Esta es un versión antigua publicada el 2020-10-23. Consulte la versión más reciente.

REVISIÓN DE TÉCNICAS DE SISTEMAS DE VISIÓN ARTIFICIAL PARA LA INSPECCIÓN DE PROCESOS DE SOLDADURA TIPO GMAW

Autores/as

  • Erik Donaldo Lambraño García Universidad Francisco de Paula Santander
  • José Luis Lázaro Plata Universidad Francisco de Paula Santander
  • Alfredo Emilio Trigos Quintero Universidad Francisco de Paula Santander

DOI:

https://doi.org/10.24054/rcta.v1i29.189

Palabras clave:

GMAW, soldadura, visión artificial, inspección

Resumen

El proceso de soldadura GMAW es ampliamente estudiado debido a su alta productividad y bajo costo. En este trabajo se han revisado las investigaciones orientadas a la inspección del proceso de GMAW a través de sistemas de visión artificial con el
objetivo de establecer los principales elementos utilizados en estos sistemas destacando dos categorías: métodos computacionales (software y algoritmos generales), materiales y modelos matemáticos (métodos estadísticos y numéricos). Estas categorías se traslapan en el estudio y se han utilizado para evaluar el costo en términos de recursos humanos y recursos económicos. Las investigaciones revisadas se desarrollaron en la última década, con la excepción de algunas investigaciones que desempeñaron un papel principal en el desarrollo de los sistemas de inspección de los procesos GMAW. Finalmente, se han destacado los posibles campos de investigación para aquellos que intentan explorar sistemas de visión artificial para inspección de procesos GMAW.

Descargas

Los datos de descargas todavía no están disponibles.

Citas

Akkas, N., Karayel, D. G., Ozkan, S. S., Ahmet, O. L., & Topal, B. (2013). Modeling and Analysis of the Weld Bead Geometry in Submerged Arc Welding by Using Adaptive Neurofuzzy Inference System. Mathematical Problems in Engineering, 2013, 1–10. http://doi.org/10.1155/2013/473495

Alfaro, S. C. A., Vargas, J. A. R., De Carvalho, G. C., & De Souza, G. G. (2015). Characterization of “humping” in the GTA welding process using infrared images. Journal of Materials Processing Technology, 223, 216–224. http://doi.org/10.1016/j.jmatprotec.2015.03.052

Ali, G., Gsimalla, G., & Dening, J. (2013). Implementation of Welding Defects Detection and Monitoring in Arc Welding Robots. International Journal of Science and Research (IJSR) ISSN (Online Index Copernicus Value Impact Factor, 14611(2), 2319–7064. Retrieved from www.ijsr.net

Aviles-Viñas, J. F., Lopez-Juarez, I., & Rios-Cabrera, R. (2015). Industrial Robot: An International Journal For Authors Acquisition of welding skills in industrial robots. Industrial Robot: An International Journal An International Journal Industrial Robot An International Journal Industrial Robot An International Journal, 42(2), 156–166. Retrieved from http://dx.doi.org/10.1108/IR-09-2014-0395

Aviles-Viñas, J. F., Rios-Cabrera, R., & Lopez-Juarez, I. (2016). On-line learning of welding bead geometry in industrial robots. International Journal of Advanced Manufacturing Technology, 83(1–4), 217–231. http://doi.org/10.1007/s00170-015-7422-6

Baskoro, A. S., Masuda, R., Kabutomori, M., & Suga, Y. (2009). An application of genetic algorithm for edge detection of molten pool in fixed pipe welding. International Journal of Advanced Manufacturing Technology, 45(11–12), 1104–1112. http://doi.org/10.1007/s00170-009-2048-1.

Caballero Amaury, Velasco Gabriel, Pardo García A. (2013). Differentiations of objects in diffuse databases. Revista colombiana de tecnologías de Avanzada. 2 (22). Pág. 131 – 137.

Camargo, E., Coronel and Calderón, M. (2014). Hogar inteligente por control de voz usando redes neuronales. Revista Colombiana de Tecnologías de Avanzada. Vol 1. Núm. 25.

Campbell S., Galloway, A. M., & McPherson, N. A. (2012). Artificial Neural Network Prediction of Weld Geometry Performed Using GMAW with Alternating Shielding Gases. Welding Journal, 91(6), 174–181.

