Research Information System
METALS, vol.15, no.3, 2025 (SCI-Expanded, Scopus)
Modern steel manufacturing processes demand rigorous quality control to rapidly and accurately detect and classify defects in steel plates. In this work, we propose an enhanced three-stage cluster-then-classify method (ETSCCM) that merges clustering-based data partitioning with strategic feature subset selection and refined hyperparameter tuning. Initially, the appropriate number of clusters is determined by combining K-means with hierarchical clustering, ensuring a more precise segmentation of the Steel Plates Fault dataset. Concurrently, various correlated feature subsets are assessed to identify those that maximize classification performance. The best-performing scenario is then used in conjunction with the most effective classifier, identified through comparative analyses involving widely adopted algorithms. Experimental outcomes on real-world fault data, as well as additional publicly available datasets, indicate that our approach can achieve a significant increase in prediction accuracy compared to conventional methods. This study introduces a new method by jointly refining cluster assignments and classification parameters through scenario-based feature subsets, going beyond single-stage methods in enhancing detection accuracy. Through this multi-stage process, pivotal data relationships are uncovered, resulting in a robust, adaptable framework that advances industrial fault diagnosis.