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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Sarfraz, Shoaib
University of Birmingham
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (9/9 displayed)
- 2024Evaluation of the influence of dissolved nitrates on corrosion behaviour of ship structural steel exposed to seawater environmentcitations
- 2024Evaluation of the influence of dissolved nitrates on corrosion behaviour of ship structural steel exposed to seawater environmentcitations
- 2021Investigations on quality characteristics in gas tungsten arc welding process using artificial neural network integrated with genetic algorithm
- 2021Investigations on quality characteristics in gas tungsten arc welding process using artificial neural network integrated with genetic algorithmcitations
- 2020Chapter 5: Comprehensive study on tool wear during machining of fiber-reinforced polymeric compositescitations
- 2019Taguchi-based GRA for parametric optimization in turning of AISI L6 tool steel under cryogenic cooling
- 2019Parametric modelling and multi-objective optimization of electro discharge machining process parameters for sustainable productioncitations
- 2019Experimental characterization of electrical discharge machining of aluminum 6061 T6 alloy using different dielectricscitations
- 2017Effect of different dielectrics on material removal rate, electrode wear rate and microstructures in EDMcitations
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document
Investigations on quality characteristics in gas tungsten arc welding process using artificial neural network integrated with genetic algorithm
Abstract
as 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 (R 2 ) and RMSE value of all response parameters are satisfactory, and the R 2 of all the data remained higher than 0.65.