Sensor Fusion to Estimate the Depth and Width of the Weld Bead in Real Time in GMAW Processes
AbstractThe arc welding process is widely used in industry but its automatic control is limited by the difficulty in measuring the weld bead geometry and closing the control loop on the arc, which has adverse environmental conditions. To address this problem, this work proposes a system to capture the welding variables and send stimuli to the Gas Metal Arc Welding (GMAW) conventional process with a constant voltage power source, which allows weld bead geometry estimation with an open-loop control. Dynamic models of depth and width estimators of the weld bead are implemented based on the fusion of thermographic data, welding current and welding voltage in a multilayer perceptron neural network. The estimators were trained and validated off-line with data from a novel algorithm developed to extract the features of the infrared image, a laser profilometer was implemented to measure the bead dimensions and an image processing algorithm that measures depth by making a longitudinal cut in the weld bead. These estimators are optimized for embedded devices and real-time processing and were implemented on a Field-Programmable Gate Array (FPGA) device. Experiments to collect data, train and validate the estimators are presented and discussed. The results show that the proposed method is useful in industrial and research environments. View Full-Text
Share & Cite This Article
Bestard, G.A.; Sampaio, R.C.; Vargas, J.A.R.; Alfaro, S.C.A. Sensor Fusion to Estimate the Depth and Width of the Weld Bead in Real Time in GMAW Processes. Sensors 2018, 18, 962.
Bestard GA, Sampaio RC, Vargas JAR, Alfaro SCA. Sensor Fusion to Estimate the Depth and Width of the Weld Bead in Real Time in GMAW Processes. Sensors. 2018; 18(4):962.Chicago/Turabian Style
Bestard, Guillermo A.; Sampaio, Renato C.; Vargas, José A.R.; Alfaro, Sadek C.A. 2018. "Sensor Fusion to Estimate the Depth and Width of the Weld Bead in Real Time in GMAW Processes." Sensors 18, no. 4: 962.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.