Title:	Mild steel GMA welds microstructural analysis and estimation using sensor fusion and neural network modeling
Authors:	Caio, Leandro Bruno Alves Silva, Alysson Martins Almeida Alvarez Bestard, Guillermo Vieira, Lais Soares Carvalho, Guilherme Caribé de Alfaro, Sadek Crisóstomo Absi
metadata.dc.identifier.orcid:	https://orcid.org/ 0000-0001-6659-441X https://orcid.org/ 0000-0002-7426-0687 https://orcid.org/ 0000-0002-0361-0555
Assunto::	GMAW Estimativa de microestrutura Redes neurais Fusão de sensores
Issue Date:	13-Aug-2021
Publisher:	MDPI
Citation:	CAIO, Leandro Bruno Alves et al. Mild steel GMA welds microstructural analysis and estimation using sensor fusion and neural network modeling. Sensors, v. 21, n. 16, 5459, 2021. DOI: https://doi.org/10.3390/s21165459. Disponível em: https://www.mdpi.com/1424-8220/21/16/5459. Acesso em: 02 set. 2021.
Abstract:	This study aims at evaluating the efficiency of sensor fusion, based on neural networks, to estimate the microstructural characteristics of both the weld bead and base material in GMAW processes. The weld beads of AWS ER70S-6 wire were deposited on SAE 1020 steel plates varying welding voltage, welding speed, and wire-feed speed. The thermal behavior of the material during the process execution was analyzed using thermographic information gathered by an infrared camera. The microstructure was characterized by optical (confocal) microscopy, scanning electron microscopy, and X-ray Diffraction tests. Finally, models for estimating the weld bead microstructure were developed by fusing all the information through a neural network modeling approach. A R value of 0.99472 was observed for modelling all zones of microstructure in the same ANN using Bayesian Regularization with 17 and 15 neurons in the first and second hidden layers, respectively, with 4 training runs (which was the lowest R value among all tested configurations). The results obtained prove that RNAs can be used to assist the project of welded joints as they make it possible to estimate the extension of HAZ.
Licença::	Copyright: © 2021 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 (https:// creativecommons.org/licenses/by/ 4.0/).
DOI:	https://doi.org/10.3390/s21165459
Appears in Collections:	Artigos publicados em periódicos e afins

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