Abstract
The development of rapid and non-destructive measurement methods for green anode quality assessment during production is important for the industry. It would improve process agility and robustness to face increasing variability in the raw materials. A machine vision sensor using a combination of image texture methods was used for extracting relevant paste textural features which were used as inputs of a Partial Least Squares regression model trained on the process variables. Previous work on laboratory anodes demonstrated sensitivity to pitch demand and particle size distribution. This sensor was then tested in a paste plant to assess its responsiveness to the industrial variability. The sensor was found sensitive to the variations in pitch during optimization experiments.
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References
E. J. Seger (1975), New method of measuring electrode resistance for quality control. In Light Metals 1975, p. 283–291.
E. Fawzi, P. Haldemann, A. Nispel and L. Logan (1996), The in-line inspection of carbon anodes for aluminium production. JOM 48(2): 24–27.
D. Audet et L. Parent (2009), System and method to forecast the electrical conductivity of anodes for aluminium production before baking. US Patent 7576534.
M. Gagnon, G. Léonard, A. Bernard, Y. El Ghaoui et S. Gourmaud (2016), Mirea: An on-line quality control equipment integration in an operational context. In Light Metals 2016, Springer, p. 979–984.
Y. Kocaefe, D. Kocaefe, D. Bhattacharyay, A. Benzaoui et J.-F. Desmeules (2020). Testing of SERMA technology on industrial anodes for quality control of aluminium production. In Light Metals 2020, Springer, p. 1189–1195.
D. P. Ziegler and J. Secasan (2016). Methods for determining green electrode electrical resistivity and methods for making electrodes. US Patent 9416458.
J. Lauzon-Gauthier et J. Secasan (2021). Development and applications of the four points probe (4PP) electrical resistivity measurements for anode process optimization. In Light Metals 2021, Springer, p. 951–956.
M. Ben Boubaker, D. Picard, C. Duchesne, J. Tessier, H. Alamdari et M. Fafard (2017). Non-destructive testing of baked anodes based on modal analysis and principal component analysis. In Light Metals 2017, Springer, p. 1289–1298.
M. Ben Boubaker, D. Picard, C. Duchesne, J. Tessier, H. Alamdari et M. Fafard (2018). Inspection of baked carbon anodes using a combination of multi-spectral acousto-ultrasonic techniques and principal component analysis. Ultrasonics 89:126–136.
J. Lauzon-Gauthier, C. Duchesne et J. Tessier (2014). Texture analysis of anode paste images. In Light Metals 2014, Springer, p. 1123–1128.
J. Lauzon-Gauthier, C. Duchesne et J. Tessier (2020). A Machine vision sensor for quality control of green anode paste material. JOM 72(11):287–295.
J. Lauzon-Gauthier (2015). Monitoring of a carbon anode paste manufacturing process using machine vision and latent variable methods, Ph.D. Thesis, Université Laval, Quebec city, Canada.
[13]S. G. Mallat (1989). A Theory for multiresolution decomposition: the wavelet representation. IEEE Transactions on pattern analysis and machine inteligence 11(17):674–693.
R. M. Haralick, K. Shanmugam et I. H. Dinstein (1973). Textural features for image classification. IEEE Transactions on systems, man, and cybernetics 3(16):610–621.
S. Wold, M. Sjöström et L. Eriksson (2001). PLS-regression: a basic tool of chemometrics. Chemometrics and intelligent laboratory systems 58(12):109–130.
Acknowledgements
The authors acknowledge financial support of the Natural Sciences and Engineering Research Council of Canada (NSERC) [Grant Numbers RGPIN261188-2013 and RDCPJ 417576-11], Fonds de Recherche du Québec—Nature et Technologies (FRQNT) through the Aluminium Research Centre—REGAL, and Alcoa Corporation. The participation of Deschambault’s paste plant team is also gratefully acknowledged.
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Lauzon-Gauthier, J., Duchesne, C., Tessier, J. (2022). Machine Vision Sensor Based on Image Texture Analysis Applied to Industrial Anode Paste. In: Eskin, D. (eds) Light Metals 2022. The Minerals, Metals & Materials Series. Springer, Cham. https://doi.org/10.1007/978-3-030-92529-1_110
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DOI: https://doi.org/10.1007/978-3-030-92529-1_110
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