| People | Locations | Statistics |
|---|---|---|
| Naji, M. |
| |
| Motta, Antonella |
| |
| Aletan, Dirar |
| |
| Mohamed, Tarek |
| |
| Ertürk, Emre |
| |
| Taccardi, Nicola |
| |
| Kononenko, Denys |
| |
| Petrov, R. H. | Madrid |
|
| Alshaaer, Mazen | Brussels |
|
| Bih, L. |
| |
| Casati, R. |
| |
| Muller, Hermance |
| |
| Kočí, Jan | Prague |
|
| Šuljagić, Marija |
| |
| Kalteremidou, Kalliopi-Artemi | Brussels |
|
| Azam, Siraj |
| |
| Ospanova, Alyiya |
| |
| Blanpain, Bart |
| |
| Ali, M. A. |
| |
| Popa, V. |
| |
| Rančić, M. |
| |
| Ollier, Nadège |
| |
| Azevedo, Nuno Monteiro |
| |
| Landes, Michael |
| |
| Rignanese, Gian-Marco |
|
Gupta, Munish
Opole University of Technology
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (6/6 displayed)
- 2023A state of the art on surface morphology of selective laser-melted metallic alloyscitations
- 2023Wear performance of Ti-6Al-4 V titanium alloy through nano-doped lubricantscitations
- 2022A state-of-the-art review on mechanical characteristics of different fiber metal laminates for aerospace and structural applicationscitations
- 2021Investigations on quality characteristics in gas tungsten arc welding process using artificial neural network integrated with genetic algorithm
- 2021Establishing the relationship between cutting speed and output parameters in belt grinding on steels, aluminum and nickel alloys: development of recommendationscitations
- 2020A review of indirect tool condition monitoring systems and decision-making methods in turning: critical analysis and trendscitations
Places of action
| Organizations | Location | People |
|---|
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.