the composition in the melting process, and can make comprehensive use of the alloy components in the scrap aluminum, especially scrap aluminum with high zinc, copper, and magnesium content, which need to be stored separately and can be used as intermediate alloy raw materials for melting aluminum alloys.