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1.安徽理工大学 人工智能学院,合肥 231131
2.淮南师范学院 机械电气工程学院,淮南 232038
WANG Zhihui, E-mail: wangzhihui0424@163.com
Received:24 June 2025,
Revised:2025-08-13,
Published:15 March 2026
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李康康,王智慧,朱梓豪,等. 基于改进PSO算法的多维振动铸件分拣平台设计与调姿优化[J]. 机械强度,2026,48(3):68-76.
LI Kangkang,WANG Zhihui,ZHU Zihao,et al. Design and posture adjustment optimization of a multi-dimensional vibration casting sorting platform based on an improved PSO algorithm[J]. Journal of Mechanical Strength,2026,48(3):68-76.
李康康,王智慧,朱梓豪,等. 基于改进PSO算法的多维振动铸件分拣平台设计与调姿优化[J]. 机械强度,2026,48(3):68-76. DOI: 10.16579/j.issn.1001.9669.2026.03.008.
LI Kangkang,WANG Zhihui,ZHU Zihao,et al. Design and posture adjustment optimization of a multi-dimensional vibration casting sorting platform based on an improved PSO algorithm[J]. Journal of Mechanical Strength,2026,48(3):68-76. DOI: 10.16579/j.issn.1001.9669.2026.03.008.
目的
2
针对传统铸件分拣平台运动自由度受限及调姿精度不足的问题,设计一种多维振动调姿平台并提出相应的优化算法。
方法
2
首先,研发了一种具有拨动机构的创新结构,并引入多维振动激励模块以实现铸件位姿调整;其次,提出了一种自适应动态调整权重的改进粒子群优化(Improved Particle Swarm Optimization
IMPSO)算法,通过引入自适应干扰力和动态权重机制,提升了算法的收敛性能;再次,建立了以位置和姿态偏差为目标的适应度函数,通过Matlab软件仿真对比了IMPSO算法与粒子群优化(Particle Swarm Optimization
PSO)、遗传粒子群优化(Genetic Algorithm-Particle Swarm Optimization
GA-PSO)、混沌粒子群优化(Chaotic Particle Swarm Optimization
CPSO)算法的性能;最后,搭建了样机试验平台进行实测验证。
结果
2
仿真结果表明,相比于传统算法,IMPSO算法的迭代收敛速度提升了10%~27%;样机试验显示,平台对铸件的位置与姿态调整最大偏差分别控制在0.396 74 cm与0.244 13°以内,能够满足复杂工业场景下的高精度柔性分拣需求。
Objective
2
To address the issues of limited degrees of freedom and insufficient accuracy in traditional casting sorting platforms
a multi-dimensional vibration posture adjustment platform and an associated optimization algorithm are proposed.
Methods
2
Firstly
an innovative toggle mechanism and a multi-dimensional vibration excitation module were developed to achieve flexible posture adjustment. Secondly
an improved particle swarm optimization (IMPSO) algorithm was proposed
incorporating adaptive interference forces and dynamic weight adjustment to enhance global search capabilities. Then
a fitness fuction targeting position and posture deviations was established
comparative simulations between IMPSO
particle swarm optimization (PSO)
genetic algorithm-particle swarm optimization (GA-PSO)
and chaotic particle swarm optimization (CPSO) were performed using Matlab. Finally
a physical prototype was constructed to verify the performance through experimental tests.
Results
2
The results show that the IMPSO algorithm improves the convergence speed by 10% to 27% compared with other algorithms. Prototype tests demonstrate that the maximum position and orientation deviations are controlled within 0.35 cm and 0.25°
respectively. This study provides a theoretical and technical reference for high-precision flexible sorting in complex industrial scenarios.
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