Friction stir welding (FSW) is the most widely used solid-state joining method for sheet and plate-like materials, valued for being a highly adaptable, environmentally sound, and energy-efficient process. The literature has repeatedly shown that FSW is a reliable joining technique for high-demand technological applications, particularly for high-strength aluminum and titanium alloys used in aerospace, which are difficult to join with traditional fusion welding methods. To describe the microstructural changes caused by solid-state FSW, many studies analyze mechanical parameters of FSW joints, including tensile strength, bending, torsion, elasticity, and fatigue responses. In recent years, the push to expand FSW's use, broaden the range of compatible alloy systems, and improve the resulting mechanical properties has led to significant advancements in this joining method. Accordingly, this review study provides a comprehensive investigation into the methodology and the materials relevant to this specific field.

The robot arms often follow a certain trajectory depending on the type of end effector with functions of spray-painting, arc welding, bonding or machining etc. Therefore, trajectory-tracking control is a very important issue in robot arm applications. Also, the robot must be able to follow the determined trajectory stably under the influence of external forces or machining forces it encounters in its operations. In this study, a Model Predictive Control (MPC) for trajectory tracking control of a 2 Degrees of Freedom (DoFs) Selective Compliant Assembly Robot Arm (SCARA) under an external force acting to the tip of the robot along the trajectory was performed. The effectiveness of the MPC method used has been demonstrated by simulation applications. According to simulation studies, successful results were obtained.

This study investigates the vibrational characteristics of multi-layered graphene sheets with variable thickness (VTGSs) by using molecular dynamics (MD) simulations. It is aimed to determine how the natural frequencies and vibration damping ratios of variable-thickness graphene change with respect to temperature. Atomistic models for six distinct geometries (1L, 3LT, 3LTB, 5LT, 5LTB, and 9LTB) were generated to analyze the influence of structural design and temperature on their natural frequencies. The simulations were performed using the Large-Scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) with an AIREBO potential to represent interatomic carbon interactions. Natural frequencies of all atomistic models were extracted by applying the Fast Fourier Transform (FFT) method to the Velocity Autocorrelation Function (VACF) data obtained from the simulations. In addition, the analysis was conducted at three different temperatures: 250 K, 300 K, and 350 K. Key findings reveal that an increase in the number of graphene layers results in a decrease in the fundamental natural frequency due to the increased mass of the structure. Moreover, it was noted that natural frequencies decrease with increasing temperature. It is attributed to the reduction in structural rigidity at higher thermal energies. These results provide critical insights into how geometric and thermal variations affect the dynamic behavior of complex multi-layered graphene structures.

This study comprehensively investigates the dynamic vibration behavior of multilayer carbon nanotubes with stepped cross-sectional geometries under various boundary conditions, which is crucial for their advanced engineering applications. The methodology integrates classical molecular dynamics simulations to determine the bending stiffness of single-walled and multi-walled atomistic structures, which are subsequently utilized in the Euler–Bernoulli beam theory based on nonlocal elasticity for vibration analysis. The research focuses on elucidating the influence of the μ/L ratio (a key length parameter) and different support conditions on the natural frequencies and mode shapes of these nanostructures. Key findings reveal that the cross-sectional geometry significantly impacts the vibrational characteristics. A consistent trend observed across all examined boundary conditions is a decrease in natural frequencies as the μ/L ratio increases, indicating that increased free length or reduced fixed length leads to lower stiffness and, consequently, reduced natural frequencies. The study presents Frequency Response Functions (FRFs) and the first four mode shapes, which visually confirm these dynamic characteristics. Graphical representations further reinforce the sensitivity of natural frequencies to both the μ/L ratio and support conditions. The systematic analysis presented in this work provides vital data for predicting resonance phenomena, optimizing structural stability, and enabling precise control over the vibrational response of these advanced nanomaterials in diverse engineering applications.

In this study, the hardware design, manufacturing, and control of a Two-Wheeled Inverted Pendulum Manipulator (TWIPM) are successfully achieved. The system comprises a two-degree-of-freedom independently driven chassis integrated with a three-degree-of-freedom robotic manipulator. To enable the robot to navigate and reach designated target coordinates on inclined terrain, an autonomous trajectory planning scheme is implemented using the A* algorithm, and the system’s mathematical model is comprehensively updated. To ensure balance stability and enhance robustness against external perturbations, a PI-based supplementary control architecture is proposed to support the core PID controllers governing wheel angular position and chassis tilt. The efficacy of the proposed control strategy is initially validated via numerical simulations under various reference trajectories and disturbance inputs on flat surfaces. Subsequently, experimental validations conducted on a physical testbed featuring an inclined ramp demonstrate the robot’s autonomous trajectory tracking and precise trajectory tracking positioning capabilities, confirming its real-world viability. Finally, future research directions are outlined, focusing on end-effector position optimization, the integration of a dynamic payload estimator under varying weights, and the fully onboard execution of navigation algorithms on the central microcontroller.

This study investigated the dynamic behavior of AA7075 plates joined by Friction Stir Welding (FSW), focusing on the influence of key process parameters, rotation, and traverse speeds, on the resulting dynamic characteristics. Experimental Modal Analysis (EMA) was performed under free boundary conditions to determine resonance frequencies, mode shapes, and damping ratios, revealing that an increase in traverse speed consistently led to a decrease in natural frequencies across most modes, thereby indicating reduced joint stiffness attributed to insufficient heat input. Furthermore, localized weld defects caused significant damping variations, particularly in low-order modes. To complement the experimental findings and enable simultaneous, multi-output prediction of these coupled dynamic parameters, a Co-Active Neuro-Fuzzy Inference System (CANFIS) model was developed. The CANFIS architecture utilized spindle speed and feed rate as inputs to predict natural frequency and damping ratio for multiple vibration modes as tightly coupled outputs. The trained model demonstrated strong agreement and high predictive accuracy against the EMA experimental data, with convergence analysis confirming its stable learning and excellent generalization capability. The successful integration of EMA and CANFIS establishes a robust hybrid framework for both physical interpretation and intelligent, coupled prediction of the dynamic behavior of FSW-welded AA7075 plates.