Advanced Defect Part Identification System: A Comprehensive Multi-Modal Approach Integrating Computer Vision, Deep Learning, and IoT for Industry 4.0 Quality Control

A unified five-point defect detection framework including (1) advanced machine vision methodologies, (2) multi-modal sensing, (3) expert systems, (4) decision support systems and (5) edge-computing infrastructure supports both Zero Defect Manufacturing (ZDM) and adaptive quality assurance in smart factories. Our approach offers a robust five-point approach that resolves many of the current limitations in machine vision defect detection. First, the use of advanced vision models where transfer learning is employed has demonstrated an overall accuracy of 96.8% across all defect types. Second, a multi-modal sensor fusion approach delivers an 8-12% increase in robustness over a single-modal approach to defect detection. Third, a hybrid expert system takes the raw predictions produced by AI and creates actionable intelligence for the manufacturing environment. Fourth, an edge computing-enabled platform provides the fastest possible real-time inference at 48 milliseconds while allowing for use cloud-based infrastructure to continually improve the models. Finally, Explainable AI offers manufacturers the ability to remain compliant with safety-critical requirements. Directions for future research suggest developing data-efficient learning methods, applying Explainable AI, performing Digital Twin integration and creating standardized benchmarks for cross-domain defect detection.