Study on Ceramic Sintering Process Based on Random Forest

Abstract

While the manufacturing process of ceramics is complex and each link is important to the ceramic, we focus on the sintering process of ceramics and construct models and algorithms to investigate the temperature of the ceramic sintering process in order to improve the yield of the ceramic sinter. We make full use of the ceramic sintering process node temperature and ceramic product problem data, adopt factor analysis for data dimensionality reduction, and use principal component analysis to extract factors to improve efficiency for subsequent optimisation. The random forest algorithm of integrated machine learning is used to investigate the effect of different nodal temperature rates on the ceramic product, and a decision tree is derived to identify the more influential temperature rates. These obtained important temperature factors affecting ceramic sintering can be used as a reference for the ceramic sintering process, thus achieving a study of the temperature of the ceramic sintering process.