This study has arisen from the necessity to manufacture an impeller and a shaft pair of a water pump from two different types of steels. The impeller has to have resistant to corrosion and the shaft has to have magnetic permeability property. It is deemed suitable to use the AISI 316 austenitic stainless steel performing high resistance corrosion for the impeller and Ck 45 carbon steel with magnetic permeability property for the shaft. The joining of AISI 316 and Ck 45 steels has been achieved by using the friction welding method. After welding process, the tests of tensile and micro hardness were applied, the microstructures were observed, and composition of elements was found in the welding zone. As optimum welding parameters, 100 MPa of friction pressure, 10 s of friction time, 200 MPa of upset pressure and 20 s of upset time were determined in 3000 rev/min rotation speed.
The optimization of the friction welding parameters through experimental studies does not only cause loss of time and materials but also increases the cost. In this study, an Artificial Neural Network (ANN) model is developed for the analysis of the correlation between the friction welding parameters and tensile strength of both AISI 316 austenitic-stainless steel and Ck 45 steel. The input parameters of the model are friction time, friction pressure and upset pressure while tensile strength is the output. Experimental data are used to train and test the neural network. A good correlation was obtained between the experimental values and the ANN model prediction (R 2 = 0.9711). By using this model, the number of experiments to obtain optimal parameters of friction welding and number of tensile tests could be minimized. K e y w o r d s : friction welding, AISI 316 stainless steel, Ck 45 steel, mechanical properties, Artificial Neural Network (ANN)
The joining of dissimilar metals is one of the most essential necessities of industries. Manufacturing by the joint of alloy steel and normal carbon steel is used in production, because it decreases raw material cost. The friction welding process parameters such as friction pressure, friction time, upset pressure, upset time and rotating speed play the major roles in determining the strength and microstructure of the joints. In this study, response surface methodology (RSM), which is a wellknown design of experiments approach, is used for modeling the mathematical relation between the responses (tensile strength and maximum temperature), and the friction welding parameters with minimum number of experiments. The results show that RSM is an effective method for this type of problems for developing models and prediction.
The microstructural characteristics of friction welded AISI 316 stainless steel samples in a welding zone and a heat affected zone were investigated. Inhomogeneous plastic deformation occurred due to friction welding. Individual grains in the final microstructure underwent various evolution mechanisms. These were caused by the growth of the initially recrystallized grains or as a result of the dynamic recrystallization of the sub-grains formed. The grains within the welding zone and the heat affected zone exhibited different densities of dislocations and experienced various degrees of recovery. Using reasonable estimates of the strain, strain rate and temperature of the friction welding, the dependence of the dynamic recrystallization grain size was found to have the same dependence on the Zener-Hollomon parameter as material deformed via a conventional hot working process.
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