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作 者:吕向飞 黄帅[3] LYU Xiangfei;HUANG Shuai(School of Intelligent Manufacturing and Automobile,Chongqing College of Electronic Engineering,Chongqing 410331,China;State Key Laboratory of Mechanical Transmission,Chongqing University,Chongqing 400044,China;School of Mechanical Engineering,Jiujiang Vocational and Technical College,Jiujiang 332000,Jiangxi,China)
机构地区:[1]重庆电子工程职业学院智能制造与汽车学院,重庆410331 [2]重庆大学机械传动国家重点实验室,重庆400044 [3]九江职业技术学院机械工程学院,江西九江332000
出 处:《噪声与振动控制》2021年第6期211-214,228,共5页Noise and Vibration Control
基 金:江西省教育厅科学技术研究资助项目(GJJ171245)。
摘 要:隔振橡胶的工作载荷通常为时变和幅变载荷,为更好预测橡胶疲劳寿命,有必要建立不同应变比作用下橡胶疲劳寿命的高精度预测模型。在不同应变比作用下的橡胶疲劳寿命实测数据的基础上,引入等效应变的概念,将不同应变比作用下的疲劳寿命预测问题转化为运用传统裂纹萌生法可以处理的问题。分别采用指数函数、幂函数、幂指混合函数和二次多项式进行等效应变的计算,以相关系数为拟合优度,对不同模型的建模精度进行评价。结果表明:以幂指混合函数作为等效应变的近似计算模型拟合精度相对最高。进一步的疲劳寿命预测表明,将所提方法应用于不同应变比作用下的隔振橡胶疲劳寿命预测时,所获得的寿命预测值与实测值相比处于2倍分散线以内,说明模型的预测精度符合工程需求。The working load of vibration isolation rubber is usually time-varying and amplitude-varying.In order to better predict the fatigue life of rubber,it is necessary to establish a high-precision fatigue life prediction model for rubbers under different strain ratios.Based on the measurement data of fatigue life of rubbers under different strain ratios,the concept of equivalent strain is introduced to transform the fatigue life prediction problem of different strain ratios into such a problem that can be dealt with by the traditional crack initiation method.The exponential function,power function,powerexponent mixed function and quadratic polynomial models are used respectively to calculate the equivalent strain.Moreover,the correlation coefficient is used as the fitting goodness to evaluate the modeling accuracy of different models.The results show that the fitting accuracy of the power-exponential mixed function is the highest.Finally,the proposed method is applied to the fatigue life prediction of vibration isolation rubber under different strain ratios.Compared with the measured values,the obtained life prediction values are within 2 times of the dispersion lines,indicating that the prediction accuracy of the model meets the engineering requirement.
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