Hinges are essential components in mechanical devices, allowing for movement and rotation. While various types of hinges have been widely used in industries, such as rotary hinges, Hooke hinges, spherical hinges, hydraulic cylinders, and ball screw nut pairs, they still have certain limitations. For instance, under heavy loads, traditional hinges need to be thick in order to meet rigidity requirements. Additionally, in special cases where space is limited and loads are large, traditional hinges may struggle to fulfill their function.
As a result, there has been growing interest in researching new hinge designs. Particle swarm optimization (PSO) algorithm, a type of swarm intelligence algorithm, has gained significant development and application in engineering fields. This algorithm utilizes the behavior of bird groups flying for food to achieve optimal solutions in complex spaces through collaboration and competition among individuals. PSO algorithms are highly efficient, easy to implement, and extensively used in engineering practice. The basic process of a PSO algorithm includes initialization, particle flight, and result determination. The algorithm starts by randomly generating an initial population of particles, which move within the feasible region. By calculating the fitness value of each particle, the algorithm determines the new movement direction and speed of each particle. During each round of particle movement, the optimal particle and the historical optimal particle have a greater influence on the next round of motion. After multiple iterations, the algorithm obtains the optimal solution.
The convergence performance of the PSO algorithm has been improved by introducing inertia weights, as proposed by Shi and Eberhart. The particle evolution equation involves several components, including inertia, cognition, and social cooperation. The algorithm parameters, such as the particle speed and number of iterations, can be adjusted based on specific requirements. PSO algorithms have become a widely used intelligent optimization algorithm in engineering applications and often outperform genetic algorithms. However, PSO algorithms still face challenges, such as premature convergence. Therefore, there has been significant research dedicated to improving the PSO algorithm and addressing its limitations.
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In the context of hinge design, the project requirements include a load capacity of 3 tons and a rotation angle of ±90 degrees, with dimensions not exceeding 2000 mm x 500 mm x 1000 mm. To meet these requirements, the 2rpr mechanism is selected as the hinge mechanism. This mechanism consists of a rotating pair and a moving pair, offering high rigidity, error adjustment, and compensation capabilities. Additionally, the mechanism is symmetrical, enabling easy installation and maintenance.
During the optimization design process, the rotation angle and size requirements are met by applying geometric constraints. However, the key challenge lies in ensuring the mechanism has excellent force transmission capabilities. This is typically achieved by setting a minimum transmission angle for the mechanism.
To analyze the force transmission, the rod CE is selected as the analysis object. Assuming a load mass of m and a distance of d between its center of mass and the rotation pair, the force exerted on the rod CE is examined. By considering the angles between the forces and the rod CE, as well as the angle between the rod and the X-axis, a force balance equation is derived. This equation ensures the mechanism's force transmission capabilities.
Based on the analysis results, the moving pair is designed accordingly. An electric cylinder model, GSX40-1201, is preliminarily selected, taking into account stroke, thrust, and axial dimensions. Other factors, such as component size, are also considered in the final design. Sliding bearings made of aluminum bronze are chosen for each rotating pair, considering their high load-bearing capacity and precision requirements. The main components are made of 35CrMnSiA alloy steel, which offers high tensile strength and elastic modulus.
Upon completion of the mechanical design, a CAD model is established to visualize the final design. The particle swarm optimization algorithm has successfully optimized the design of the large-rotation-angle heavy-duty hinge, ensuring it meets all the design requirements.
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In conclusion, the particle swarm optimization algorithm has proven effective in optimizing the design of a large-rotation-angle heavy-duty hinge. Through careful configuration and analysis, the optimal design of the 2rpr mechanism was achieved. The mechanical design, including the selection of components and materials, was successfully completed. The CAD model provides a visual representation of the final design. Overall, particle swarm optimization algorithms offer a valuable tool for the efficient and effective design of hinges and contribute to improving the performance and functionality of mechanical devices in various industries.
References:
1. Wei Minhe, Han Xianguo, Zhang Jun. Optimization research on 3-UPS/S parallel billiard ball hinge [J]. Aerospace Manufacturing Technology, 2011 (3): 19-23.
2. Chen Lishun, Li Li, Zhang Hongliang. Joint design of new super-redundant robotJ. Mechanical Design and Manufacturing, 2010 (6): 148-150.
3. Yang Shun, Cai Anjiang. Optimal Design of Electronic Accelerator Pedal Voltage Adjustment Parameters Based on RBF and Particle Swarm Optimization Algorithm [J]. Mechanical Design and Manufacturing, 2011 (1): 72-74.
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