Staff Members - Benha University
Exercising hybrid statistical tools GA-ANN and GA-ANFIS to optimize underwater friction stir welding process parameters for tensile strength improvement,
Proceedings of the 11th International Conférence on Engineering, Project, and Production Management EPPM 2021, September 2021, Online. • 2021
Publication Information
Authors
Ibrahim Sabry
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.
Publisher
Not Available
Volume
Not Available
Issue
Not Available
Pages
Not Available
publication.type
Local
Paper Link
Open Link
Supplementary Materials
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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
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