Carvalho, E. A. N., Luciano, B. A., Freire, R. C. S., Molina, L., & Freire, E. O. (2009). Fault-tolerant weld line detection for automatic inspection of storage tanks based on distance and visual information fusion. 2009 IEEE Intrumentation and Measurement Technology Conference, I2MTC 2009, (May), 791–796. http://doi.org/10.1109/IMTC.2009.5168558

Chapuis, J., Romero, E., Soulie, F., & Bordreuil, C. (2013). Experimental Analysis of Droplet-Gas Interaction During Gmaw Process. Trends in Welding Research: Proceedings of the 9Th International Conference, 448–452.

Chen, S., Zhang, S., Huang, N., Zhang, P., & Han, J. (2016). Droplet transfer in arcing-wire GTAW. Journal of Manufacturing Processes, 23, 149–156. http://doi.org/10.1016/j.jmapro.2016.05.014

Chokkalingham, S., Chandrasekhar, N., & Vasudevan, M. (2012). Predicting the depth of penetration and weld bead width from the infrared thermal image of the weld pool using artificial neural network modeling. Journal of Intelligent Manufacturing. http://doi.org/10.1007/s10845-011-0526-4

Chu, H.-H., & Wang, Z.-Y. (2016). A vision-based system for post-welding quality measurement and defect detection. International Journal of Advanced Manufacturing Technology, 86(9–12). http://doi.org/10.1007/s00170-015-8334-1

Cruz, J. G., Torres, E. M., & Absi Alfaro, S. C. (2015). A methodology for modeling and control of weld bead width in the GMAW process. Journal of the Brazilian Society of Mechanical Sciences and Engineering. http://doi.org/10.1007/s40430-014-0299-8

Dávila-Ríos, I., López-Juárez, I., Méndez, G. M., Osorio-Comparán, R., Lefranc, G., & Cubillos, C. (2016). A fuzzy approach for on-line error compensation during robotic welding. 2016 6th International Conference on Computers Communications and Control, ICCCC 2016, (Icccc), 264–270. http://doi.org/10.1109/ICCCC.2016.7496772

Duarte, L., Franco, N., Chagas, C. M., Koike, C., Crisóstomo, S., & Alfaro, A. (2007). Real time synchronization of weld pool image acquisition in the dip mode of metal transfer GMAW processes, 3–8.

Gao, X., Zhong, X., You, D., & Katayama, S. (2012). Kalman Filtering Compensated by Radial Basis Function Neural Network for Seam Tracking of Laser Welding. Control Systems Technology, IEEE Transactions on, PP(99), 1. http://doi.org/10.1109/TCST.2012.2219861

Gomes, J. H. F., Costa, S. C., Paiva, A. P., & Balestrassi, P. P. (2012). Mathematical modeling of weld bead geometry, quality, and productivity for stainless steel claddings deposited by FCAW. Journal of Materials Engineering and Performance.

Gonzalez, R. C. E., Woods, S. L., Gonzalez, R. E. R. E. R. C., Woods, R. E., & Eddins, S. L. (2004). Digital image processing using MATLAB (No. 04; TA1637, G6.).

Guo, B., Shi, Y., Yu, G., Liang, B., & Wang, K. (2016). Weld deviation detection based on wide dynamic range vision sensor in MAG welding process. The International Journal of Advanced Manufacturing Technology, 87(9–12), 3397–3410. http://doi.org/10.1007/s00170-016-8721-2

Guo, H., Hu, J., & Tsai, H. L. (2010). Three-Dimensional Modeling of Gas Metal Arc Welding of Aluminum Alloys. Journal of Manufacturing Science and Engineering, 132(2), http://doi.org/10.1115/1.4001479

He, Y., Xu, Y., Chen, Y., Chen, H., & Chen, S. (2016). Weld seam profile detection and feature point extraction for multi-pass route planning based on visual attention model. Robotics and Computer-Integrated Manufacturing, 37, 251–261. http://doi.org/10.1016/j.rcim.2015.04.005

Huang, W., & Kovacevic, R. (2011). A laser-based vision system for weld quality inspection. Sensors, 11(1), 506–521. http://doi.org/10.3390/s110100506

