scholarly journals Artificial neural networks application for modeling of friction stir welding effects on mechanical properties of 7075-T6 aluminum alloy

2015 ◽  
Vol 103 ◽  
pp. 012034 ◽  
Author(s):  
E Maleki
Keyword(s):  
Mechanical Properties ◽  
Neural Networks ◽  
Aluminum Alloy ◽  
Artificial Neural Networks ◽  
Friction Stir Welding ◽  
Friction Stir ◽  
Artificial Neural

Prediction of Ferrite-Martensite Dual-Phase Steels Mechanical Properties by Use of Artificial Neural Networks

2013 ◽  
Vol 773-774 ◽  
pp. 268-274
Author(s):  
Amir Ghiami ◽  
Ramin Khamedi
Keyword(s):  
Mechanical Properties ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Back Propagation ◽  
Total Elongation ◽  
Ultimate Tensile Stress ◽  
Dual Phase ◽  
Dual Phase Steels ◽  
Good Ability ◽  
Artificial Neural

This paper presents an investigation of the capabilities of artificial neural networks (ANN) in predicting some mechanical properties of Ferrite-Martensite dual-phase steels applicable for different industries like auto-making. Using ANNs instead of different destructive and non-destructive tests to determine the material properties, reduces costs and reduces the need for special testing facilities. Networks were trained with use of a back propagation (BP) error algorithm. In order to provide data for training the ANNs, mechanical properties, inter-critical annealing temperature and information about the microstructures of many specimens were examined. After the ANNs were trained, the four parameters of yield stress, ultimate tensile stress, total elongation and the work hardening exponent were simulated. Finally a comparison of the predicted and experimental values indicates that the results obtained from the given input data reveal a good ability of the well-trained ANN to predict the described mechanical properties.

Microhardness and Microstructural Studies on Underwater Friction Stir Welding of 5052 Aluminum Alloy

2017 ◽  
Author(s):  
Srinivasa Rao Pedapati ◽  
Dhanis Paramaguru ◽  
Mokhtar Awang
Keyword(s):  
Mechanical Properties ◽  
Aluminum Alloy ◽  
Friction Stir Welding ◽  
Plate Thickness ◽  
Base Material ◽  
Friction Stir ◽  
Underwater Friction Stir Welding ◽  
Void Area ◽  
Average Size ◽  
Microstructural Studies

As compared to normal Friction Stir Welding (FSW) joints, the Underwater Friction Stir Welding (UFSW) has been reported to be obtainable in consideration of enhancement in mechanical properties. A 5052-Aluminum Alloy welded joints using UFSW method with plate thickness of 6 mm were investigated, in turn to interpret the fundamental justification for enhancement in mechanical properties of material through UFSW. Differences in microstructural features and mechanical properties of the joints were examined and discussed in detail. The results indicate that underwater FSW has reported lower hardness value in the HAZ and higher hardness value in the intermediate of stir zone (SZ). The average hardness value of underwater FSW increases about 53% greater than its base material (BM), while 21% greater than the normal FSW. The maximum micro-hardness value was three times greater than its base material (BM), and the mechanical properties of underwater FSW joint is increased compared to the normal FSW joint. Besides, the evaluated void-area fraction division in the SZ of underwater FSW joint was reduced and about one-third of the base material (BM). The approximately estimated average size of the voids in SZ of underwater FSW also was reduced to as low as 0.00073 mm2, when compared to normal FSW and BM with approximately estimated average voids size of 0.0024 mm2 and 0.0039 mm2, simultaneously.

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