温锻压力机肘杆机构的自适应粒子群算法优化

Optimization on elbow-bar mechanism for warm forging press based on adaptive particle swarm algorithm

为了改善温锻压力机肘杆机构的下压性能、提高产品质量,提出了肘杆机构的自适应粒子群算法优化方法。使用封闭矢量法建立了传动机构的运动学模型。以6连杆尺寸为优化参数,以滑块最大运动速度、最大加速度和曲柄最大输出扭矩为优化目标,建立了优化模型,根据曲柄存在条件、上连杆摆角约束、滑块行程约束等设置了约束条件。在粒子群算法的基础上,提出了算法参数随粒子适应度和算法迭代次数的自适应变化律,给出了基于自适应粒子群算法的优化模型求解方法。经过优化,在公称压力行程范围内,滑块的最大运动速度降低了1.87%,最大加速度降低了3.13%,曲柄最大输出扭矩降低了1.10%,改善了肘杆机构的下压性能,同时证明了自适应粒子群算法在传动机构参数优化中的有效性。

In order to improve the pressure performance of elbow-bar mechanism in warm forging press and the quality of products,the optimization method of elbow-bar mechanism was proposed based on adaptive particle swarm algorithm,and the kinematics model of transmission mechanism was established by closed vector method. Then,taking the sizes of six links as the optimizing parameters and taking the maximum movement speed and the maximum accelerate of slider,as well as the maximum output torque of crank as the optimization goals,the optimization model was built,and the constraint conditions were set based on crank existing condition,upper link swing angle constraint and slider stroke constraint. On the basis of particle swarm algorithm,the adaptive change law of algorithm parameters with particle fitness and iteration number of algorithm was proposed,and the optimization model solution method was provided by adaptive particle swarm algorithm. The optimization results show that within the range of nominal pressure stroke,the maximum movement speed and the maximum accelerate of slider as well as the maximum output torque of crank decrease by 1. 87%,3. 13% and 1. 10% respectively to improve the pressure performance of elbow-bar mechanism,and the effectiveness of adaptive particle swarm algorithm in the parameter optimization of transmission mechanism is also proved.