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

• Main Authors: Pamela Chiñas-Sanchez, Ismael Lopez-Juarez, Jose Antonio Vazquez-Lopez, Jose Luis Navarro-Gonzalez, Aidee Hernandez-Lopez
• Format: Article
• Language: English
• Published: MDPI AG 2021-02-01
• Series: Mathematics
• Subjects: multivariate patterns; support vector machines (SVM); mahalanobis distance; robotic welding process; GMAW
• Online Access: https://www.mdpi.com/2227-7390/9/5/467

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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>D</mi><mn>2</mn></msup></semantics></math></inline-formula> 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>88.8</mn><mo>%</mo></mrow></semantics></math></inline-formula>, which demonstrates the effectiveness of the method, which can also be used in other MVPR tasks.