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永州职业技术学院 信息工程学院, 湖南 永州 425000
唐爱武(1980— ),男,湖南东安人,学士,副教授;主要研究方向为机械设计制造及自动化;twj02169@163.com。
陈天佑(1985— ),男,湖南祁阳人,学士,讲师;主要研究方向为机械设计与制造;cxhxh7474@163.com。
纸质出版日期:2024-01-15,
收稿日期:2022-10-18,
修回日期:2022-12-22,
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唐爱武,陈天佑.复杂工况条件下齿轮传动过程中磨损量预测研究[J].机械传动,2024,48(01):143-150.
Tang Aiwu,Chen Tianyou.Research on Prediction of Wear Amount in the Gear Transmission Process Under Complex Working Conditions[J].Journal of Mechanical Transmission,2024,48(01):143-150.
唐爱武,陈天佑.复杂工况条件下齿轮传动过程中磨损量预测研究[J].机械传动,2024,48(01):143-150. DOI: 10.16578/j.issn.1004.2539.2024.01.021.
Tang Aiwu,Chen Tianyou.Research on Prediction of Wear Amount in the Gear Transmission Process Under Complex Working Conditions[J].Journal of Mechanical Transmission,2024,48(01):143-150. DOI: 10.16578/j.issn.1004.2539.2024.01.021.
磨损是齿轮传动过程中的主要失效形式,磨损加剧会使齿轮齿侧间隙非线性增大、传动精度下降及齿面冲击力增大,进而导致齿轮传动系统振动加剧,对齿轮传动性能及设备的稳定运行造成重大影响。为了解决上述问题,提出了复杂工况条件下齿轮传动过程中磨损量预测方法。基于形式磨损指数识别并判定齿轮磨损状态,通过深入分析齿轮磨损机制并以此为基础,绘制典型齿轮磨损过程曲线,计算齿轮传动摩擦力矩数值,构建了齿轮磨损量数学模型;再将已知齿轮状态数值输入至所构建模型中,即可得出齿轮预测磨损量,实现齿轮磨损量的预测。试验结果表明,在3种复杂工况条件下,提出的预测模拟数据更接近于实际参数,验证了磨损量预测的精度。
Wear is the main failure form in the gear transmission process. The increased wear will increase the non-linear backlash of the gear teeth
reduce the transmission accuracy and increase the impact force of the tooth surface
which will lead to aggravated vibration of the gear transmission system
and impose great impact on the gear transmission performance and the stable operation of the equipment. In order to solve the above problems
a method for predicting the wear amount in the gear transmission process under complex working conditions is proposed. The gear wear state is identified and determined based on the formal wear index. Through in-depth analysis of the gear wear mechanism
a typical gear wear process curve is drawn based on this
the gear transmission friction torque value is calculated
and a mathematical model of the gear wear amount is built; then the known gear state values are input into the construction model
the predicted gear wear amount is obtained
and the prediction of gear wear amount is realized. The experimental results show that under three complex working conditions
the prediction simulation data proposed in this study is closer to the actual parameters
which fully verifies that the prediction accuracy of the wear amount is higher.
复杂工况齿轮传动状态识别磨损量计算及预测预测精度
Complex working conditionsGear transmissionState identificationWear amountCalculation and predictionPrediction accuracy
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