Particle auto-statistics and measurement of the spherical powder for 3D printing based on deep learning

With the development of metal powder 3D printing technology, how to accurately extract the particle size and spheroidization rate information of powder particles from microscopic images has gained much more importance. In this paper, a particle auto-statistics and measurement system on microscopic imaging of the spherical powder was presented, based on one deep learning framework—Mask R-CNN. The proposed model can efficiently detect more than 1 000 particles in a microscopy image, even under the existence of many occlusion particles, and provide statistical results of particle size distribution, degree of sphericity and spheroidization ratio, simultaneously. Compared with traditional image segmentation method, the particle recognition accuracy was improved by 23.6%. Moreover, smaller particles that stuck on big particles can be recognized, according to the comparison in particle size distribution between proposed method and the laser diffraction technique.