Article: Building fault-specific decision trees for quality control in steel plate manufacturing through machine learning approach Journal: International Journal of Mechatronics and Manufacturing Systems (IJMMS) 2025 Vol.18 No.2 pp.103 - 120 Abstract: The quality control of steel plates is vital in manufacturing, where identifying faults can enhance productivity and reduce costs. This study uses the publicly available Steel Plates Faults dataset from the UCI Machine Learning Repository to propose a fault-specific decision tree framework for fault classification and analysis. The dataset includes multiple fault types, such as Pastry, Z_Scratch, K_Scatch, Stains, Dirtiness, and Bumps, along with features like plate thickness, luminosity indices, and perimeter measurements. Interpretable decision tree models were trained for each fault type, enabling the extraction of actionable rules that highlight feature thresholds contributing to classification. Analysis of feature importance revealed critical factors influencing predictions, offering insights for proactive measures in steel production. The framework bridges the gap between automated fault detection and decision-making, demonstrating high accuracy with interpretability. These results underscore the value of explainable AI for industrial applications and highlight the importance of open datasets in advancing quality control research. Inderscience Publishers - linking academia, business and industry through research

Title: Building fault-specific decision trees for quality control in steel plate manufacturing through machine learning approach

Authors: Rajendra Bhange; A.S. Chatpalliwar

Addresses: Department of Mechanical Engineering, Ramdeobaba University, Ramdeo Tekdi, Katol Road, Nagpur, 440 013, Maharashtra, India ' Department of Mechanical Engineering, Ramdeobaba University, Ramdeo Tekdi, Katol Road, Nagpur, 440 013, Maharashtra, India

Abstract: The quality control of steel plates is vital in manufacturing, where identifying faults can enhance productivity and reduce costs. This study uses the publicly available Steel Plates Faults dataset from the UCI Machine Learning Repository to propose a fault-specific decision tree framework for fault classification and analysis. The dataset includes multiple fault types, such as Pastry, Z_Scratch, K_Scatch, Stains, Dirtiness, and Bumps, along with features like plate thickness, luminosity indices, and perimeter measurements. Interpretable decision tree models were trained for each fault type, enabling the extraction of actionable rules that highlight feature thresholds contributing to classification. Analysis of feature importance revealed critical factors influencing predictions, offering insights for proactive measures in steel production. The framework bridges the gap between automated fault detection and decision-making, demonstrating high accuracy with interpretability. These results underscore the value of explainable AI for industrial applications and highlight the importance of open datasets in advancing quality control research.

Keywords: fault detection; steel plate defects classification; decision tree framework; surface defects; machine learning; industrial applications.

DOI: 10.1504/IJMMS.2025.150093

International Journal of Mechatronics and Manufacturing Systems, 2025 Vol.18 No.2, pp.103 - 120

Received: 15 May 2025
Accepted: 14 Jul 2025
Published online: 28 Nov 2025 *

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Title: Building fault-specific decision trees for quality control in steel plate manufacturing through machine learning approach

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