西安交通大学机械制造系统工程国家重点实验室,西安,710049
网络首发:2015-08-10,
纸质出版:2015
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米维, 闫柯, 吴文武, 等. 考虑热-变形耦合的主轴-轴承系统瞬态热特性分析[J]. 西安交通大学学报, 2015,49(8):52-47.
Transient Thermal Property Analysis for Spindle-Bearing System Considering Thermo-Deformation Coupling[J]. 2015, 49(8): 52-47.
米维, 闫柯, 吴文武, 等. 考虑热-变形耦合的主轴-轴承系统瞬态热特性分析[J]. 西安交通大学学报, 2015,49(8):52-47. DOI: 10.7652/xjtuxb201508009.
Transient Thermal Property Analysis for Spindle-Bearing System Considering Thermo-Deformation Coupling[J]. 2015, 49(8): 52-47. DOI: 10.7652/xjtuxb201508009.
为了更准确地预测主轴-轴承系统的温度场并实时监测关键零部件的温升情况
建立了考虑热-变形耦合的轴系瞬态热网络模型。根据热弹性力学理论
推导出主轴-轴承系统在装配应力、离心应力和热应力综合作用下的径向复合变形方程
基于热网络法优选试验轴系关键部件作为温度节点
综合考虑润滑剂黏温效应及轴系径向复合应力与变形
实时修正轴系热源、热边界条件等特性参数
实现了温度场与变形的耦合分析。通过编程求解获得了不同条件下轴承的瞬态温升曲线及轴系关键热参数的瞬态特性
结果表明
主轴转速越高
轴系热平衡温度越高
平衡时间越短; 迭代步长的选取只影响温升曲线的收敛时间
不影响稳态温度值。与试验数据的对比结果表明
使用该瞬态热网络模型预测轴系温度场可显著降低计算误差。
To predict the temperature field of spindle-bearing system more accurately and monitor the temperature rise of key parts
a transient thermal network model with the coupling of the temperature field and deformation was established. The radial compound stress-deformation equation under the initial assembly stress
centrifugal stress and thermal stress was derived following thermo-elasticity theory. The key system components were picked out as the heat nodes according to thermal network optimization. Taking both temperature-viscosity effect and radial compound stress-deformation into account
several key thermal parameters
such as heat source and thermal boundary conditions
were modified in real time. The coupled temperature field and deformation of spindle-bearing system were analyzed. The transient temperature curves of bearings under different conditions and the transient properties of key thermal parameters were obtained numerically. The results shows that the higher of the spindle speed
the higher of the equilibrium temperature
and the shorter of the equilibrium time. The iterative step can only affect the equilibrium time but not the equilibrium temperature. A comparison with a set of experiments indicates that the transient thermal network model enables to reduce errors remarkably in predicting the temperature field of spindle-bearing system.
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