基于蜂群算法的机床主轴对流换热系数优化

发布时间：2018-07-01 14:44

  本文选题：对流换热系数 + 主轴温度　； 参考：《仪器仪表学报》2015年12期

【摘要】：机床主轴温度场分析是一种减少主轴热误差、提高主轴精度及其稳定性的重要方法。作为热分析边界条件中关键参数的对流换热系数,其值大小反映了主轴零部件表面与空气对流换热的强度,对主轴有限元温度场分析结果的影响最为明显。以170CP06-DM机械主轴为研究对象,深入研究主轴对流换热系数的影响因素,提出一种基于人工蜂群优化算法的机床主轴换热系数优化算法。实验表明,提出的蜂群算法能够根据环境温度和转速而自动寻找优化的主轴换热系数。与传统的经验确定对流换热系数方法相比,主轴前轴承处的计算最大误差、平均误差、均方差分别提高了大约4.54℃、2.87℃、1.65℃,主轴后轴承处的计算最大误差、平均误差、均方差分别提高了大约7.12℃、3.49℃、2.41℃。
[Abstract]:The temperature field analysis of machine tool spindle is an important method to reduce the thermal error of spindle and improve the accuracy and stability of spindle. The convection heat transfer coefficient, which is the key parameter in the boundary condition of thermal analysis, reflects the intensity of convection heat transfer between the surface of spindle parts and air, and has the most obvious influence on the temperature field analysis results of spindle finite element method. Taking the 170CP06-DM mechanical spindle as the research object, the factors influencing the convection heat transfer coefficient of the spindle are studied in depth, and an optimization algorithm of the spindle heat transfer coefficient of machine tool based on artificial bee colony optimization algorithm is proposed. The experimental results show that the proposed algorithm can automatically find the optimized heat transfer coefficient of the spindle according to the ambient temperature and rotation speed. Compared with the traditional method of determining convection heat transfer coefficient, the maximum error, mean error and mean deviation of the front bearing are increased by about 4.54 鈩，

本文编号：2088116