0-DMF: ADecision-Support Framework for Zero Defects Manufacturing

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2024

Autores

Coutinho, B (Autor) Outra; Pereira, E (Autor) FEUP; Gil Manuel Gonçalves (Autor) FEUP

Ata de Conferência Internacional

Título: Proceedings of the International Conference on Informatics in Control, Automation and Robotics

Páginas: 253-260

21st International Conference on Informatics in Control, Automation and Robotics, ICINCO 2024

Porto, 18 November 2024 through 20 November 2024

Indexação

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-018-GMQ

DOI: 10.5220/0012924400003822

Abstract (EN): Manufacturing companies are increasingly focused on minimising defects and optimising resource consump tion to meet customer demands and sustainability goals. Zero Defect Manufacturing (ZDM) is a widely adopted strategy to systematically reduce defects. However, research on proactive defect-reducing measures remains limited compared to traditional defect detection approaches. This work presents the 0-DMF decision support framework, which employs data-driven techniques for defect reduction through (1) defect prediction, (2) process parameter adjustments to prevent predicted defects, and (3) clarifying prediction factors, provid ing contextual information about the manufacturing process. For defect prediction, Machine Learning (ML) algorithms, including XGBoost, CatBoost, and Random Forest, were evaluated. For process parameter ad justments, optimisation algorithms such as Powell and Dual Annealing were implemented. To enhance trans parency, Explainable Artificial Intelligence (XAI) methods, including SHAP and LIME, were incorporated. Tailored for the melamine-surfaced panels process, the methods showed promising results. The defect predic tion model achieved a recall value of 0.97. The optimisation method reduced the average defect probability by 28 percentage points. The integration of XAI enhanced the framework¿s reliability. Combined into a unified tool, all tasks delivered fast results, meeting industrial time constraints. These outcomes signify advancements in predictive quality through data-driven approaches for defect prediction and prevention. © 2024 by SCITEPRESS¿ Science and Technology Publications, Lda.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 7