Discontinuity Detection in the Shield Metal Arc Welding Process
AbstractThis work proposes a new methodology for the detection of discontinuities in the weld bead applied in Shielded Metal Arc Welding (SMAW) processes. The detection system is based on two sensors—a microphone and piezoelectric—that acquire acoustic emissions generated during the welding. The feature vectors extracted from the sensor dataset are used to construct classifier models. The approaches based on Artificial Neural Network (ANN) and Support Vector Machine (SVM) classifiers are able to identify with a high accuracy the three proposed weld bead classes: desirable weld bead, shrinkage cavity and burn through discontinuities. Experimental results illustrate the system’s high accuracy, greater than 90% for each class. A novel Hierarchical Support Vector Machine (HSVM) structure is proposed to make feasible the use of this system in industrial environments. This approach presented 96.6% overall accuracy. Given the simplicity of the equipment involved, this system can be applied in the metal transformation industries. View Full-Text
Share & Cite This Article
Cocota, J.A.N.; Garcia, G.C.; Da Costa, A.R.; De Lima, M.S.F.; Rocha, F.A.S.; Freitas, G.M. Discontinuity Detection in the Shield Metal Arc Welding Process. Sensors 2017, 17, 1082.
Cocota JAN, Garcia GC, Da Costa AR, De Lima MSF, Rocha FAS, Freitas GM. Discontinuity Detection in the Shield Metal Arc Welding Process. Sensors. 2017; 17(5):1082.Chicago/Turabian Style
Cocota, José A.N.; Garcia, Gabriel C.; Da Costa, Adilson R.; De Lima, Milton S.F.; Rocha, Filipe A.S.; Freitas, Gustavo M. 2017. "Discontinuity Detection in the Shield Metal Arc Welding Process." Sensors 17, no. 5: 1082.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.