系统仿真学报 ›› 2016, Vol. 28 ›› Issue (2): 434-441.

• 仿真应用工程 • 上一篇    下一篇

基于仿真优化的掘进机铲板综合性能研究

王大勇, 王慧   

  1. 辽宁工程技术大学机械工程学院,阜新 123000
  • 收稿日期:2015-04-28 修回日期:2015-06-09 出版日期:2016-02-08 发布日期:2020-08-17
  • 作者简介:王大勇(1979-),男,辽宁阜新,博士生,讲师,研究方向为机械系统设计及动态分析。

Study on Comprehensive Performance of Roadheader Shovel Based on Simulation Optimization Method

Wang Dayong, Wang Hui   

  1. School of Mechanical Engineering, Liaoning Technical University, Fuxin 123000, China
  • Received:2015-04-28 Revised:2015-06-09 Online:2016-02-08 Published:2020-08-17

摘要: 提出一种改进遗传算法,即在传统遗传算法中融入非均匀变异算子和小生境运算,可使解有效地朝着最优化运行,且更好地保持解的多样性和较高的收敛速度,并且将铲板宽度、铲板倾角及围板厚度作为设计变量,对铲板的铲掘力、装载能力进行多目标优化设计。优化结果表明:优化后铲板宽度降低1.33%,铲板倾角降低9.47%,而铲板围板厚度增加10.4%,这将对掘进机整机稳定性和铲板强度有显著效果;铲板铲掘力提高4.72%,铲板装载能力提高5.12%,改善了掘进机装载机构的综合性能,符合眼下掘进机铲板设计的趋势。同时,在Pro/E, ADAMS, ANSYS协同仿真环境下了优化后铲板的可靠性。

关键词: 掘进机铲板, 装载能力, 铲掘力, 铲板围板厚度, 改进遗传算法, 优化设计, 可靠性

Abstract: An improved genetic algorithm that integrates into non-uniform mutation operator and niching technology, which could run toward the optimal solution effectively, and to better maintain the diversity of the solution and high convergence speed was proposed. Meanwhile, taking shovel width and shovel dip for design variables optimizes loading capacity, shovel grubbing force and shovel coaming thickness of roadheader shovel. Optimization results show that: shovel width is reduced by 1.33%, shovel dip is reduced by 9.47%, and Shovel coaming thickness is increased by 11.4%, which has a significant effect on the overall stability of roadheader and the shovel strength. Loading capacity is increased by 4.72%, and shovel grubbing force is increased by 5.12% after optimization, which improve comprehensive performance of the loading mechanism of roadheader and accord with development trend of roadheader shovel in line with the current and future. Meanwhile, the reliability of shovel is analysed under the environment of co-simulation of Pro/E, ADAMS and ANSYS.

Key words: roadheader shovel, loading capacity, shovel grubbing force, shovel coaming thickness, improved genetic algorithm, optimization design, reliability

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