فهرست مطالب مهندس حامد مظفری

In this study, 1.5vol.% of SiC nanoparticles was added to AZ31 magnesium alloy via a stir-casting method. Next, the as-cast ingots were extruded at 400°C with the ratio of 3.78. After extruding, the materials were subjected to multidirectional forging (MDF) at 320°C for 2, 4, 6, and 8 passes. In order to evaluate the mechanical properties of extruded and MDFed materials, shear punch (SPT) and Vickers microhardness tests were applied. The results of these tests showed that hard ceramic nanoparticles improved the shear strength and hardness of the matrix alloy. The shear yield strength, ultimate shear strength, and hardness of extruded alloy were 86.70 MPa, 119.43 MPa, and 52.55 HV, respectively, while in extruded AZ31/SiCp nanocomposite, these values increased by 9.91%, 5.48%, and 13.99%, respectively. It was also observed that nanocomposites processed with multi-directional forging offer better mechanical properties than non-MDFed materials. The results indicated that after the first two passes, there was a significant improvement in the mechanical properties of the nanocomposites, such that the shear yield strength, ultimate shear strength, and hardness were improved in contrast with the extruded state by 27.12%, 17.95%, and 16.03%, respectively. Mechanical properties during the next passes were periodically reduced and increased. Microstructural observations also showed that the average grain size variations were periodic during the increase of MDF passes. After the second pass, the grains were finer than the extruded state, and their size increased in the next two passes. From 4th to 6th pass, the grain size decreased and the smallest grains were obtained in this case, while in the last two passes, the grains grew slightly. Despite the smaller and homogeneous structure created by the 6th and 8th passes, the best mechanical properties were obtained in the second pass, which means, in addition to the microstructural changes, also modifications in the material texture during the MDF process had an impact on mechanical properties.

Regarding the growing development of traffic perception systems, advanced driver assistance systems play a significant role in improving automotive safety. They should be able to guide intelligent vehicles through complicated driving scenarios. The complex nature of the driving process results in complicated control engineering methods. Modeling driver behavior based on psychological concepts would simplify the driving logic and human-machine interaction. In this research, psychological concepts and tire force limitations are formulated based on vehicle kinematics and kinetics as a function of speed and curvature. A multi-objective cost function is defined based on psychological concepts and tire force limits. The speed and the curvature, at which the cost function is minimal, are selected as the decided values. Saturated proportional controllers set the vehicle speed and path curvature on the decided values by adjusting the steering angle of the front wheels, accelerator pedal position, and brake force. The model performance is evaluated by a complicated driving scenario, which includes travelling in the same and opposite directions, presence of obstacles with different sizes and speeds, and high curvature paths. The model avoids face-to-face collisions with a time-to-collision close to 0.72 s. Also, it can avoid obstacles in tight spaces as narrow as 30 cm. Simulation results indicate that the proposed driver model performs safely at the presence of moving obstacles and tight spaces