Marín Díaz, Gabriel2025-12-222025-12-222025Marín Díaz, G. (2025). Comparative analysis of explainable ai methods for manufacturing defect prediction: A mathematical perspective. Mathematics, 13(15), 2436. https://doi.org/10.3390/math131524362227-7390https://hdl.handle.net/11268/16643The increasing complexity of manufacturing processes demands accurate defect prediction and interpretable insights into the causes of quality issues. This study proposes a methodology integrating machine learning, clustering, and Explainable Artificial Intelligence (XAI) to support defect analysis and quality control in industrial environments. Using a dataset based on empirical industrial distributions, we train an XGBoost model to classify high- and low-defect scenarios from multidimensional production and quality metrics.engAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/journal article10.3390/math13152436open accessInformática y desarrolloInteligencia artificialEstadísticaGoal 8: Promote inclusive and sustainable economic growth, employment and decent work for allGoal 9: Build resilient infrastructure, promote sustainable industrialization and foster innovation