Online defect detection method and system based on similarity of the temperature field in the melt pool
Online defect detection method and system based on similarity of the temperature field in the melt pool
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DOI：
10.1016/j.addma.2022.102760
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发表时间：
2022-03
影响因子：
11
通讯作者：
W. Feng;Zhuangzhuang Mao;Yang Yang-Yang;Heng Ma;Kai Zhao;Chaoqi Qi;Ce Hao;Zhanwei Liu;H. Xie-H
中科院分区：
文献类型：
--
作者：
W. Feng;Zhuangzhuang Mao;Yang Yang-Yang;Heng Ma;Kai Zhao;Chaoqi Qi;Ce Hao;Zhanwei Liu;H. Xie-H
加法制造(AM)是一种重要的生产趋势。同时，缺乏在线的缺陷检测技术是制约AM进一步发展的关键问题为了实现对缺陷的有效在线监测，提出了一种激光工程净成形(透镜)印刷过程熔池缺陷在线检测方法和系统。将单摄像头熔池温度在线测量系统集成到镜头打印设备中。实现了印刷过程的同步监测，并在线获得了高分辨率的熔池温度分布和演变图像。提出了一种缺陷检测方法--温度分布相似性检测法。TDSD方法主要是基于熔池内温度场的全局分布或前沿加热区的相似性。根据熔池温度场中表面缺陷引起的异常特征，可以有效地在线检测缺陷。提出了外部边界对齐、内部相关检测的相似性检测策略，建立了包含人工缺陷影响下熔池中所有点的时间和空间特征的印刷质量数据库。通过优化检测覆盖范围，可以显著提高缺陷识别的灵敏度，并可以更准确地识别衬底缺陷和飞溅等异常特征。实验结果表明，在水平衬底的情况下，可以检测到直径大于25µm的表面气孔缺陷，检测准确率超过90%，相对定位误差约为6.4%。该方法对在线检测表面缺陷具有实际应用前景，对AM过程中的异常反馈和质量控制具有重要意义。
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 µm 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.