Quality prediction method for aluminum alloy ingot based on XGBoost

The quality of aluminum alloy ingots is very important to the performance of products. But the casting process is complicated. Chemical composition and other factors also have a great impact on the quality of the ingots. In this paper, based on the actual production of an aluminum casting plant in China, we propose a data-driven ingot quality prediction method using XGBoost. This method includes four parts: data collection, database construction, data cleaning and relation model establishment. Among them, data collection and database construction are based on process knowledge. Make sure the data is realistic. At the stage of data cleaning, the features which have great influence on the quality of ingot casting but are neglected in production are found. In dealing with the missing value, the fitting method is adopted to solve the serious problem of missing industrial data. The ensemble learning method is used to build the relational model. It can completely reflect the aluminum alloy casting process and make accurate prediction of ingot quality quantitatively. As can be seen from a large number of comparative experiment, the XGBoost model works very well and gives the key factors that affect the quality of ingots.