Sensor virtual inteligente para la inspección de calidad en sistemas robóticos de dispensación de adhesivos
Enviado: 05-12-2024
|Aceptado: 01-07-2025
|Publicado: 10-07-2025
Derechos de autor 2025 Alfredo Rodríguez Magdalena, Ignacio Díaz Blanco, Jose María Enguita González

Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
Descargas
Palabras clave:
Robots manipuladores, detección y diagnóstico de fallos, supervisión de las condiciones, Garantía de calidad y mantenimiento, instrumentos virtuales, sistemas N-dimensionales, precepción y detección, sistemas de fabricación flexible
Agencias de apoyo:
Normagrup Technology
Resumen:
La automatización de procesos de producción conlleva la eliminación de la supervisión humana directa, lo que exige el desarrollo de sistemas capaces de analizar la calidad de manera autónoma y en tiempo real. En este contexto, se propone el diseño de un sistema inteligente de inspección para la dispensación de adhesivo en un proceso de montaje de luminarias. El sistema parte de la observación de que los fluidos no newtonianos, como los utilizados en estos procesos, presentan una dinámica compleja influenciada por la reología, la gravedad y la geometría de aplicación, pudiendo manifestar comportamientos inestables que resultan en una deposición irregular y, por lo tanto, en ineficiencias. La solución propuesta se basa en un sistema de perfilometría láser 3D integrado sobre una célula robotizada, en la que un brazo manipulador desplaza el chasis de la luminaria bajo una válvula de dispensación de adhesivo. A partir de los perfiles generados durante el proceso, se extraen características representativas mediante técnicas de ingeniería de características y/o reducción de dimensionalidad. Estas se utilizan como entrada para un clasificador supervisado, cuyo objetivo es evaluar en tiempo real si la dispensación se está realizando correctamente. En conjunto, se presenta una solución viable y escalable para entornos de fabricación flexible, orientada a la inspección autónoma de calidad en procesos de montaje robotizado.
Citas:
Adargoma Suarez Garcia, L., Espinosa Escudero, M.D.M., Dominguez Somonte, M., 2021. ASSESSMENT OF ASSEMBLY PROCEDURES IN FUSED DEPOSITION MODELLING PARTS. DYNA 96, 39–43. https://doi.org/10.6036/9569
Bogert, W.v.d., Lorenz, J., Yi, X., Shih, A.J., Fazeli, N., 2024. Lumped-Parameter Modeling and Control for Robotic High-Viscosity Fluid Deposition. IEEE Robotics and Automation Letters 9, 1953–1960. https://doi.org/10.1109/LRA.2024.3349931
Brunton, S.L., Kutz, J.N., 2022. Data-Driven Science and Engineering. 2 ed., Cambridge University Press.
Burga, R., Tausek, A., 2003. Control of adhesive dispensing parameters during transition from research to production environments, in: Proceedings of the 5th Electronics Packaging Technology Conference (EPTC 2003), IEEE, Singapore. pp. 671–674. https://doi.org/10.1109/EPTC.2003.1271604
Chapra, S.C., Canale, R.P., 2010. Numerical methods for engineers. McGraw-Hill Higher Education.