Huang, W., & Kovacevic, R. (2012). Development of a real-time laser-based machine vision system to monitor and control welding processes. International Journal of Advanced Manufacturing Technology, 63(1–4), 235–248. http://doi.org/10.1007/s00170-012-3902-0

Kim, I. S., Son, J. S., Park, C. E., Kim, I. J., & Kim, H. H. (2005). An investigation into an intelligent system for predicting bead geometry in GMA welding process. Journal of Materials Processing Technology, 159(1), 113–118. http://doi.org/10.1016/j.jmatprotec.2004.04.415

Kiran, D. V., Cheon, J., Arif, N., Chung, H., & Na, S. (2016). Three-dimensional finite element modeling of pulsed AC gas metal arc welding process. The International Journal of Advanced Manufacturing Technology, 1, 1453–1474. Li, Y., Li, Y. F., Wang, Q. L., Xu, D., & Tan, M. (2010). Measurement and defect detection of the weld bead based on online vision inspection. IEEE Transactions on Instrumentation and Measurement, 59(7), 1841–1849. Muhammad, J., Altun, H., & Abo-Serie, E. (2016). Welding seam profiling techniques based on active vision sensing for intelligent robotic welding. International Journal of Advanced Manufacturing Technology, 1–19. http://doi.org/10.1007/s00170-016-8707-0

Ogawa, Y. (2011). High speed imaging technique Part 1 – high speed imaging of arc welding phenomena. Science and Technology of Welding and Joining, 16(1), 33–43. http://doi.org/10.1179/136217110X12785889549903

Park, M. H., Kim, I. S., Lee, J. P., Kim, D. H., Jin, B. J., Kim, I. J., & Kim, J. S. (2016). Sensitivity Analysis for Prediction of Bead Geometry using Plasma Arc Welding in Bellows Segment. International Journal of Engineering Research & Science, 2(4), 2395–6992.

Pinto-Lopera, J. E., Motta, J. M. S. T., & Alfaro, S. C. A. (2016). Real-time measurement of width and height of weld beads in GMAW processes. Sensors (Switzerland), 16(9), 1–14. http://doi.org/10.3390/s16091500

Ramos, E. G., de Carvalho, G. C., & Absi Alfaro, S. C. (2013). Analysis of weld pool oscillation in P-GMAW by Means of Shadowgraphy Image Processing. Soldagem & Inspecao, 18(1), 39–49. http://doi.org/10.1590/S0104-92242013000100006

Ranjan, R., Khan, A. R., Parikh, C., Jain, R., Mahto, R. P., Pal, S., … Chakravarty, D. (2016). Classification and identification of surface defects in friction stir welding: An image processing approach. Journal of Manufacturing Processes, 22, 237–253. http://doi.org/10.1016/j.jmapro.2016.03.009

Rodríguez Oscar Oswaldo, Pineda Pinto Ronald Fernando, Cárdenas Pedro Fabián. (2012). Herramientas EJS 3D/MATLAB para el control del sistema no lineal aplicado al péndulo invertido sobre carro deslizante. Revista colombiana de tecnologías de Avanzada. 1 (19). Pág. 28 – 34.

Reisgen, U., Purrio, M., Buchholz, G., & Willms, K. (2014). Machine vision system for online weld pool observation of gas metal arc welding processes. Welding in the World, 58(5), 707–711. http://doi.org/10.1007/s40194-014-0152-9

Rios-Cabrera, R., America, ·, Morales-Diaz, B., Aviles-V Nas, J. F., & Lopez-Juarez, I. (2016). Robotic GMAW online learning: issues and experiments. Int J Adv Manuf Technol, 87, 2113–2134. http://doi.org/10.1007/s00170-016-8618-0

Senthil Kumar, G. ., Natarajan, U. ., Veerarajan, T. ., & Ananthan, S. S. . (2014). Quality level assessment for imperfections in GMAW. Welding Journal, 93(3), 85s–97s. Retrieved from https://www.scopus.com/inward/record.uri?eid=2-s2.0-84896272690&partnerID=40&md5=469d1cc80105d887ce947d3642828ea2

Shao, Y., Wang, Z., & Zhang, Y. (2011). Monitoring of liquid droplets in laser-enhanced GMAW. International Journal of Advanced Manufacturing Technology, 57(1–4), 203–214. http://doi.org/10.1007/s00170-011-3266-x

