The dynamic balance assessment during the assembly of the coordinator gyroscope significantly impacts the guidance accuracy of precision-guided equipment.In dynamic balance debugging,reliance on rudimentary counterwei...The dynamic balance assessment during the assembly of the coordinator gyroscope significantly impacts the guidance accuracy of precision-guided equipment.In dynamic balance debugging,reliance on rudimentary counterweight empirical formulas persists,resulting in suboptimal debugging accuracy and an increased repetition rate.To mitigate this challenge,we present a multi-head residual graph attention network(ResGAT)model,designed to predict dynamic balance counterweights with high precision.In this research,we employ graph neural networks for interaction feature extraction from assembly graph data.An SDAE-GPC model is designed for the assembly condition classification to derive graph data inputs for the ResGAT regression model,which is capable of predicting gyroscope counterweights under small-sample conditions.The results of our experiments demonstrate the effectiveness of the proposed approach in predicting dynamic gyroscope counterweight in its assembly process.Our approach surpasses current methods in mitigating repetition rates and enhancing the assembly efficiency of gyroscopes.展开更多
基金supported by the NationalNatural Science Foundation of China(No.51705100)the Foundation of Research on Intelligent Design Method Based on Knowledge Space Reconstruction and Perceptual Push(No.52075120).
文摘The dynamic balance assessment during the assembly of the coordinator gyroscope significantly impacts the guidance accuracy of precision-guided equipment.In dynamic balance debugging,reliance on rudimentary counterweight empirical formulas persists,resulting in suboptimal debugging accuracy and an increased repetition rate.To mitigate this challenge,we present a multi-head residual graph attention network(ResGAT)model,designed to predict dynamic balance counterweights with high precision.In this research,we employ graph neural networks for interaction feature extraction from assembly graph data.An SDAE-GPC model is designed for the assembly condition classification to derive graph data inputs for the ResGAT regression model,which is capable of predicting gyroscope counterweights under small-sample conditions.The results of our experiments demonstrate the effectiveness of the proposed approach in predicting dynamic gyroscope counterweight in its assembly process.Our approach surpasses current methods in mitigating repetition rates and enhancing the assembly efficiency of gyroscopes.
文摘考虑门架变形、轮胎刚度阻尼以及车体惯性质量等参数的影响,搭建了基于VL motion的纵向动力学模型,结合门架液压系统模型建立基于AMESim与VL motion的联合仿真分析平台,辨识负载高位抖动/晃动工况中惯性力矩的时变特征,根据零力矩点理论(Zero Moment Point,ZMP)分析了叉车纵向堆垛的稳定条件。针对叉车纵向堆垛过程中由于液压系统冲击引起的失稳问题,设计了一种组合式液压缓冲装置。仿真结果表明,由于组合式液压缓冲装置的节流与吸能作用,减轻了负载运动状态变化所引起的液压系统冲击问题,提高了叉车纵向堆垛稳定性。
