Investigations on quality characteristics in gas tungsten arc welding process using artificial neural network integrated with genetic algorithm

Tomaz, Italo do Valle and Colaço, Fernando Henrique Gruber and Sarfraz, Shoaib and Pimenov, Danil Yu and Gupta, Munish Kumar and Pintaude, Giuseppe (2021) Investigations on quality characteristics in gas tungsten arc welding process using artificial neural network integrated with genetic algorithm. International Journal of Advanced Manufacturing Technology, 113 (11-12). pp. 3569-3583. ISSN 0268-3768

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Abstract

Gas tungsten arc welding (GTAW) technology is widely used in industry and has advantages, including high precision, excellent welding quality, and low equipment cost. However, the inclusion of a large number of process parameters hinders its application on a wider scale. Therefore, there is a need to implement the prediction and optimization models that effectively enhance the process performance of the GTAW process in different applications. In this study, a five-factor five-level central composite design (CCD) matrix was used to conduct GTAW experiments. AISI 1020 steel blank was used as a substrate; UTP AF Ledurit 60 and UTP AF Ledurit 68 were used as the materials of two tubular wires. Further, an artificial neural network (ANN) was used to simulate the GTAW process and then combined with a genetic algorithm (GA) to determine welding parameters that can provide an optimal weld. In welding experiments, five different welding current levels, welding speed, distance to the nozzle, angle of movement, and frequency of the wire feed pulses were used. Using GA, optimal welding parameters were determined: welding current = 222 A, welding speed = 25 cm/min, nozzle deflection distance = 8 mm, travel angle = 25°, wire feed pulse frequency = 8 Hz. The determination coefficient (R2) and RMSE value of all response parameters are satisfactory, and the R2 of all the data remained higher than 0.65.

Item Type:	Article
Additional Information:	Funding Information: G. Pintaude acknowledges CNPq for the grant through Project no. 308416/2017-1. Authors thank IFSC through Application no. 05/2015/PROPPI. They also thank CMCM?UTFPR for the facilities in the characterization of materials. Funding Information: G. Pintaude acknowledges CNPq for the grant through Project no. 308416/2017-1. Authors thank IFSC through Application no. 05/2015/PROPPI. They also thank CMCM–UTFPR for the facilities in the characterization of materials. Publisher Copyright: © 2021, The Author(s).
Uncontrolled Keywords:	/dk/atira/pure/subjectarea/asjc/2200/2207
Departments or Groups:
Depositing User:	Admin SSL
Date Deposited:	19 Oct 2022 23:12
Last Modified:	11 Aug 2023 01:35
URI:	http://repository-testing.wit.ie/id/eprint/4626

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