This paper aims to explore the adaptability of the SPH-FEM coupling method in simulating gear and rack meshing impacts. Taking the gears and racks of a pumping unit as the research object, the adaptive SPH-FEM coupling method is used to simulate gear and rack meshing impacts. This provides a new approach for studying the impact of gear mechanisms that may involve structural damage. The propagation law of the impact stress is obtained, which reveals the microscopic mechanism of formation of the tooth-face impact wear and deduces that the metal debris generated in the meshing impact process is the main reason for the appearance of tooth surface defects. The results show that the peak residual stresses in section I and section II are 6.694 & times;10(2) MPa and 2.935 & times;10(2) MPa, respectively, after impacting for 0.6 ms, and the error of micro-experiment verification is very small, which indicates that the method is effective.
This study investigated the interaction of flow fields between two adjacent petrochemical heaters under the action of strong wind. The effect of flow field interaction on the force and stress distribution are analyzed for four relative positions of the two heaters. The analysis results reveal that the relative position of the heaters has a significant influence on the stress distribution. When the relative position of the two heaters changes, the pressure and vortex distribution through the heaters also change, and the pressure and friction forces acting on the heaters are affected, too. For heater A, when it is located behind heater B, heater A is subjected to the lowest stress and all monitored positions are safe without the risk of fatigue failure. When heater A is located to the right of heater B, heater A is subjected to the greatest stress and 23 of the 50 monitored stresses exceed the fatigue limit (75 MPa). On the other hand, for heater B, when heater A is located ahead of heater B (i.e., heater B is located behind heater A), the stress on heater B is the lowest and all monitored positions are safe without the risk of fatigue failure. When heater A is located to the right of heater B, heater B is subjected to the greatest stress and 9 of the 48 monitored stresses exceed the fatigue limit (75 MPa). When the smaller heater B is located behind the larger heater A, the pressure resistance of the rear heater B is smaller than that of the rear heater A when the larger heater A is located behind the smaller heater B. When heater A is located behind the right of heater B, the pressure resistance is greater than the case of one heater located behind or ahead of the other and less than the case of heater A located at the right of heater B. Further, when heater A is located behind the right of heater B, the stress on heater B is smaller than that on heater A.
This study focuses on the finite element analysis of the residual mechanical properties of 316 EN 1.4401 cold-worked austenitic stainless steel reinforcing bars, compared to experimental results. The study assessed the influence of user-defined material properties on an isotropic elasticity model and a multilinear isotropic hardening model, evaluated under different conditions to validate against experimental observations. This study demonstrated a strong correlation between experimental results and numerical simulations, showing only slight differences in the residual mechanical response across various cooling methods. The investigation revealed a high correlation between total maximum strain (mm/mm) and user-defined (the total elastic and plastic strain). The variation in ultimate tensile strength (f(u)) between the experimental and numerical results is less than 2% except for one case which involving slow cooling inside furnace at 500 degrees C (2.5%). Likewise, the difference in 0.2% proof strength (f(0.2p)) is below 2.5%, except for three cases involving rapid water cooling at 500 degrees C (3.2%) and intermediate air cooling at 500 degrees C (3.3%) and 500 degrees C (3.8%) respectively. Therefore, the numerical methods can enhance the precision of experimental results for assessing structural characteristics.
The current electronic differential control strategy based on the Ackermann steering model only constrains the speed of the driving wheels and does not consider dynamic performance. This paper is based on wheel hub electric vehicles and focuses on the turning driving conditions of the vehicle. From the perspective of improving the stability of the vehicle's steering control, innovative electronic differential control methods based on sliding mode control and fuzzy control are designed. Taking the vehicle's lateral angular velocity and center of mass side slip angle as control objectives, the vehicle is driven at low speeds through double lane change. The control performance of the two designed electronic differentials is compared and verified with the electronic differential control strategy that evenly distributes the total driving torque. The results indicate that overall, sliding mode control has the best performance, maintaining excellent differential performance under various operating conditions. Based on fuzzy control, there is a certain degree of degradation in electronic differential performance during high-speed driving conditions. The electronic differential control system based on the average distribution of total expected torque can also achieve differential driving of vehicles, but its effect is not as good as the other two.
High-speed robotic operations often generate significant vibrations and trajectory deviations, compromising precision and efficiency in industrial automation. This study proposes an advanced motion control strategy integrating B-Spline trajectory planning with the L-BFGS-B optimization algorithm and systematic tuning of CNT and Override parameters. Experimental validation involved a six-axis robotic arm executing a standard 90-degree corner path and a complex multi-turn trajectory. Results indicate optimal parameter tuning effectively balances vibration suppression and positional accuracy, maintaining vibration magnitudes between 0.75 and 1.25 G. Utilizing two B-Spline control points significantly reduces average positional error from 27.68 mm to 7.52 mm, with a minimal operation time increase of 0.15 seconds. Compared to conventional methods, the proposed approach achieves a 12% reduction in execution time and a 35% reduction in positional errors, highlighting its efficacy and substantial potential to enhance automated manufacturing systems, precision machining tasks, and advanced robotic motion planning applications.
