Multi-Image Fusion-Based Defect Detection Method for Real-Time Monitoring of Recoating in Ceramic Additive Manufacturing.

Abstract

Vat photopolymerization is characterized by its high precision and efficiency, making it a highly promising technique in ceramic additive manufacturing. However, the process faces a significant challenge in the form of recoating defects, necessitating real-time monitoring to maintain process stability. This article presents a defect detection method that leverages multi-image fusion and deep learning for identifying recoating defects in ceramic additive manufacturing. In the image fusion process, multiple single-channel recoating images captured by monitoring camera positioned near the photopolymerization equipment are merged with curing area mask image to create a three-channel color image. The recoating images suffer from perspective distortion due to their side view. To facilitate fusion with the curing area image, image rectification technique is applied to correct the perspective distortion, transforming the side view recoating images into a top-down view. Subsequently, the fused images are processed using a channel-wise YOLO (You Only Look Once, CW-YOLO) method to extract features, enabling the distinction of various types of defects. When compared with other deep learning models, CW-YOLO achieves higher detection accuracy while maintaining a detection rate of 103.58fps, meeting the requirements for real-time detection. Furthermore, the paper introduces the F1 score as a comprehensive evaluation metric, capturing both detection accuracy and recall rate. The results show that the F1 score is enhanced by approximately 10% after image fusion, demonstrating that the proposed method can significantly improve defect detection, particularly in cases involving difficult-to-distinguish defects like material shortages and scratches.