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

$\begin{array}{l} N_i^0(t) = \left\{ \begin{gathered} 0,{\rm{ }}{t_i} \leqslant t \leqslant {t_{i + 1}} \\ 1,{\rm{ }}\rm otherwise \\ \end{gathered} \right. \\ N_i^k(t) = \dfrac{{t - {t_i}}}{{{t_{i + k}} - {t_i}}}N_i^{k - 1}(t) + \dfrac{{{t_{i + k + 1}} - t}}{{{t_{i + k + 1}} - {t_{i + 1}}}}N_{i + 1}^{k - 1}(t) \\ \end{array} $

(2)

View in Article

$DS = \frac{{2\sqrt {\pi A} }}{P}$

(3)

View in Article

$SR = \left(1 - \dfrac{{{N_{non}}}}{{{N_{all}}}}\right) \times 100 {\text{%}} $

(4)

View in Article

Tools

Get Citation

Copy Citation Text

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

Download Citation

EndNote(RIS)BibTex Plain Text

Set citation alerts for article

Save article for my favorites

Paper Information

Category: Image processing

Received: Jun. 10, 2021

Accepted: --

Published Online: Dec. 7, 2021

The Author Email:

DOI:10.3788/IRLA2021G004

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology