摘要
针对清淤机双举升缸负载敏感系统冲击压力大,进料装置随地面起伏导致双举升液压缸被动伸缩、寿命低等问题,设计了一种并联多蓄能器分流的清淤机双举升缸负载敏感系统。采用AMESim仿真软件建立清淤机双举升缸负载敏感系统的仿真模型,通过对该液压系统进行仿真分析及基于正交试验对液压系统参数进行优化。结果表明:在变负载工况下,分别并联合适规格的蓄能器、合理控制泵排量以及管道长度,清淤双举升缸负载敏感液压系统工作较稳定、主阀最大冲击压力由原始的37.68MPa降低到12.84MPa,举升液压缸液压冲击由原始的4.2MPa降低到2.05MPa,有效的降低了液压冲击及脉动现象,提高了换向阀及液压缸的使用寿命,为清淤机液压系统设计提供了参考。
Aiming at the problems of large impact pressure of double lift cylinder load sensitive system of dredging machine,passive expansion of double lift hydraulic cylinder caused by fluctuation of feeding device with ground and low service life,a parallel multi-accumulator shunt double lift cylinder load sensitive system of dredging machine was designed.The simulation model of load sensing system of double-lift cylinder is established by AMESim,and the hydraulic system parameters are optimized based on orthogonal test and simulation analysis of the hydraulic system.The results showed that:Under the variable load condition,the accumulator of appropriate specifications is connected in parallel,the pump displacement and pipeline length are controlled reasonably,the load sensitive hydraulic system of the double lift cylinder for desilting is stable,the maximum impact pressure of the main valve is reduced from the original 37.68MPa to 12.84MPa,and the hydraulic impact of the lift hydraulic cylinder is reduced from the original 4.2MPa to 2.05MPa.Effectively reduce the hydraulic shock and pulsation phenomenon,improve the service life of reversing valve and hydraulic cylinder,and provide a reference for the design of the dredging machine hydraulic system.
作者
贾春强
何彬
朱玉芹
孙斌
JIA Chun-Qiang;HE Bin;ZHU Yu-Qin;SUN Bin(School of Mechanical Engineering,Shenyang Jianzhu University,Shenyang Liaoning 110168,China;Science Innovation Center,Shenyang Research Institute of China Coal Technology&Industry Group,Fushun Liaoning 113122,China)
出处
《机电产品开发与创新》
2023年第2期7-11,共5页
Development & Innovation of Machinery & Electrical Products
基金
沈阳市高层次创新人才计划项目(RC190431)。
关键词
清淤机
负载敏感
液压冲击
优化设计
Dredging machine
Load sensitive
Hydraulic impact
The optimization design
