| publication name | Exercising hybrid statistical tools GA-ANN and GA-ANFIS to optimize underwater friction stir welding process parameters for tensile strength improvement, |
|---|---|
| Authors | Ibrahim Sabry |
| year | 2021 |
| keywords | underwater friction stir welding, ANN-GA, ANFIS-GA, RSM-GA, tensile strength |
| journal | Proceedings of the 11th International Conférence on Engineering, Project, and Production Management EPPM 2021, September 2021, Online. |
| volume | Not Available |
| issue | Not Available |
| pages | Not Available |
| publisher | Not Available |
| Local/International | Local |
| Paper Link | https://eppm2021.pb.edu.pl/app/uploads/2021/09/Book-of-Abstracts-EPPM-2021-17.09.2021-www.pdf#page=59 |
| Full paper | download |
| Supplementary materials | Not Available |
Abstract
This work investigates the tensile strength (σUTS) of tests ASTM D3039 specified parts manufactured using UWFSW by Al 6082- T6 material. Three parameters were varied in the fabrication of test specimens: rotational speed from 1000 to 1800 rpm, traveling speed from 4 to 10 mm/s, and shoulder diameter from 10 to 20 mm. Using a polynomial fitting model of second-order, hybrid optimization methodologies such as artificial neural network- genetic algorithm (ANN-GA), and adaptive neuro fuzzy interface framework – genetic algorithm – (ANFIS-GA) are also used to optimise these process parameters. ANN-GA achieved the highest precision of 98.99 %, resulting in optimum parameters like rotational speed 1800 rpm, travelling speed 4 mm/s, and shoulder diameter 15 mm to produce a maximum tensile strength of 199.0212 MPa. The hybrid models developed could be used to predict and maximise specific process parameters and impacts for a variety of industrial situation