Online defect detection method and system based on similarity of the temperature field in the melt pool

Additive manufacturing (AM) is an important production trend. Meanwhile, the lack of an online defect detection technology is a key problem that limits the further development of AM. To realize effective online monitoring of defects, an online melt pool defect detection method and system for the laser engineered net shaping (LENS) printing process is proposed in this study. The online temperature measurement system of the melt pool with a single camera was integrated into the LENS printing equipment. Synchronous monitoring of the printing process was realized, and images of the temperature distribution and evolution of the melt pool with high resolution were obtained online. A defect detection method, called temperature distribution similarity detection (TDSD) method, is proposed. The TDSD method is mainly based on the similarity of the global distribution or the front heating region of the temperature field in the melt pool. According to the abnormal characteristics caused by surface defects in the temperature field in the melt pool, defects can be detected efficiently online. "External boundary alignment, internal correlation detection" strategy is proposed for similarity detection, and the printing quality database, including temporal and spatial characteristics of all points in the melt pool under the influence of artificial defects can be established. By optimizing the detection coverage, the sensitivity of defect identification can be significantly improved, and abnormal characteristics, such as substrate defects and spatter can be identified more accurately. The experimental results indicated that surface pore defects with a diameter of over 25 mu m could be detected, the defect detection accuracies exceeded 90%, and the relative location error was approximately 6.4% in the case of a horizontal substrate. The developed method has practical application prospects for online surface defects detection and is of significance to the abnormal feedback and quality control in the AM process.