Real-Time 3D Surface Measurement in Additive Manufacturing Using Deep Learning

Layer-wise 3D surface morphology information is critical for the quality monitoring and control of additive manufacturing (AM) processes. However, most of the existing 3D scan technologies are either contact or time consuming, which are not capable of obtaining the 3D surface morphology data in a real-time manner during the process. Therefore, the objective of this study is to achieve real-time 3D surface data acquisition in AM, which is achieved by a supervised deep learning-based image analysis approach. The key idea of this proposed method is to capture the correlation between 2D image and 3D point cloud, and then quantify this relationship by using a deep learning algorithm, namely, convolutional neural network (CNN). To validate the effectiveness and efficiency of the proposed method, both simulation and real-world case studies were performed. The results demonstrate that this method has strong potential to be applied for real-time surface morphology measurement in AM, as well as other advanced manufacturing processes.

Technique Influence on Cast-Iron Surface Textures Induced by LST

The influence of various technological parameters on laser-textured cast-iron surface is presented in this paper. A kind of micro-pits array was analyzed using single-factor method, where Nd3+: YAG solid laser is applied to obtained regular surface textures, and 3D surface morphology profiler used to compare the surface topographies related to technology. Among of the set of parameters, the distance between working surface and cavity mirror, pumping current and number of pulses were concerned variables in the process of manufacturing of microstructures. The geometry structures for variables were characterized by the radius and depth of micro-pits. Moreover, surface roughness for different technological parameter were expressed with Ra, Rq and Rt. Abbott curve was adopted for valuing the abrasion resistance of given textured surfaces. One optical range of parameters is achieved for improving the level of laser texturing surface.