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In the present study, the authors have attempted to present a novel approach for the prediction, analysis, and optimization of the Friction Stir Welding (FSW) process based on the Deep Neural Network (DNN) model. To obtain the DNN structure with high accuracy, the most focus has been on the number of hidden layers and the activation functions. The DNN was developed by a small database containing results of tensile and hardness tests of welded 7075-T6 aluminum alloy. This material and the production method were selected based on the application in the construction of fishing boat flooring, because on the one hand, it faces the corrosion caused by proximity to sea water and on the other hand, due to direct contact with human food, i.e., fish etc., antibacterial issues should be considered. All the major parameters of the FSW process, including axial force, rotational speed, and traverse speed as well as tool diameter and tool hardness, were considered to investigate their correspondence effects on the tensile strength and hardness of welded zone. The most important achievement of this research showed that by using SAE for pre-training of neural networks, higher accuracy can be obtained in predicting responses. Finally, the optimal values for various welding parameters were reported as rotational speed: 1600 rpm, traverse speed: 65 mm/min, axial force: 8 KN, shoulder and pin diameters: 15.5 and 5.75 mm, and tool hardness: 50 HRC. [ABSTRACT FROM AUTHOR]

Hardened nickel coating is widely used in many industrial applications and manufacturing processes because of its benefits in improving the corrosion fatigue life. It is clear that increasing the coating thickness provides good protection against corrosion. However, it reduces the fatigue life. Thus, applying a thin layer of coated nickel might give an acceptable corrosion protection with minimum loss of the fatigue life. In the present study, the effects of hardened nickel coating with different thicknesses on the fatigue behavior of CK45 mild steel were experimentally investigated. After conducting the experimental tests, we carried out two different modeling approaches of finite element method (FEM) and artificial neural network (ANN). In the FEM modeling, an attempt was made to analyze the fatigue of the components by modeling the interface phase between the base metal and coating more accurately and using the spring elements; ANNs were developed based on the back propagation (BP) error algorithm. The comparison of the obtained results from FEM and ANN modeling with the experimental values indicates that both of the modeling approaches were tuned finely. [ABSTRACT FROM AUTHOR]