Authors:
Beatriz Coutinho
1
;
Eliseu Pereira
1
;
2
and
Gil Gonçalves
1
;
2
Affiliations:
1
Faculty of Engineering of the University of Porto, Portugal
;
2
SYSTEC-ARISE, Faculty of Engineering of the University of Porto, Portugal
Keyword(s):
Zero Defect Manufacturing, Decision-Support Systems, Data-Driven Manufacturing, Defect Prediction.
Abstract:
Manufacturing companies are increasingly focused on minimising defects and optimising resource consumption 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, providing contextual information about the manufacturing process. For defect prediction, Machine Learning (ML) algorithms, including XGBoost, CatBoost, and Random Forest, were evaluated. For process parameter adjustments, optimisation algorithms such as Powell and Dual Annealing were implemented. To enhance transparency, Explainable Artificial Intelligence (XAI) method
s, including SHAP and LIME, were incorporated. Tailored for the melamine-surfaced panels process, the methods showed promising results. The defect prediction 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.
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