浙江大学学报(工学版)  2018, Vol. 52 Issue (2): 247-254    DOI: 10.3785/j.issn.1008-973X.2018.02.006
 机械与动力工程

1. 南京工业大学 机械与动力工程学院, 江苏 南京 210009;
2. 南京工业大学 江苏省工业装备数字制造及控制技术重点实验室, 江苏 南京 210009;
3. 重庆大学 机械传动国家重点实验室, 重庆 400044
Thermal characteristics and thermal error modeling analysis for motorized spindle of gear grinding machine tool
XIE Jie1, HUANG Xiao-diao1,2, FANG Cheng-gang1,2, ZHOU Bao-cang3, LU Ning1
1. School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing 210009, China;
2. Jiangsu Key Laboratory of Digital Manufacturing for Industrial Equipment and Control Technology, Nanjing Tech University, Nanjing 210009, China;
3. State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, China
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Abstract:

A new modeling method based on fuzzy neural network (FNN) was proposed to solve the problem of the thermal error caused by the motorized spindle in the grinding machining process. The internal heat generating and transfer mechanism of the spindle were analyzed to reveal the heat transfer law. The temperature field and the thermal deformation of the spindle system were numerically simulated by finite element analysis (FEA) software with the given thermal load and boundary condition. The maximum risen temperature and the largest thermal deformation were obtained. Fuzzy neural network model and BP neural network model were trained respectively, through acquiring temperature and thermal deformation values of the spindle in the thermal error experiment. The thermal error model between temperature field and its thermal deformation was established to predict the thermal error of the spindle. Results show that the modeling accuracy of the fuzzy neural network model and the BP model are 96.74% and 89.77% respectively in the prediction model of the radial thermal error. The thermal error model of FNN model is superior to that of BP neural network for the fitting and forecasting accuracy.

 CLC: TH161

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XIE Jie, HUANG Xiao-diao, FANG Cheng-gang, ZHOU Bao-cang, LU Ning. Thermal characteristics and thermal error modeling analysis for motorized spindle of gear grinding machine tool. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2018, 52(2): 247-254.

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