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Out-of-Control Multivariate Patterns Recognition Using D 2 and SVM: A Study Case for GMAW

Author

Listed:
  • Pamela Chiñas-Sanchez

    (Tecnologico Nacional de Mexico, Instituto Tecnologico de Saltillo, Saltillo 25280, Mexico)

  • Ismael Lopez-Juarez

    (Centre for Research and Advanced Studies (CINVESTAV), Ramos Arizpe 25900, Mexico
    Current address: Ind. Metalurgica 1062, P Ind Saltillo Ramos Arizpe, Ramos Arizpe, Coahuila 25900, Mexico.)

  • Jose Antonio Vazquez-Lopez

    (Tecnologico Nacional de Mexico, Instituto Tecnologico de Celaya, Celaya 38010, Mexico)

  • Jose Luis Navarro-Gonzalez

    (IJ Robotics SA de CV, Saltillo 25000, Mexico)

  • Aidee Hernandez-Lopez

    (Sistema Avanzado de Bachillerato y Educacion Superior, Celaya 38010, Mexico)

Abstract

Industrial processes seek to improve their quality control, including new technologies and satisfying requirements for globalised markets. In this paper, we present an innovative method based on Multivariate Pattern Recognition (MVPR) and process monitoring in a real-world study case. By identifying a distinctive out-of-control multivariate pattern using the Support Vector Machines (SVM) and the Mahalanobis Distance D 2 it is possible to infer the variables that disturbed the process; hence, possible faults can be predicted knowing the state of the process. The method is based on our previous work, and in this paper we present the method application for an automated process, namely, the robotic Gas Metal Arc Welding (GMAW). Results from the application indicate an overall accuracy up to 88.8 % , which demonstrates the effectiveness of the method, which can also be used in other MVPR tasks.

Suggested Citation

  • Pamela Chiñas-Sanchez & Ismael Lopez-Juarez & Jose Antonio Vazquez-Lopez & Jose Luis Navarro-Gonzalez & Aidee Hernandez-Lopez, 2021. "Out-of-Control Multivariate Patterns Recognition Using D 2 and SVM: A Study Case for GMAW," Mathematics, MDPI, vol. 9(5), pages 1-14, February.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:5:p:467-:d:505371
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    References listed on IDEAS

    as
    1. María Nela Pastuizaca Fernández & Andrés Carrión García & Omar Ruiz Barzola, 2015. "Multivariate multinomial T2 control chart using fuzzy approach," International Journal of Production Research, Taylor & Francis Journals, vol. 53(7), pages 2225-2238, April.
    2. Xueliang Zhou & Pingyu Jiang & Xianxiang Wang, 2018. "Recognition of control chart patterns using fuzzy SVM with a hybrid kernel function," Journal of Intelligent Manufacturing, Springer, vol. 29(1), pages 51-67, January.
    Full references (including those not matched with items on IDEAS)

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