Inclusion Classification by Computer Vision and Machine Learning

摘要

This paper describes the use of computer vision and machine-learning methods to classify non-metallic inclusions in steel based on back-scattered electron (BSE) scanning electron microscope (SEM) images obtained during automated inclusion analysis. The use of automated inclusion analysis has produced major contributions to both control of inclusions during steel processing and a mechanistic understanding of inclusion evolution.