Öztürk, A.Aydin, M.N.2025-02-152025-02-15202409798350365887https://doi.org/10.1109/UBMK63289.2024.10773408https://hdl.handle.net/20.500.12469/7188This study aims to develop a machine learning approach for defect evaluation in steel sheet production. The primary objective is to improve the defect decision process by integrating human knowledge with technical data. The paper uses a case study with data from 2020 and reviews the literature on steel surface defects, decision support systems, classification algorithms, and text mining. The study focuses on the detection and repair of defects, aiming to eliminate defects in production and optimize decisions related to defect detection and repair. The methodology of the study involves comparing different classification techniques and enhancing these results with text processing applications. The study concludes that the existence of text data improves the performance of the classification algorithms. © 2024 IEEE.eninfo:eu-repo/semantics/closedAccessData-DrivenDefect EvaluationKnowledge-BasedMachine LearningSteel Sheet ProductionText MiningConference Object46246710.1109/UBMK63289.2024.107734082-s2.0-85215510953N/AN/A