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

Yichao Wang1,2, Zheng Zhang1, Haizhou Huang1, and Wenxiong Lin1
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
  • show less
    References(29)

    [1] S Cooke, K Ahmadi, S Willerth, et al. Metal additive manufacturing:Technology, metallurgy and modelling. Journal of Manufacturing Processes, 57, 978-1003(2020).

    [2] [2] Qian M, Froes F H. Titanium Powder Metallurgy: Science, Technology Applications[M]. Oxfd: ButterwthHeinemann, 2015.

    [3] A Strondl, O Lyckfeldt, H K Brodin, et al. Characterization and control of powder properties for additive manufacturing. JOM, 67, 549-554(2015).

    [4] P Sun, Z Z Fang, Y Zhang, et al. Review of the methods for production of spherical Ti and Ti alloy powder. JOM, 69, 1853-1860(2017).

    [5] W-H Wei, L-Z Wang, T Chen, et al. Study on the flow properties of Ti-6Al-4V powders prepared by radio-frequency plasma spheroidization. Advanced Powder Technology, 28, 2431-2437(2017).

    [6] J A Slotwinski, E J Garboczi, P E Stutzman, et al. Characterization of metal powders used for additive manufacturing. Journal of Research of the National Institute of Standards and Technology, 119, 460(2014).

    [7] A B Spierings, M Voegtlin, T U Bauer, et al. Powder flowability characterisation methodology for powder-bed-based metal additive manufacturing. Progress in Additive Manufacturing, 1, 9-20(2016).

    [8] [8] ISO 133221. Particle size analysisImage analysis methodsPart 1: Static image analysis methods[S]. Switzerl: [s.n.], 2014.

    [9] [9] Scientific T. Thermo Scientific ParticleMetric [OL]. [20210321].https:www.thermofisher.cndercatalogproductPARTICLEMETRICSID=srchsrpPARTICLEMETRIC#PARTICLEMETRICSID=srchsrpPARTICLEMETRIC.

    [10] [10] ISO 14488. Particulate materialsSampling sample splitting f the determination of particulate properties[S]. Switzerl: [s.n.], 2007.

    [11] Z Chong, M Chaoyang, W Zicheng, et al. Spheroidization of TC4 (Ti6Al4V) alloy powders by radio frequency plasma processing. Rare Metal Materials and Engineering, 48, 446-451(2019).

    [12] A B Oktay, A Gurses. Automatic detection, localization and segmentation of nano-particles with deep learning in microscopy images. Micron, 120, 113-119(2019).

    [13] C T Rueden, J Schindelin, M C Hiner, et al. ImageJ2:ImageJ for the next generation of scientific image data. BMC bioinformatics, 18, 1-26(2017).

    [14] T Grant, A Rohou, N Grigorieff. cisTEM, user-friendly software for single-particle image processing. eLife, 7, e35383(2018).

    [15] [15] He K, Gkioxari G, Dollár P, et al. Mask rcnn[C]Proceedings of the IEEE International Conference on Computer Vision, 2017.

    [16] Y Yu, K Zhang, L Yang, et al. Fruit detection for strawberry harvesting robot in non-structural environment based on Mask-RCNN. Computers and Electronics in Agriculture, 163, 104846(2019).

    [17] M Frei, F E Kruis. Image-based size analysis of agglomerated and partially sintered particles via convolutional neural networks. Powder Technology, 360, 324-336(2020).

    [18] Y Wu, M Lin, S Rohani. Particle characterization with on-line imaging and neural network image analysis. Chemical Engineering Research and Design, 157, 114-125(2020).

    [19] H Huang, J Luo, E Tutumluer, et al. Automated segmentation and morphological analyses of stockpile aggregate images using deep convolutional neural networks. Transportation Research Record, 2674, 285-298(2020).

    [20] J Ruiz-Santaquiteria, G Bueno, O Deniz, et al. Semantic versus instance segmentation in microscopic algae detection. Engineering Applications of Artificial Intelligence, 87, 103271(2020).

    [21] B C Russell, A Torralba, K P Murphy, et al. LabelMe:a database and web-based tool for image annotation. International Journal of Computer Vision, 77, 157-173(2008).

    [22] S Ren, K He, R Girshick, et al. Faster r-cnn:Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems, 28, 91-99(2015).

    [23] [23] He K, Zhang X, Ren S, et al. Deep residual learning f image recognition[C]. Proceedings of the IEEE Conference on Computer Vision Pattern Recognition, 2016.

    [24] A Canziani, A Paszke, E Culurciello. An analysis of deep neural network models for practical applications. arXiv preprint, arXiv:1605.07678(2016).

    [25] [25] Lin T Y, Maire M, Belongie S, et al. Microsoft coco: Common objects in context[C]European Conference on Computer Vision, 2014.

    [26] P Vangla, N Roy, M L Gali. Image based shape characterization of granular materials and its effect on kinematics of particle motion. Granular Matter, 20, 1-19(2018).

    [27] [27] De Bo C, De Bo C. A Practical Guide to Splines[M]. New Yk: SpringerVerlag, 1978.

    [28] M L Hentschel, N W Page. Selection of descriptors for particle shape characterization. Particle & Particle Systems Characterization, 20, 25-38(2003).

    [29] S Özbilen. Satellite formation mechanism in gas atomised powders. Powder Metallurgy, 42, 70-78(1999).

    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)BibTexPlain Text
    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