Next Article in Journal
Vehicle Counting and Moving Direction Identification Based on Small-Aperture Microphone Array
Previous Article in Journal
Nitride-Based Materials for Flexible MEMS Tactile and Flow Sensors in Robotics
Due to scheduled maintenance work on our core network, there may be short service disruptions on this website between 16:00 and 16:30 CEST on September 25th.
Article

Discontinuity Detection in the Shield Metal Arc Welding Process

1
School of Mines, Federal University of Ouro Preto (UFOP), Morro do Cruzeiro, 35400-000 Ouro Preto, Brazil
2
Instituto Tecnológico Vale (ITV)—Avenida Juscelino Kubitschek, 31, Bauxita, 35400-000 Ouro Preto, Brazil
3
Institute for Advanced Studies (IEAv-CTA), 12228-970 São José dos Campos, SP, Brazil
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Sensors 2017, 17(5), 1082; https://doi.org/10.3390/s17051082
Received: 23 February 2017 / Revised: 16 April 2017 / Accepted: 25 April 2017 / Published: 10 May 2017
(This article belongs to the Section Physical Sensors)
This 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
Keywords: support vector machine; artificial neural network; shielded metal arc welding; sensory fusion support vector machine; artificial neural network; shielded metal arc welding; sensory fusion
Show Figures

Figure 1

MDPI and ACS Style

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. https://doi.org/10.3390/s17051082

AMA Style

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. https://doi.org/10.3390/s17051082

Chicago/Turabian Style

Cocota, José A.N., Gabriel C. Garcia, Adilson R. Da Costa, Milton S.F. De Lima, Filipe A.S. Rocha, and Gustavo M. Freitas 2017. "Discontinuity Detection in the Shield Metal Arc Welding Process" Sensors 17, no. 5: 1082. https://doi.org/10.3390/s17051082

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop