Laser ultrasonic imaging for defect detection on metal additive manufacturing components with rough surfaces
Jun Zhang, Jinfeng Wu, Xin Zhao, Shuxian Yuan, Guanbing Ma, Jiaqi Li, Ting Dai, Huaidong Chen, Bing Yang, Hui Ding
Abstract
Defects or discontinuities are inevitable during the melting and consolidation process of metal additive manufacturing. Online inspection of microdefects during the processing of layer-by-layer fusion is urgently needed for quality control. In this study, the laser ultrasonic C-scan imaging system is established to detect the surface defects of selective laser melting (SLM) samples that have a different surface roughness. An autosizing method based on the maximum correlation coefficient and lag time is proposed to accurately measure the defect length. The influences of the surface roughness on the laser ultrasound signal-to-noise ratio distribution and defect sizing accuracy are also studied. The results indicate that the proposed system can detect notches with a depth of 50 µm and holes with a diameter of 50 µm, comparable in size to raw powder particles. The average error for the length measurement can reach 1.5% if the notch is larger than 2 mm. Meanwhile, the sizing error of a 1 mm length notch is about 9%. In addition, there is no need to remove the rough surface of the as-built SLM samples during the detection process. Hence, we propose that the laser ultrasonic imaging system is a potential method for online inspection of metal additive manufacturing.