Journal of Welding Science

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Today, steel to aluminum joints are used to facilitate transportation and fuel consumption. These joints are applied from nuclear, aerospace and naval to automobile and kitchen industries. According to previous studies fusion welding processes are not suitable methods for these joints, solid-state welding, especially friction stir welding, is a proper way to use for these joints. However, using this method for these two metals needs adequate prediction of temperature distribution and material flow to obtain enhanced joints. In this study, a finite element method is used to predict the temperature distribution. In addition, a computational fluid dynamics solution is coupled with the thermal solution. Therefore, the flow rate, strain rate and dynamic viscosity is obtained. Also, the joint morphology is predicted using the Level Set method. It is shown the material flow depends on flow rate, strain rate and dynamic viscosity and is intensively function of rotational speed. Additionally, offset to the aluminum side improves the morphology of the stir zone.

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In this research, artificial neural network (ANN) and genetic algorithm (GA) were used in order to produce and develop the NiAl intermetallic coating with the best wear behavior and the most value of hardness. The effect of variations of current, voltage and gas flow on the hardness and wear resistance were optimized by ANN and GA. In the following, the optimum  values of current, voltage and gas flow were obtained 90(A), 10(v) and 9 (Lit/min), respectively. Then, the wear behavior in the environment temperature and high temperature for optimized NiAl compound was compared with two other experimental samples.

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Aluminum to stainless steel joints are broadly used in industries in order to reduce fuel consumption. While fusion welding is not a suitable method to join these metals. solid state welding, like friction welding (FW), is an effective way to this process. However, risk of intermetallic compounds (IMCs) formation is probable in these welds. In previews investigations formation of FeAl₃, Fe₂Al₅ and Fe₄Al1₃ is reported. In this study, effect of different parameters on generated heat and temperature distribution that lead to formation of these compounds in a FW of aluminum alloy to stainless steel is investigated using Finite Element Method (FEM). Additionally, a mathematical modeling of the parameters is performed using Artificial Neural Network (ANN) and the optimum level of the parameters has been found.