Título:	Sensor fusion to estimate the depth and width of the weld bead in real time in GMAW processes
Autor(es):	Alvarez Bestard, Guillermo Sampaio, Renato Coral Vargas, José A. R. Alfaro, Sadek Crisóstomo Absi
ORCID:	https://orcid.org/0000-0001-6659-441X https://orcid.org/0000-0002-0361-0555
Assunto:	Soldagem FPGA Soldagem GMAW Redes neurais (Computação) Fusão de sensores
Data de publicação:	23-Mar-2018
Editora:	MDPI
Referência:	ALVAREZ BESTARD, Guillermo et al. Sensors, v. 18, n. 4, 962, 2018. DOI: https://doi.org/10.3390/s18040962. Disponível em: https://www.mdpi.com/1424-8220/18/4/962. Acesso em: 10 dez. 2020.
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.
Licença:	© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
DOI:	https://doi.org/10.3390/s18040962
Aparece nas coleções:	Artigos publicados em periódicos e afins

Os itens no repositório estão protegidos por copyright, com todos os direitos reservados, salvo quando é indicado o contrário.