Infrared and Laser Engineering, Volume. 50, Issue 10, 2021G004(2021)

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

Wang Yichao1,2, Zhang Zheng1, Huang Haizhou1, and Lin Wenxiong1
Author Affiliations
  • 1Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou 350002, China
  • 2University of the Chinese Academy of Sciences, Beijing 100049, China
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    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.

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    0 Introduction

    Powder properties are of great importance in powder bed fusion(PBF), one of the popular metal additive manufacturing (also called 3D printing) methods currently[1-2]. Typically, spherical particles with proper size distribution support high flowability and density of the powder. A dense powder layer with uniform thickness can significantly improve the dimension accuracy during the melting process of PBF[3].

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    Yichao Wang, Zheng Zhang, Haizhou Huang, Wenxiong Lin. Particle auto-statistics and measurement of the spherical powder for 3D printing based on deep learning[J]. Infrared and Laser Engineering, 2021, 50(10): 2021G004

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    Paper Information

    Category: Image processing

    Received: Jun. 10, 2021

    Accepted: --

    Published Online: Dec. 7, 2021

    The Author Email:

    DOI:10.3788/IRLA2021G004

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