The effect of parameters involved in prediction of mechanical properties of Friction Stir Welding have been investigated in this study through modeling. For this purpose, the friction stir welding on both sheets of 5083 Aluminum (Al5083) and pure Copper (Cu) was experimentally conducted at first level. Three factors of Rotation Speed (RPM), Traverse Speed (mm/min) and the Tool’s pin angle was examined. During the tentative test, many experiments were not satisfactorily performed. Thanks to a more rigorous study hypothesis, proper samples were obtained by changing the geometry of different tools. In order to overcome the softness of aluminum sheets and the rise of shoulder diameter, and also to avoid the vertical instability of the sheet, shortening the pin’s length was suggested. In this paper, the Full Factorial method has been employed to evaluate the result of Artificial Neural Networks (ANN), Imperialist Competitive Algorithm, Particle Swarm Optimization, and also the effects of input parameters of the process on output parameters. Moreover, Al5083 and Cu joining sheets were analyzed. Micro-hardness and tensile tests have been based on the process’ input parameters to obtain mechanical properties. The function of ANN model demonstrates that it can estimate the number of mechanical properties with an adequate precision. Using the evaluation factors of mechanical properties and micro-hardness and also a R2 ~ 0.943 analysis, the optimum parameters of Al5083 and Cu joining sheets can be anticipated. The rotation speed of 1150 rpm, traverse speed of 40 mm/min, and pin angle of 2° are the optimum conditions based on the average review of analyses. These optimum conditions led to improve the maximum tensile force up to 15 ~ 21%.

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Forging is one of the oldest and the most important processes of metal forming. The process occurs due to the waxy deformation of metal. In the forging process, the die walls control the material flow and the mechanical properties of the workpiece are significantly improved. Today's world industries, financing costs play a leading role in production. These categories have entered the industry to create a variety of simulation and numerical modeling methods to eliminate this problem. Simulation and experimental test, mechanical properties in the hot forging process in high-cost materials are of great interest to researchers. In this paper, the mechanical properties of hot forging in Ti6Al4V alloy are investigated. According to the obtained results, it can be found that a very close agreement has been made on experiments reported in literature and the simulation. Depending on the results, the strain rate Z-axis showed the shear bands appeared exactly on the 45-degree plates and in the sample center. In fact, the intersection of the band's sample center is the max strain applied to the die. This area has been created with an effective strain and stress at all depths of the workpiece and gradually expands.

The Constrained Groove Pressing (CGP) on annealed Al5083 (Al5083-O) alloy sheets by means of Polymethyl Methacrylate (PMMA) polymer is investigated in this research. Input parameters include Traverse Speed (1, 2, and 3 mm/min), Pass Number (1, 2, 3, and 4), and Lubricants (Grease, Oil, and without lubricants). Reciprocally, output parameters consist of Nanostructure (SEM and EBSD images), Hardness (HV), and Mechanical Properties – Impact Strength (kJ/m2), Tensile Strength (MPa), Young's Modulus (MPa), and Elongation (%). Revealed by the results, the impact strength is gradually improved in the 1st, 2nd, 3rd, and 4th passes by respectively adding oil and grease, compared to the state of using no lubricant. The impact strength results demonstrated that the addition of grease has a significant effect on the impact strength of composite samples. In order to build a high-quality product through the CGP process, it is very important for the mechanical properties to be based on the input parameters. In CGP processes, the Continuous Dynamic Recrystallization (CDRX) is considered as the ultrafine-grained mechanism. To predict the grain size evolution and the plastic deformation rate, the combination of Finite Element (FE) method and the ETMB model is utilized. Indicating the high generalizability and reliability as compared to other modeling methods, both the experimental test results and the ETMB model data, which have a higher degree of accuracy, are presented.

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