Comparative Analysis of Explainable AI Methods for Manufacturing Defect Prediction: A Mathematical Perspective
The 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 a...
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universidad Europea (UEM) |
| Repositorio: | ABACUS. Repositorio de Producción Científica |
| Idioma: | inglés |
| OAI Identifier: | oai:abacus.universidadeuropea.com:11268/16643 |
| Acceso en línea: | https://hdl.handle.net/11268/16643 |
| Access Level: | acceso abierto |
| Palabra clave: | Informática y desarrollo Inteligencia artificial Estadística Goal 8: Promote inclusive and sustainable economic growth, employment and decent work for all Goal 9: Build resilient infrastructure, promote sustainable industrialization and foster innovation |
| Sumario: | The 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. |
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