An Automated Particle Size Analysis Method for SEM Images of Powder Coating Particles
Source abstract
Abstract
The particle size of powder coatings is an important factor affecting flow and fluidization performance, which in turn determines the quality of the coating. Powder coating particles, such as boron nitride, titanium dioxide, barium titanate, niobium oxide, and tungsten oxide, can be seen in SEM images as circular or polygonal shapes, with irregular edges and sizes, as well as aggregation and stacking between them. This paper introduces a novel method of automatic particle identification and size analysis applied in SEM images for the investigation of coating quality. Firstly, a fast gradient segmentation algorithm (Split–Merge) is utilized to automatically generate samples through determining the adhesive particles and particle groups. The samples are then manually checked and corrected to further segment the particles in order to form the dataset. Finally, the YOLOv5, a mature target detection algorithm, is used to train the labeled data in order to produce a multi-target detection recognition model. The model can be applied to identify more pictures of the same substance, and the particles’ long and short axes are estimated using the elliptical coverage of the particles within the identified image boundary box. Experiment results from four kinds of powders suggest that this method can provide automatic particle size analysis for industrial SEM particle images.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Mineral Processing and Grinding · Granular flow and fluidized beds · Cyclone Separators and Fluid Dynamics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Civil and Architecture Engineering, Changzhou Institute of Technology, 666 Liaohe Road, Changzhou 213032, China
School of Health Science and Engineering, University of Shanghai for Science and Technology, 516 Jungong Road, Shanghai 200093, China
Funding
National Natural Science Foundation of China
References (22)
Show source-provided references
1. Claase, M.B., Vercoulen, P., and Misev, T.A. (2013). Particulate Products: Tailoring Properties for Optimal Performance, Springer International Publishing.
2. Particle size analysis in pharmaceutics: Principles, methods and applications10.1007/s11095-006-9146-7 ↗
3. Influence of coke particle size on pore structural determination by optical microscopy10.1016/j.coal.2013.08.004 ↗
4. Optical measurement of nebulizer sprays: A quantitative comparison of diffraction, phase doppler interferometry, and time of flight techniques10.1016/S0021-8502(99)00030-0 ↗
5. Mechanical characterisation of agarose-based chromatography resins for biopharmaceutical manufacture10.1016/j.chroma.2017.11.038 ↗
6. An overview of powder granulometry on feedstock and part performance in the selective laser melting process
7. Particle Size Characterization Through Bed Permeability Tests
8. Wuddivira, M.N., Bramble, D.S.E., Ramlochan, A., Gouveia, G.A., Francis, R.C., and De Caires, S.A. (2023). Effects of sample mass and suspension salinity on particle size distribution in predominantly medium to heavy textured soils using the hydrometer method. J. Plant Nutr. Soil Sci., early view.10.1002/jpln.202300088 ↗
9. Method development for separation and analysis of tire and road wear particles from roadside soil samples10.1021/acs.est.2c03695 ↗
10. An improved estimation of size distribution from particle profile measurements10.1016/j.powtec.2006.05.008 ↗
11. Dynamic image based shape analysis of hard and lignite coal particles ground by laboratory ball and gyro mills10.1016/j.fuproc.2014.05.017 ↗
12. Particle size distribution modeling of milled coals by dynamic image analysis and mechanical sieving10.1016/j.fuproc.2015.11.007 ↗
13. Development of a mass model in estimating weight-wise particle size distribution using digital image processing10.1016/j.ijmst.2017.03.015 ↗
14. Ioffe, S., and Szegedy, C. (2015, January 6–11). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the International Conference on Machine Learning, PMLR, Lille, France.
15. He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27–30). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.90 ↗
16. Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012). Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst., 25.
17. Wang, C.Y., Liao HY, M., Wu, Y.H., Chen, P.Y., Hsieh, J.W., and Yeh, I.H. (2020, January 14–19). CSPNet: A new backbone that can enhance learning capability of CNN. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA.10.1109/CVPRW50498.2020.00203 ↗
18. Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21–26). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.243 ↗
19. Spatial pyramid pooling in deep convolutional networks for visual recognition10.1109/TPAMI.2015.2389824 ↗
20. Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23–28). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.10.1109/CVPR.2014.81 ↗
21. Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27–30). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.91 ↗
22. Redmon, J., and Farhadi, A. (2017, January 21–26). YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.690 ↗