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Sánchez Aguiar, A. F., & Espinosa Bedoya, A. (2018). Identificación de Poros en Uniones Soldadas Empleando Técnicas de Visión por Computador. Ingenierías USBmed, 9(2), 27–33. https://doi.org/10.21500/20275846.3532
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Abstract
La inspección visual automática se ha convertido en un proceso con amplia aplicación a nivel industrial en procesos relacionados con la inspección de la calidad en productos. Las uniones soldadas pueden presentar defectos como: salpicaduras, grietas, socavados y poros, generalmente se evalúan en campo en las estructuras empleando personal calificado. En este artículo se presenta un método basado en visión por computador para identificar los poros en uniones soldadas. El método desarrollado alcanza una eficiencia del 78%.
References
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[2] Domingo Mery. Inspección visual automática. In Primer Congreso Internacional de Ingeniería Mecatrónica, Lima, Perú, 2002.
[3] Victor Hugo C. de Albuquerque, Cleiton Carvalho Silva, Thiago Ivo de S. Menezes, Jesualdo Pereira Farias, and Joao Manuel R. S. Tavares. Automatic evaluation of nickel alloy secondary phases from sem images. Microscopy Research and Technique, 74(1):36–46, 2011.
[4] Peter Ian Hansen, Brett Browning, Peter Rander, and Hatem Alismail. Automating visual inspection of pipes used for natural gas production. Qatar Foundation Annual Research Forum Proceedings, (2010):EEO4–, December 2010.
[5] Hong-Dar Lin, Y Chiu, and Shih-Yin Hsu. A visual inspection system for quality control of optical lenses. International Journal of the Physical Sciences, 6(11):2701–2709, 2011.
[9] N.S.S. Mar, P.K.D.V. Yarlagadda, and C. Fookes. Design and development of automatic visual inspection system for pcb manufacturing. Robotics and Computer-Integrated Manufacturing, 27(5):949 – 962, 2011.
[6] Ricardo Petrino, Miguel Angel Guarnes, and Hector Gellon. Inspeccion automatica de impresiones sobre envases cilindricos empleando vision artificial. In VIII Congreso Argentino de Ciencias de la Computación, 2002.
[7] S. Ravikumar, K.I. Ramachandran, and V. Sugumaran. Machine learning approach for automated visual inspection of machine components. Expert Systems with Applications, 38(4):3260 – 3266, 2011.
[8] S Swillo and M Perzyk. Automatic inspection of surface defects in die castings after machining. Archives of Foundry Engineering, 11:231–236, 2011.
[10] Wang, G., & Liao, T. W. (2002). Automatic identification of different types of welding defects in radiographic images. Ndt & E International, 35(8), 519-528.
[11] Carrasco, M. A., & Mery, D. (2004). Segmentation of welding defects using a robust algorithm. Materials Evaluation, 62(11), 1142-1147.
[12] AMZA, C. G., FLORIN, D. N., & GEORGE, E. Automatic Image Processing For Welding Inspection.
[13] Xu, Y., Fang, G., Chen, S., Zou, J. J., & Ye, Z. (2014). Real-time image processing for vision-based weld seam tracking in robotic GMAW. The International Journal of Advanced Manufacturing Technology, 1-13.
[14] Lima, I., de Oliveira, D., Moreira, E. V., Chad, L. C., Ponciano, M., Manão, R., & Lopes, R. T. Investigation of weld cracks by Microfocus tomography.
[15] Norman, P., Engström, H., & Kaplan, A. F. H. (2007, August). State-of-the-art of monitoring and imaging of laser welding defects. In 11th NOLAMP Conference, 11th NOLAMP Conference in Laser Processing of Materials (pp. 20-22).
[16] Mery, D., & Berti, M. A. (2003). Automatic detection of welding defects using texture features. Insight-Non-Destructive Testing and Condition Monitoring, 45(10), 676-681.
[17] Carvalho, A. A. D., Suita, R. C. D. S. B., Silva, R. R. D., & Rebello, J. M. A. (2003). Evaluation of the relevant features of welding defects in radiographic inspection. Materials Research, 6(3), 427-432.
[18] Rathod, V. R., & Anand, R. S. (2010, February). Comparison of different segmentation techniques in detection of flaws in weldments. In Computer and Automation Engineering (ICCAE), 2010 The 2nd International Conference on (Vol. 2, pp. 673-677). IEEE.
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