أعضاء هيئة التدريس - جامعة بنها
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
معلومات البحث
المؤلفون
Ibrahim Sabry
الكلمات المفتاحية
underwater friction stir welding, ANN-GA, ANFIS-GA, RSM-GA, tensile strength
المجلة العلمية
Proceedings of the 11th International Conférence on Engineering, Project, and Production Management EPPM 2021, September 2021, Online.
الناشر
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المجلد
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العدد
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الصفحات
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publication.type
Local
رابط البحث
Open Link
المواد المرفقة
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الملخص
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