Ultrasonic welding is gaining popularity for joining of thin and dissimilar materials and foils in the fabrication of automotive Li-ion battery packs because of excellent efficiency, high production rate, high welding quality, etc. Precise control of the parameters of the welding process plays an important role in achieving good joint quality. Numerical simulation can greatly help control the main input parameters such as frequency, clamping pressure, friction coefficient, and vibration amplitude. In this present work, a three-dimensional thermo-mechanical Finite Element (FE) model is proposed using ABAQUS/EXPLICIT for the dissimilar Al to Cu weld to predict the deformation and temperature as output parameters during welding process by varying input parameters. The simulation results showed that the clamping pressure, vibration frequency and friction coefficient have a great influence on heat production during the process which was critical to determine the final quality of the welded joint. Studies also showed that increased clamping force and welding frequency led to increased deformation. تفاصيل المقالة

In the present study, an artificial neural network (ANN) model is developed to predict the correlation between the friction stir extrusion (FSE) parameters and the recycled wires’ average grain sizes. FSE is a solid–state synthesis technique, in which machining chips are firstly loaded into the container, and then a rotating tool with a central hole is plunged into the chips at a selected rotational speed and feed rate to achieve indirect extrusion. Selecting rotational speed (RS), vertical speed (VS), and extrusion hole size (HS) as the input and average grain size as the output of the system, the 3–6–1 ANN is used to show the correlation between the input and output parameters. Checking the accuracy of the neural network, R squared value and Root–Mean–Square–Error (RMSE) of the developed model (0.94438 and 0.75794, respectively) have shown that there is a good agreement between experimental and predicted results. A sensitivity analysis has been conducted on the ANN model to determine the impact of each input parameter on the average grain size. The results showed that the rotational speed has more effect on average grain size during the FSE process in comparison to other input parameters. تفاصيل المقالة

In this research, the feasibility of solid-state recycling of pure magnesium (Mg) chips is investigated by applying a synthesis technique called shear consolidation processing (SCP). During the SCP, machining chips are first loaded into the container and slightly compacted, and then a rotating tool with a designated diameter is plunged into the Mg chips at a selected spindle rotation speed and feed rate. Due to the huge amount of heat generation, the softened materials are compressed and synthesized to form a consolidated part eventually. The results show that the SCP process is a feasible solution for producing a void-free consolidated material directly from Mg chips in a single step. The microstructure analysis using optical microscopy (OM) and scanning electron microscopy (SEM) shows a significant grain refinement in the produced part compared with the base material (from around 900 µm to 11 µm). The recycled specimen has a much higher hardness (at least 100% increase) than the parent material and also exhibits better wear resistance. This improvement is attributed to the resulting fine-grained microstructure due to severe plastic deformation during the SCP process. تفاصيل المقالة