Shen, H. Y., Wu, J., Lin, T., & Chen, S. B. (2008). Arc welding robot system with seam tracking and weld pool control based on passive vision. International Journal of Advanced Manufacturing Technology, 39(7–8), 669–678. http://doi.org/10.1007/s00170-007-1257-8

Shengqiang, F., Hiroyuki, O., Hidennori, T., Yuichi, K., & Shengsun, H. (2011). Qualitative and quantitative analysis of GMAW welding fault based on Mahalanobis distance. International Journal of Precision Engineering and Manufacturing. http://doi.org/10.1007/s12541-011-0127-3

Squillace, A., Prisco, U., Ciliberto, S., & Astarita, A. (2012). Effect of welding parameters on morphology and mechanical properties of Ti-6Al-4V laser beam welded butt joints. Journal of Materials Processing Technology, 212(2), 427–436. http://doi.org/10.1016/j.jmatprotec.2011.10.005

Sreedhar, U., Krishnamurthy, C. V., Balasubramaniam, K., Raghupathy, V. D., & Ravisankar, S. (2012). Automatic defect identification using thermal image analysis for online weld quality monitoring. Journal of Materials Processing Technology, 212(7), 1557–1566. http://doi.org/10.1016/j.jmatprotec.2012.03.002

Sreeraj, P., Kannan, T., & Maji, S. (2013). Estimation of Optimum Dilution in the GMAW Process Using Integrated ANN-GA. Journal of Engineering (United States), 2013. http://doi.org/10.1155/2013/285030

Ummenhofer, T., & Medgenberg, J. (2009). On the use of infrared thermography for the analysis of fatigue damage processes in welded joints. International Journal of Fatigue, 31(1), 130–137. http://doi.org/10.1016/j.ijfatigue.2008.04.005

Wang, X., Shi, Y., Yu, G., Liang, B., & Li, Y. (2016). Groove-center detection in gas metal arc welding using a template-matching method. The International Journal of Advanced Manufacturing Technology, 86(9–12), 2791–2801. http://doi.org/10.1007/s00170-016-8389-7

Wang, Z. Z., Ma, X. J., & Zhang, Y. M. (2011). Simultaneous Imaging and Measurement of Pool Surface and Metal Transfer. Welding Journal, 90(6), 121S–128S.

Wang, Z., Zhang, Y. M., & Wu, L. (2010). Measurement and Estimation of Weld Pool Surface Depth and Weld Penetration. Welding Journal, 89(6), 117s–126s. Retrieved from https://app.aws.org/wj/supplement/wj0610-117.pdf

Wang, Z. (2016). A laser back-lighting based metal transfer monitoring system for robotic gas metal arc welding. Robotics and Computer-Integrated Manufacturing, 38, 52–66. http://doi.org/10.1016/j.rcim.2015.10.004

Wang, Z., Huang, Y., & Zhang, Y. (2013). Unsupervised droplet identification during the pulsed laser enhanced GMAW process. International Journal of Advanced Manufacturing Technology, 67(5–8), 1449–1457. http://doi.org/10.1007/s00170-012-4580-7

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. International Journal of Advanced Manufacturing Technology, 73(9–12), 1413–1425. http://doi.org/10.1007/s00170-014-5925-1

Xu, Y., Fang, G., Lv, N., Chen, S., & Jia Zou, J. (2015). Computer vision technology for seam tracking in robotic GTAW and GMAW. Robotics and Computer-Integrated Manufacturing, 32, 25–36. http://doi.org/10.1016/j.rcim.2014.09.002

Descargas

Publicado

2020-10-23

Versiones

Cómo citar

Lambraño García, E. D., Lázaro Plata, J. L. ., & Trigos Quintero, A. E. . (2020). REVISIÓN DE TÉCNICAS DE SISTEMAS DE VISIÓN ARTIFICIAL PARA LA INSPECCIÓN DE PROCESOS DE SOLDADURA TIPO GMAW. REVISTA COLOMBIANA DE TECNOLOGIAS DE AVANZADA (RCTA), 1(29), 47–57. https://doi.org/10.24054/rcta.v1i29.189

Número

Sección

Artículos