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Abstract
Leather manufacturing is a quality-sensitive process where early detection of defects and predictive quality control are crucial for minimizing waste, ensuring product consistency, and maximizing economic return. Traditional quality control methods in tanneries rely heavily on manual inspection, which is prone to subjectivity and inefficiency. Recent advances in Artificial Intelligence (AI), particularly in computer vision, fuzzy logic, and machine learning, offer new opportunities to automate and optimize defect detection, surface analysis, and predictive control. This paper presents a review of AI-based systems applied to leather processing before 2020 and proposes a conceptual framework for integrating predictive quality control using neural networks and real-time imaging
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