Enhanced Classification of Refractory Coatings in Foundries: A VPCA-Based Machine Learning Approach

Abstract

This paper introduces a thorough approach for classifying refractory coatings used on chemically bonded sand according to their thickness, which is essential for monitoring mold and core coatings in foundries. The method combines feature extraction through vectorized principal component analysis (VPCA) with classification modeling using a machine learning algorithm. The study examines five different scenarios, which involve the utilization of raw axial, radial, and temperature data, as well as the use of scalar properties. Additionally, the study involves extracting features from the first two approaches and training on the complete dataset. An assessment of performance is carried out, showcasing the strong ability to classify accurately across all levels of coating thickness. In addition, Hotelling's T-squared statistics are used to identify changes in the process, offering valuable information about the structure and distinctiveness of the data classes. This study demonstrates the efficacy of feature extraction methods and machine learning algorithms in accurately categorizing coating thicknesses, providing practical solutions for applications in the foundry industry. This systematic methodology not only improves the comprehensibility and effectiveness of classification models but also offers vital understanding into process monitoring and identification of abnormalities within intricate datasets.