Grain size determination and classification using adaptive image segmentation with shape information for milling quality evaluation
1
Silesian University of Technology
Submission date: 2017-07-31
Final revision date: 2017-09-18
Acceptance date: 2017-12-04
Online publication date: 2017-12-18
Publication date: 2018-03-12
Corresponding author
Diagnostyka 2018;19(1):41-48
KEYWORDS
TOPICS
ABSTRACT
In this paper, authors described methods of material granularity evaluation and a novel method for grain size determination with inline electromagnetic mill device diagnostics. The milling process quality evaluation can be carried out with vibration measurements, analysis of the milling material images or well-known screening machines. The method proposed in this paper is developed to the online examination of the milled product during the milling process using real-time digital images. In this paper, authors concentrated their work on copper ore milling process. Determination of the total number of the grain, the size of each grain, also the classification of the grains are the main goal of the developed method. In the proposed method the vision camera with lightning mounted at two assumed angles has been used. The detection of the grains has been based on an adaptive segmentation algorithm, improved with distance transform to enhance grains detection. The information about particles shape and context is used to optimize the grain classification process in the next step. The final classification is based on the rule-based method with defined particle shape and size parameters.
REFERENCES (22)
1.
Kurzydlo M, Pawelczyk M. Vibration measurement for copper ore milling and classification process optimization. Vibroeng. Procedia 2015; 6: 18–23.
2.
Atmaca A, Kanoglu M. Reducing energy consumption of a raw mill in cement industry. Energy 2012; 42: 261–269. https://doi.org/10.1016/j.ener....
3.
Ogonowski S, Ogonowski Z, Pawelczyk M. Model of the air stream ratio for an electromagnetic mill control system. 21st International Conference on Methods and Models in Automation and Robotics, MMAR 2016. https://doi.org/10.1109/MMAR.2...
4.
Makinde OA, Ramatsetse BI, Mpofu K. Review of vibrating screen development trends: Linking the past and the future in mining machinery industries. International Journal of Mineral Processing 2015; 145: 17-22. https://doi/org/10.1016/j.minp....
5.
Ramatsetse B, Matsebe O, Mpofu K, Desai DA. Conceptual design framework for developing a reconfigurable vibrating screen for small and medium mining enter-prises. SAIIE25 proceedings 2013; 595: 1-10.
6.
Krauze O, Pawelczyk M. Estimating parameters of loose material stream using vibration measurements. 17th International Carpathian Control Conference (ICCC) Proceedings 2016; 378 – 383. doi: 10.1109/CarpathianCC.2016.7501127.
7.
Agrawal V, Panigrahi BK, Subbarao PMV. Review of control and fault diagnosis methods applied to coal mills. Journal of Process Control 2015; 32: 138–153 https://doi/org/10.1016/j.jpro....
8.
Jardine AKS, Lin D, Banjevic D. A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing 2006; 20: 1483–1510. doi: https://doi.org/10.1016/j.ymss....
9.
Buchczik D, Ilewicz W. Evaluation of calibration results using the least median of squares method in the case of linear multivariate models. 21st International Conference on Methods and Models in Automation and Robotics (MMAR) 2016; 800-805. https://doi.org/10.1109/MMAR.2....
10.
Wiora J, Wrona S, Pawelczyk M. Evaluation of measurement value and uncertainty of sound pressure level difference obtained by active device noise reduction. Measurement 2016; 96: 67–75. https://doi.org/10.1016/j.meas....
11.
Chung CH, Chang FJ. A refined automated grain sizing method for estimating river-bed grain size distribution of digital images. Journal of Hydrology 2013; 486: 224–233. https://doi.org/10.1016/j.jhyd....
12.
Asmussen P, Conrad O, Günther A, Kirsch M, Riller U. Semi-automatic segmentation of petrographic thin section images using a “seeded-region growing algorithm” with an application to characterize wheathered subarkose sandstone. Computers & Geosciences 2015; 83: 89–99. https://doi.org/10.1016/j.cage....
13.
Mehrabi A, Mehrshad N, Massinaei M. Machine vision based monitoring of an industrial flotation cell in an iron flotation plant. International Journal of Mineral Processing 2014; 133: 60–66. https://doi.org/10.1016/j.minp....
14.
Igathinathane C, Ulusoy U. Machine vision methods based particle size distribution of ball-and gyro-milled lignite and hard coal. Powder Technology 2016; 297: 71–80. https://doi.org/10.1016/j.powt....
15.
Heilbronner R. Automatic grain boundary detection and grain size analysis using polarization micrographs or orientation images. Journal of Structural Geology 2000; 22: 969–981. https://doi.org/10.1016/S0191-....
16.
Yesiloglu-Gultekin N, Keceli A, Sezer E, Can A, Gokceoglu C, Bayhan H. A computer program (tsecsoft) to determine mineral percentages using photographs obtained from thin sections. Computers & Geosciences 2012; 46: 310–316. https://doi.org/10.1016/j.cage....
17.
Obara B. A new algorithm using image colour system transformation for rock grain segmentation. Mineralogy and Petrology 2007; 91: 271–285. https://doi.org/10.1007/s00710....
18.
Choudhury KR, Meere PA, Mulchrone KF. Automated grain boundary detection by CASRG. Journal of Structural Geology 2006; 28: 363–375. https://doi.org/10.1016/j.jsg.....
19.
Ooi CH, Isa NAM. Adaptive contrast enhancement methods with brightness preserving IEEE Trans. Consum. Electron 2010; 56 (4) 2543-2551. https://doi.org/10.1109/TCE.20....
20.
Singh K, Kapoor R. Image enhancement using exposure based sub image histogram equalization Pattern Recogn. Lett. 2014; 36 10-14. https://doi.org/10.1016/j.patr....
21.
Lai YR, Tsai PC, Yao CY, Ruan SJ. Improved local histogram equalization with gradient-based weighting process for edge preservation. Multimedia Tools Appl. 2015; 1-29. https://doi.org/10.1007/s11042....
22.
Otsu N. A threshold selection method from gray-level histograms. IEEE Trans. Sys., Man., Cyber. 1979; 9 (1): 62–66.
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