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

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17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022) (SOCO 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 531))

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Abstract

This paper presents an approach based on machine learning methods to solve a real industrial problem. During the manufacture of stainless steel with certain characteristics, due to the manufacturing process itself, the steel moves away from the ideal conditions and it is necessary to determine how far the final product is from the desired one. For this determination, a procedure for the development of a virtual sensor has been carried out to replace the current semi-manual procedure of the ACERINOX EUROPA, S.A.U. factory in Cadiz. The results obtained are very promising and it is planned to install an application in the factory to work initially in parallel to the human expert until it can be used as a stand-alone application.

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References

  1. 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)

    Google Scholar 

  2. 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

    Chapter  Google Scholar 

  3. Davis, J.R., et al.: Stainless steels. In: ASM International (1994)

    Google Scholar 

  4. 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)

    Google Scholar 

  5. 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)

    Google Scholar 

  6. Lee, J., Davari, H., Singh, J., Pandhare, V.: Industrial artificial intelligence for industry 4.0-based manufacturing systems. Manuf. Lett. 18, 20–23 (2018)

    Google Scholar 

  7. 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)

    Article  Google Scholar 

  8. Lo, K.H., Shek, C.H., Lai, J.: Recent developments in stainless steels. Mater. Sci. Eng. R. Rep. 65(4–6), 39–104 (2009)

    Article  Google Scholar 

  9. 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)

    Article  Google Scholar 

  10. 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

    Chapter  Google Scholar 

  11. Møller, M.F.: A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw. 6(4), 525–533 (1993)

    Article  Google Scholar 

  12. Noble, W.S.: What is a support vector machine? Nat. Biotechnol. 24(12), 1565–1567 (2006)

    Article  Google Scholar 

  13. 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)

    Article  Google Scholar 

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Acknowledgement

This work is supported through grant RTI2018-098160-B-I00 from MICINN-SPAIN and OT2020/091 ACERINOX EUROPA, S.A.U. (A86327632)

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Cadiz, Cadiz, Spain

    Damián Nimo, Javier González-Enrique, Daniel Urda & Ignacio J. Turias

  2. Dpto. Técnico, Polígono Industrial Los Barrios ACERINOX Europa, S.A.U., Los Barrios, Spain

    David Perez & Juan Almagro

Authors
  1. Damián Nimo
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  2. Javier González-Enrique
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  3. David Perez
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  4. Juan Almagro
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  5. Daniel Urda
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  6. Ignacio J. Turias
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Corresponding author

Correspondence to Damián Nimo .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, University of Deusto, Bilbao, Spain

    Pablo García Bringas

  2. University of León, León, Spain

    Hilde Pérez García

  3. Mechanical Engineering Department, University of La Rioja, Logroño, La Rioja, Spain

    Francisco Javier Martinez-de-Pison

  4. Inteligencia Artificial, University of Oviedo, A Coruña, La Coruña, Spain

    José Ramón Villar Flecha

  5. Data Science and Big Data Lab, Pablo de Olavide University, Sevilla, Spain

    Alicia Troncoso Lora

  6. University of Oviedo, Oviedo, Spain

    Enrique A. de la Cal

  7. Department of Civil Engineering, University of Burgos, Burgos, Spain

    Álvaro Herrero

  8. School of engineering, Pablo Olavide University, Seville, Spain

    Francisco Martínez Álvarez

  9. DIGIP, University of Bergamo, Dalmine, Bergamo, Italy

    Giuseppe Psaila

  10. Department of Industrial Engineering, University of A Coruña, Ferrol, Spain

    Héctor Quintián

  11. Department of Computing Science, University of Salamanca, Salamanca, Spain

    Emilio S. Corchado Rodriguez

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Nimo, D., González-Enrique, J., Perez, D., Almagro, J., Urda, D., Turias, I.J. (2023). A Virtual Sensor Approach to Estimate the Stainless Steel Final Chemical Characterisation. In: García Bringas, P., et al. 17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022). SOCO 2022. Lecture Notes in Networks and Systems, vol 531. Springer, Cham. https://doi.org/10.1007/978-3-031-18050-7_34

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