Chen, X., Shoenau, G., Zhang, W., 2000. Modeling of time-pressure fluid dispensing processes. IEEE Transactions on Electronics Packaging Manufacturing 23, 300–305. https://doi.org/10.1109/6104.895075
Chen, X.B., 2009. Modeling and control of fluid dispensing processes: a state-of-the-art review. The International Journal of Advanced Manufacturing Technology 43, 276–286. https://doi.org/10.1007/s00170-008-1700-5
Cruz, Y.J., Rivas, M., Quiza, R., Beruvides, G., Haber, R.E., 2020. Computer vision system for welding inspection of liquefied petroleum gas pressure vessels based on combined digital image processing and deep learning techniques. Sensors 20. https://doi.org/10.3390/s20164505
Davies, B., Harris, S., Razban, A., Efstathiou, J., 1996. Application experience of a robotic cell for automated adhesive dispensing. Mathematics and Computers in Simulation 41, 419–427. https://doi.org/10.1016/0378-4754(95)00089-5
Derebail, A., Srihari, K., Emerson, C.R., 1994. An adhesive selection advisor for PCB assembly using surface mount technology. The International Journal of Advanced Manufacturing Technology 9, 93–105. https://doi.org/10.1007/BF01750416
Gavish, M., Donoho, D.L., 2014. The Optimal Hard Threshold for Singular Values is (4/sqrt {3}). IEEE Transactions on Information Theory 60, 5040–5053. https://doi.org/10.1109/TIT.2014.2323359
Golub, G., Kahan, W., 1965. Calculating the singular values and pseudo-inverse of a matrix. Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis 2, 205–224. https://doi.org/10.1137/0702016
Hastie, T., Tibshirani, R., Friedman, J., 2009. The Elements of Statistical Learning. Springer Series in Statistics, Springer New York, New York, NY. https://doi.org/10.1007/978-0-387-84858-7
Hunter, D., Yu, H., Pukish, III, M.S., Kolbusz, J., Wilamowski, B.M., 2012. Selection of proper neural network sizes and architectures—a comparative study. IEEE Transactions on Industrial Informatics 8, 228–240. https://doi.org/10.1109/TII.2012.2187914
Iakovou, D., Aarts, R., Meijer, J., 2005. Sensor integration for robotic laser welding processes. IEEE Electron Device Letters. https://doi.org/10.2351/1.5060477
James, G., Witten, D., Hastie, T., Tibshirani, R., 2021. An Introduction to Statistical Learning: with Applications in R. Springer Texts in Statistics, Springer US, New York, NY. https://doi.org/10.1007/978-1-0716-1418-1
Javadi, Y., Mohseni, E., Macleod, C., Lines, D., Vasilev, M., Mineo, C., Foster, E., Pierce, S., Gachagan, A., 2020. Continuous monitoring of an intentionally-manufactured crack using an automated welding and in-process inspection system. Materials & Design 191, 108655. https://doi.org/10.1016/j.matdes.2020.108655
Klema, V., Laub, A., 1980. The singular value decomposition: Its computation and some applications. IEEE Transactions on Automatic Control 25, 164–176. https://doi.org/10.1109/TAC.1980.1102314
Luo, G., 2016. A review of automatic selection methods for machine learning algorithms and hyper-parameter values. Network Modeling Analysis in Health Informatics and Bioinformatics 5, 18. https://doi.org/10.1007/s13721-016-0125-6
Lv, S., Kemao, Q., 2023. Modeling the measurement precision of Fringe Projection Profilometry. Light: Science & Applications 12, 257. https://doi.org/10.1038/s41377-023-01294-0
Razban, A., Davies, B., Harris, S., Efstathiou, J., 1995. Control of an automated dispensing cell with vision controlled feedback. Control Engineering Practice 3, 1217–1223. https://doi.org/10.1016/0967-0661(95)00120-J
Razban, A., Sezgin, O., Davies, B., 1991. Real time control of automated adhesive dispensing, in: Third International Conference on Software Engineering for Real Time Systems, 1991., IET. pp. 145–150.
Rout, A., Deepak, B., Biswal, B., 2019. Advances in weld seam tracking techniques for robotic welding: A review. Robotics and Computer-Integrated Manufacturing. https://doi.org/10.1016/J.RCIM.2018.08.003
Sirovich, L., Kirby, M., 1987. Low-dimensional procedure for the characterization of human faces. J. Opt. Soc. Am. A 4, 519–524. https://doi.org/10.1364/JOSAA.4.000519
Smith, S.W., 1997. The scientist and engineer’s guide to digital signal processing. 1. ed ed., California Technical Publ, San Diego, Calif.
Soofi, A.A., Awan, A., 2017. Classification techniques in machine learning: Applications and issues. Journal of Basic & Applied Sciences 13, 459–465.
Vaish, A., Yang Lee, S., Valdivia Alvarado, P., 2018. Viscosity Control of Pseudoplastic Polymer Mixtures for Applications in Additive-Manufacturing. https://doi.org/10.26153/TSW/17019
Vasilev, M., Macleod, C., Loukas, C., Javadi, Y., Vithanage, R., Lines, D., Mohseni, E., Pierce, S., Gachagan, A., 2021. Sensor-Enabled Multi-Robot System for Automated Welding and In-Process Ultrasonic NDE. Sensors (Basel, Switzerland) 21. https://doi.org/10.3390/s21155077
Wang, Z., 2024. The active visual sensing methods for robotic welding: review, tutorial and prospect. ArXiv abs/2405.00685. https://doi.org/10.48550/arXiv.2405.00685



