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

Pamela Chiñas-Sanchez, Ismael Lopez-Juarez, Jose Antonio Vazquez-Lopez, Jose Luis Navarro-Gonzalez and Aidee Hernandez-Lopez
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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
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

Mathematics, 2021, vol. 9, issue 5, 1-14

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.

Keywords: multivariate patterns; support vector machines (SVM); mahalanobis distance; robotic welding process; GMAW (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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