Surface extraction from micro-computed tomography data for additive manufacturing

Surface topography and surface finish are two significant factors for evaluating the quality and dimensional accuracy of additive manufacturing (AM) parts. In general, compared with traditional subtraction and forming manufacturing techniques, the nature of the rough surface and the geometric complexity make the surface of AM parts "another surface," and traditional methods such as coordinate machine measurement may not be applicable. Most research on surface extraction focuses on regular surfaces, such as flat, cylindrical, or spherical surfaces, but less has been done with irregular surfaces. This paper presented an approach for extracting irregular surfaces based on micro-computed tomography (μ-CT) data of AM parts. The extracted data sets were then compared with data sets obtained by a structured light system (SLS). Areal surface texture parameters, cloud comparison methods, and statistical methods were applied to evaluate the difference between the surface data obtained by the two systems. The results showed that the two surface data correlated well and confirmed the capability of the proposed method for surface extraction from irregular surfaces. This work will contribute to the further multi-sensor data fusion for metrology and provide valuable information for near-surface defects prediction with real-time in-situ metrology method in additive manufacturing.