Using support vector regression for metal foam control
Article Sidebar
Main Article Content
Abstract
Profile monitoring focuses on the process or product variables that are characterized by a functional relationship of this variable with respect to time or space. The objective of this research is to develop a methodology based on Support Vector Regression, SVR, for no linear profiles monitoring and implement it to the density profiles of a cellular material, aluminum metal foam. The shape of a profile is associated to certain mechanic characteristics of a product which means that a significant change in the shape would be detected as an out-of-control observation by a monitoring method designed for this purpose; if this happened, it can be concluded that the mechanic properties would be different from those required. The methodology considers the estimation of percentile curves that will be the basis to define the control chart limits, and the estimation of the parameter Cost and Sigma of a SVR model with a Gaussian Kernel with the aid of cross-validation. The performance of the established control chart is evaluated supported by the boostrapping technique. The proposed method is practical and of simple interpretation. According to the results, if the shape of the density profile were to change beyond the shape indicated by the natural variability of the profile, the implemented method would detect it as out-of-control with a type I error of 0.341% (ARLreal = 293).
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Los autores conservan los derechos de autor y ceden a la revista el derecho de la primera publicación y pueda editarlo, reproducirlo, distribuirlo, exhibirlo y comunicarlo en el país y en el extranjero mediante medios impresos y electrónicos. Asimismo, asumen el compromiso sobre cualquier litigio o reclamación relacionada con derechos de propiedad intelectual, exonerando de responsabilidad a la Editorial Tecnológica de Costa Rica. Además, se establece que los autores pueden realizar otros acuerdos contractuales independientes y adicionales para la distribución no exclusiva de la versión del artículo publicado en esta revista (p. ej., incluirlo en un repositorio institucional o publicarlo en un libro) siempre que indiquen claramente que el trabajo se publicó por primera vez en esta revista.
References
J. Wang, J. Chiou y H. Muller, "Functional data analysis", Annual Review of Statistics and Its Application, vol.17, pp. 1- 41, 2015.
W. H. Woodall. "Current research on profile monitoring", Production, vol.17, pp. 420-425, 2007. https://www.scielo.br/j/prod/a/S6nScq3CJGWnbsCsByxHfJq/?format=pdf&lang=en
L. A. Jones-Farmer, W. H. Woodall, S. H. Steiner y C. W. Champ, "An overview of phase I analysis for process improvement and monitoring", Journal of Quality Technology, vol.46, pp. 265-280, 2014. https://www.tandfonline.com/doi/abs/10.1080/00224065.2014.11917969
M. Maleki, A. Amiri y P. Castagliola, "An overview on recent profile monitoring papers (2008–2018) based on conceptual classification scheme", Computers & Industrial Engineering, vol.126, pp. 705-728, 2018. https://doi.org/10.1016/j.cie.2018.10.008
M. Meneses-Guzmán,B. Chiné-Polito, F. Rodríguez-Méndez, V. Mussi, "Characterization of aluminium foams produced via a powder metallurgy route", Proceedings of the 1st Iberic Conference on Theoretical and Experimental Mechanics and Materials/11th National Congress on Experimental Mechanics, 2017. https://paginas.fe.up.pt/~tem2/Proceedings_TEMM2018/data/papers/7320.pdf
J. Goddard, S. Gerardo, B. Rosa y M. Gutiérrez, "Un algoritmo para el entrenamiento de Máquinas de Soporte para regresión", Revista de Matemática: Teoría y Aplicaciones, vol.17, p. 107–116, 2000. DOI 10.15517/RMTA.V7I1-2.183
B. Lantz, Machine Learning with R, Packt Publishing, 2013.
M. Awad y R. Khanna, Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers, Springer, pp. 221-232, 2015.
S. Cuentas, R. Peñabaena-Niebles y E. Garcia, "Support vector machine in statistical process monitoring: a methodological and analytical review", The International Journal of Advanced Manufacturing Technology, vol.91, pp. 485-500, 2017. https://link.springer.com/article/10.1007/s00170-016-9693-y
M. Reis y G. Gins, “Industrial Process Monitoring in the Big Data/Industry 4.0 Era: From Detection, to Diagnosis, to Prognosis”, Processes, vol.5, p. 35, 2017.
J. M. Moguerza, A. Muñoz y S. Psarakis, “Monitoring nonlinear profiles using support vector machines”, de Iberoamerican congress on pattern recognition, Berlin, Heidelberg, 2007. https://link.springer.com/chapter/10.1007/978-3-540-76725-1_60
A. Smola y B. Scholkopf, “A Tutorial on Support Vector Regression”, Statistics and Computing, vol.14, p. 199 – 222, 2004.
P. Refaelizadeh, L. Tang y H. Liu, “Cross – Validation”, Encyclopedia of database systems, vol.5, pp. 532-538, 2009.
G. James, D. Witten, T. Hastie y R. Tibshirani, An Introduction to Statistical Learning with Applications in R, Springer, 2013.
D. Montgomery, Introduction to statistical quality control. John Wiley & Sons, 2020.
J. Banhart, “Manufacture, characterization and application of cellular metals and metal foams”, Progress in Materials Science, vol.46, pp. 559-632, 2001. https://didattica-2000.archived.uniroma2.it//tbs2/deposito/Manufacture,_characterisation_and_application_of_cellular_metals_and_metal_foams.pdf
F. García-Moreno, “Commercial Applications of Metal Foams: Their Properties and Production”, Materials, vol.9, p. 85, 2016.
E. Solorzano, M. A. Rodriguez-Perez, J. A. Reglero y J. A. De Saja, “Density gradients in aluminium foams: characterisation by computed tomography and measurements of the effective thermal conductivity”, Journal of Materials Science, vol.42, pp. 2557-2564, 2007.
C. Körner y R. F. Singer, “Processing of metal foams—challenges and opportunities”, Advanced Engineering Materials, vol.4, pp. 159-165, 2000. https://onlinelibrary.wiley.com/doi/pdf/10.1002/%28SICI%291527-2648%28200004%292%3A4%3C159%3A%3AAID-ADEM159%3E3.0.CO%3B2-O
F. Rodríguez-Mendez, M. Meneses-Guzmán, B. Chiné-Polito, R. Pereira-Arroyo, “Desarrollo de un sistema automatizado de escaneo por radiación gamma”, Tecnología en Marcha, vol. 32, 3, pp 87-98, 2019. https://dialnet.unirioja.es/servlet/articulo?codigo=7448586