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Sensors (Basel). 2018 Mar 23;18(4). pii: E962. doi: 10.3390/s18040962.

Sensor Fusion to Estimate the Depth and Width of the Weld Bead in Real Time in GMAW Processes.

Author information

1
Postgraduate Program in Mechatronic Systems (PPMEC), Faculty of Technology, University of Brasilia, Campus Darcy Ribeiro, Brasilia 70910-900, Brazil. guillermo31416@gmail.com.
2
Department of Automatic Control, Institute of Cybernetics, Mathematics and Physics, Havana 10400, Cuba. guillermo31416@gmail.com.
3
Software Engineering Group, Faculty of Gama, University of Brasilia, Brasilia 72444-240, Brazil. renatocoral@unb.br.
4
Department of Electrical Engineering, Faculty of Technology, University of Brasilia, Brasilia 70910-900, Brazil. vargas@ene.unb.br.
5
Department of Mechanical and Mechatronic Engineering, Faculty of Technology, University of Brasilia, Brasilia 70910-900, Brazil. sadek@unb.br.

Abstract

The 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.

KEYWORDS:

FPGA; GMAW; arc welding process; bead depth estimation; bead width estimation; neural network; sensor fusion

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