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兵工学报 ›› 2025, Vol. 46 ›› Issue (S1): 250270-.doi: 10.12382/bgxb.2025.0270

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基于混合重要抽样法的弹药装填机械手回转送弹过程到位可靠性及灵敏度评估

陈义军, 孔凡成, 刘永吉*()   

  1. 空军勤务学院 航空弹药保障系, 江苏 徐州 221000

Evaluation on Positioning Reliability and Sensitivity of the Ammunition Loading Manipulator’s Rotary Feeding Process based on Hybrid Importance Sampling Method

CHEN Yijun, KONG Fancheng, LIU Yongji*()   

  1. Department of Aviation Ammunition SupportAir Force Logistics Academy, Xuzhou 221000,Jiangsu, China
  • Received:2025-04-14 Online:2025-11-06

摘要: 装填机械手的主要功能为将弹丸从弹仓中取出,并将其精确及时地运送至弹协调臂的交接位置,其到位精度对整个供弹过程有着较大影响,提高装填机械手的到位精度可靠性对整个自动装填系统有重要意义。以某弹药装填机械手为研究对象,建立机电耦合的装填机械手参数化联合仿真模型,并运用台架试验数据验证仿真模型的准确性。通过建立一种能够替代复杂动力学仿真模型的神经网络代理模型,用于获取机械手到位后的响应值,基于混合重要抽样方法结合该代理模型,分析装填机械手到位精度的可靠性及灵敏度。结果表明:在选定的12个参数影响下其到位可靠度为0.9779,具有较高的精度,同时经灵敏度分析得到齿轮之间的摩擦系数对到位可靠性影响最大,这为后续进行结构优化指明了方向。

关键词: 装填机械手, 机电耦合, 神经网络, 混合重要抽样, 到位可靠性

Abstract:

The primary function of the ammunition loading manipulator is to extract the projectiles from the magazine and transport them accurately and timely to the handover position of the ammunition coordinating arm.The positioning accuracy of the manipulator significantly impacts the entire ammunition supply process,and improving its reliability is of great importance to the overall automatic loading system.A parameterized co-simulation model that integrates electromechanical coupling is established a specific ammunition loading manipulator.The accuracy of the simulation model is validated using bench test data.A neural network surrogate model is developed to replace the complex dynamic simulation model,enabling the acquisition of response values after the manipulator reaches its target position.Finally,the reliability and sensitivity of the manipulator’s positioning accuracy are analyzed using a hybrid importance sampling method combined with the surrogate model.The results show that the positioning reliability of the manipulator is 0.9779 with high accuracy under the influence of the selected 12 parameters.The sensitivity analysis shows that the friction coefficient between gears has the greatest impact on positioning reliability,which points out the direction for subsequent structural optimization.

Key words: loading manipulator, electromechanical coupling, neural network, hybrid importance sampling, positioning reliability