• CN:11-2187/TH
  • ISSN:0577-6686

机械工程学报 ›› 2023, Vol. 59 ›› Issue (3): 54-65.doi: 10.3901/JME.2023.03.054

• 机器人及机构学 • 上一篇    下一篇

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正铲液压挖掘机三副摇杆工作机构的超多目标优化设计

徐弓岳1, 冯泽民2, 郭二廓1   

  1. 1. 江苏大学机械工程学院 镇江 212013
    2. 河北工程大学机械与装备工程学院 邯郸 056038
  • 收稿日期:2022-03-18 修回日期:2022-09-14 出版日期:2023-02-05 发布日期:2023-04-23
  • 通讯作者: 郭二廓(通信作者),男,1986年出生,博士,副教授,硕士研究生导师。主要研究方向为齿轮先进制造技术、智能制造技术及装备和工业装备数字化制造技术。E-mail:guoerkuo@163.com
  • 作者简介:徐弓岳,男,1990年出生,博士,讲师。主要研究方向为机构优化设计及计算智能方法。E-mail:xgy_ztt@163.com
  • 基金资助:
    国家自然科学基金(51805225)、中国博士后科学基金(2020M681498)和江苏省博士后科研(2020Z394)资助项目。

Many-objective Optimization Design for TriRocker Working Mechanism of Face-shovel Hydraulic Excavator

XU Gongyue1, FENG Zemin2, GUO Erkuo1   

  1. 1. School of Mechanical Engineering, Jiangsu University, Zhenjiang 212013;
    2. School of Mechanical and Equipment Engineering, Hebei University of Engineering, Handan 056038
  • Received:2022-03-18 Revised:2022-09-14 Online:2023-02-05 Published:2023-04-23

摘要: 为解决液压挖掘机工作机构的超多目标优化难题,以新型三副摇杆正铲挖掘机构为研究对象、开展基于超多目标进化算法的优化设计研究。以三副摇杆工作机构的功能特点和正铲挖掘机的挖掘力、推压力、挖掘图谱指标等性能指标为目标函数建立其约束超多目标优化模型。通过引入自适应旋转模拟二进制交叉算子,提出一种改进的约束超多目标进化算法,增强算法处理复杂约束优化问题的能力,并在标准测试函数集中进行验证。将改进算法应用于70 t级液压挖掘机三副摇杆工作机构优化实例中,并与当前最先进的8种约束多目标进化算法进行比较研究,验证所提出算法的有效性。最后基于理想解法从得到的非支配解集中筛选出满意度最高的三副摇杆工作机构设计方案,并与现有经典机型方案的性能参数进行比较。优化结果表明:提出的算法在挖掘机工作机构优化中相比于其他进化算法具有明显优势,能得到极具竞争力的三副摇杆工作机构优化方案。

关键词: 正铲液压挖掘机, 三副摇杆工作机构, 超多目标优化, 进化算法, 约束优化

Abstract: To solve the many-objective optimization problem of the working mechanism of hydraulic excavator, this paper researched the optimization design of new TriRocker shovel attachment by many-objective evolutionary algorithms. Firstly, the constrained many-objective optimization model of TriRocker working mechanism was established by taking the kinematic characteristics, digging forces, crowding force and the standards of digging map as the objective functions. Then, a modified constrained many-objective evolutionary algorithm was proposed with adaptive rotation-based simulated binary crossover to enhance the ability of dealing with complex constrained optimization problems and verified by the benchmark test functions. Finally, the proposed algorithm was applied in the optimization example of 70 t hydraulic excavator and compared with eight advanced many-objective evolutionary algorithms for further verification. The most satisfactory solution of hydraulic excavator was selected by technique for order preference by similarity to ideal solution. The results show that the proposed algorithm is significantly superior to other compared algorithms and could obtain the optimized scheme of TriRocker hydraulic excavator with market competitiveness.

Key words: face-shovel hydraulic excavator, TriRocker working mechanism, many-objective optimization, evolutionary algorithm, constrained optimization

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