Intelligent Casting Quality Inspection Method Integrating Anomaly Detection and Semantic Segmentation

Wind power generation plays an important role in renewable energy, and the core casting components have extremely high requirements for precision and quality. In actual practice, we found that an insufficient workforce limits traditional manual inspection methods and often creates difficulty in unifying quality judgment standards. Customized optical path design is often required, especially when conducting internal and external defect inspections, which increases overall operational complexity and reduces inspection efficiency. We developed an automated optical inspection (AOI) system to address these challenges. The system integrates a semantic segmentation neural network to handle external surface detection and an anomaly detection model to detect internal defects. In terms of internal defect detection, the GC-AD-Local model we tested achieved 100% accuracy on experimental images, and the results were relatively stable. In the external detection part, we compared five different semantic segmentation models and found that MobileNetV2 performed the best in terms of average accuracy (65.8%). It was incredibly stable when dealing with surface defects with significant shape variations, and the prediction results were more consistent, making it more suitable for introduction into actual production line applications. Overall, this AOI system boosts inspection efficiency and quality consistency, reduces reliance on manual experience, and is of great assistance in quality control and process intelligence for wind power castings. We look forward to further expanding the amount of data and improving the generalization capabilities of the model in the future, making the system more complete and suitable for practical applications.