scholarly journals Microstructural and Mechanical Characterization of Ti‐Containing Twinning‐Induced Plasticity Steel Welded Joint Produced by Gas Tungsten Arc Welding Process

2020 ◽  
Vol 91 (9) ◽  
pp. 2000129
Author(s):  
Humberto Hernández-Belmontes ◽  
Ignacio Mejía ◽  
José Antonio Benito ◽  
José María Cabrera
Keyword(s):  
Arc Welding ◽  
Mechanical Characterization ◽  
Welded Joint ◽  
Welding Process ◽  
Gas Tungsten Arc Welding ◽  
Gas Tungsten Arc ◽  
Gas Tungsten ◽  
Arc Welding Process

scholarly journals Characterization of titanium welded joints by the orbital gas tungsten arc welding process for aerospace application

2010 ◽  
Vol 2 (2) ◽  
pp. 203-210 ◽  
Author(s):  
José Augusto Orlowski de Garcia ◽  
Gérson Luiz de Lima ◽  
Wilson D. Bocallão Pereira ◽  
Valdir Alves Guimarães ◽  
Carlos de Moura Neto ◽  
...  
Keyword(s):  
Welded Joints ◽  
Arc Welding ◽  
Welding Process ◽  
Gas Tungsten Arc Welding ◽  
Aerospace Application ◽  
Gas Tungsten Arc ◽  
Gas Tungsten ◽  
Arc Welding Process

A Neural Network Model for Predicting Two Observable Parameters of the Welded Cross Section in Case of Robotic Gas Tungsten Arc Welding Process

2012 ◽  
Vol 162 ◽  
pp. 531-536
Author(s):  
Gabriel Gorghiu ◽  
Paul Ciprian Patic ◽  
Dorin Cârstoiu
Keyword(s):  
Neural Network ◽  
Arc Welding ◽  
Welding Process ◽  
Gas Tungsten Arc Welding ◽  
Gas Tungsten Arc ◽  
Proposed Model ◽  
Input Layer ◽  
Gas Tungsten ◽  
Arc Welding Process ◽  
Observable Parameter

The paper presents a model of using the artificial neural networks when determining the relations of dependency between the observable parameters and the controllable ones in the case of RoboticGas Tungsten Arc Welding. The proposed model is based on the direct observation of welded joints, emphasizing on the process variables which have been arranged in the nodes of a neural network. The design of the network intended to achieve an architecture that contains four nodes in the input layer (all of them being controllable parameters) and two nodes in the output layer (one for each observable parameter).

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