Quality Classification of Ceramic Sanitary Ware Products with Machine Learning Algorithms

Abstract

Ceramic products have a wide range of applications in industry due to their properties such as high temperature resistance, high hardness and corrosion resistance. On the other hand, many factors are involved in the production process of ceramic products and managing the quality control process can be challenging. Machine learning methods are widely used in classification and regression problems. In this study, we focus on the quality classification of products using data from a large-scale ceramic sanitary ware production facility. For this real-life problem, there are two classes in terms of quality depending on the data used. For the two-class problem, several machine learning methods were used to build models and discussions were made on the results obtained.