A Virtual Sensor Approach to Estimate the Stainless Steel Final Chemical Characterisation

  1. Nimo, Damián
  2. González-Enrique, Javier
  3. Perez, David
  4. Almagro, Juan
  5. Urda, Daniel
  6. Turias, Ignacio J.
  1. 1 Department of Computer Science, University of Cadiz, Cadiz, Spain
  2. 2 Dpto. Técnico, Polígono Industrial Los Barrios ACERINOX Europa, S.A.U., Los Barrios, Spain
Actes de conférence:
17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022)

ISSN: 2367-3370,  2367-3389

ISBN: 9783031180491,  9783031180507

Année de publication: 2022

Pages: 350-360

Type: Communication dans un congrès

DOI: 10.1007/978-3-031-18050-7_34 GOOGLE SCHOLAR lock_openAccès ouvert editor

Objectifs de Développement Durable

Références bibliographiques

  • Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pp. 144–152 (1992)
  • Bunkhumpornpat, C., Sinapiromsaran, K., Lursinsap, C.: Safe-Level-SMOTE: Safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, T.-B. (eds.) PAKDD 2009. LNCS (LNAI), vol. 5476, pp. 475–482. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-01307-2_43
  • Davis, J.R., et al.: Stainless steels. In: ASM International (1994)
  • Dilberoglu, U.M., Gharehpapagh, B., Yaman, U., Dolen, M.: The role of additive manufacturing in the era of industry 4.0. Proc. Manuf. 11, 545–554 (2017)
  • Dopico, M., Gómez, A., De la Fuente, D., García, N., Rosillo, R., Puche, J.: A vision of industry 4.0 from an artificial intelligence point of view. In: Proceedings on the International Conference on Artificial Intelligence (ICAI), p. 407. The Steering Committee of The World Congress in Computer Science, Computer (2016)
  • Lee, J., Davari, H., Singh, J., Pandhare, V.: Industrial artificial intelligence for industry 4.0-based manufacturing systems. Manuf. Lett. 18, 20–23 (2018)
  • Li, T., Bolic, M., Djuric, P.M.: Resampling methods for particle filtering: classification, implementation, and strategies. IEEE Signal Process. Mag. 32(3), 70–86 (2015)
  • Lo, K.H., Shek, C.H., Lai, J.: Recent developments in stainless steels. Mater. Sci. Eng. R. Rep. 65(4–6), 39–104 (2009)
  • Martínez-López, F.J., Casillas, J.: Artificial intelligence-based systems applied in industrial marketing: An historical overview, current and future insights. Ind. Mark. Manage. 42(4), 489–495 (2013)
  • Mesa, H., et al.: A machine learning approach to determine abundance of inclusions in stainless steel. In: Pérez García, H., Sánchez González, L., Castejón Limas, M., Quintián Pardo, H., Corchado Rodríguez, E. (eds.) HAIS 2019. LNCS (LNAI), vol. 11734, pp. 504–513. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29859-3_43
  • Møller, M.F.: A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw. 6(4), 525–533 (1993)
  • Noble, W.S.: What is a support vector machine? Nat. Biotechnol. 24(12), 1565–1567 (2006)
  • Saritas, M.M., Yasar, A.: Performance analysis of ann and naive bayes classification algorithm for data classification. Int. J. Intell. Syst. Appli. Eng. 7(2), 88–91 (2019)