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螺栓设计参数对连接状态的影响及轴向力可视化方法研究

Research on the influence of bolt design parameters on the connection state and the visualization method of axial force
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摘要 螺栓结构的连接状态与轴向力密切相关,而轴向力受多因素的影响,在拧紧或者松动过程中不断变化且无法做到无传感器实时监测。首先,基于螺栓的外螺纹轮廓方程,建立了精细化的螺栓有限元模型。其次,基于此模型研究了螺纹接触面摩擦因数、牙型角、螺距对于螺栓预紧过程及松动过程的影响规律。最后,关联松动角度和轴向力,结合深度学习目标检测算法,将螺栓的轴向力信息可视化。研究结果表明:在拧紧和松动过程中,螺栓轴向力实现了可观和可控,同时螺栓在预紧相同角度的情况下,其轴向力随着螺距和牙型角的增大而增加;其预紧力矩随着螺纹接触面摩擦因数和螺距的增大而变大,但会随着牙型角的增大而变小。 The axial force of the bolt structure is closely related to its connection state.However,the elastic interaction and crosstalk among bolts in the service process affect the axial force of bolts by the coupling of multi-dimensional and cross-scale variable factors,which are changing constantly during the tightening or loosening process and can hardly be monitored in real-time without sensors.Furthermore,conventional bolted structures are in general designed according to engineering experience,with frequent occurences of over-and under-design.To address these problems,this paper firstly builds a refined finite element model based on the thread profile equations of bolts and nuts,guaranteeing the reliability and accuracy of the analysis results afterwards.Based on this model,the influence of variations in bolt design parameters,such as the factor of friction of the thread contact surface,tooth angle,and pitch is investigated on the pre-loading and loosening processes of the bolt.Our study of this fundamental mechanism problem enriches the research on bolt matching design methods for special equipment and strengthens the forward design capability of fastening systems.Finally,the angle of loosening and axial force are correlated and combined with the YOLOv7 deep learning target detection algorithm to visualize the bolt’s axial force information.Our results show the axial force of the bolt rises as the pitch and tooth angle increase,assuming the bolt is pre-loaded at the same angle.The pre-load torque increases with the factor of friction of the thread contact surface and pitch,but decreases with the tooth angle because the increased contact area between the threads reduces the possibility of relative sliding of the contact surfaces.Larger pitches and factors of friction increase the connection torque,reduce the risk of loosening,and improve the stability of the connection.However,increased assembly forces may lead to installation difficulties or damage to the connections.Additionally,contact surfaces may experience increased wear during long-term service.During the loosening of the bolt,there is a rapid decrease in axial force.Smaller tooth angles,larger pitches,and larger factors of friction of the thread contact surfaces delay the end of this phase.Evidently,a reduced tooth angle could result in inadequate tightening force within the bolt structure.Moreover,the diminished friction produced by a smaller thread contact area may prove insufficient to withstand external loads and vibrations,thereby increasing the susceptibility of the bolt to loosening.As the thread pitch becomes larger,the phase of rapid decrease in axial force is slightly delayed and the decrease is greater.These laws offer theoretical support for designing bolted structures.Moreover,a less expensive digitization platform capable of correlating the axial force of bolts is built.During the process of tightening and loosening,the axial force of the bolt is both visualized and controlled.This approach combines vision with the properties of the bolts themselves.It visualizes the axial forces and provides a more accurate analytic model of the loosening characteristics of the bolted structure.The building of the digital platform improves work efficiency,reduces maintenance costs,and increases the reliability and safety of the entire system.
作者 周洋 伊鸿振 王瀚 ZHOU Yang;YI Hongzhen;WANG Han(School of Mechanical and Power Engineering,Zhengzhou University,Zhengzhou 450001,China)
出处 《重庆理工大学学报(自然科学)》 CAS 北大核心 2024年第3期220-229,共10页 Journal of Chongqing University of Technology:Natural Science
基金 河南省自然科学基金项目(202300410431)。
关键词 摩擦因数 牙型角 螺距 深度学习 轴向力 factor of friction included angle pitch deep learning axial force
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