Optics and Precision Engineering, Volume. 31, Issue 1, 129(2023)

Thermal error prediction for grinding machine spindle based on heat conduction and convolutional neural network

Peitong WANG... Jinwei FAN*, Xingfei REN and Zhuang LI |Show fewer author(s)
Author Affiliations
  • Beijing Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing100124, China
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    Figures & Tables(15)
    Temperature of micromaterial unit and its surrounding environment at t
    Structure of grinding machine spindle
    Sensor pair distribution
    Thermal analysis results of spindle
    Neural network architecture
    Flow chart of neural network training
    Temperature measurement of motorized spindle
    Experimental results of spindle thermal deformation with different rotation speeds
    Schematic diagram of grinding shaft
    Thermal error compensation method based on OPC UA
    Shaft grinding experiment
    • Table 1. Measuring instruments for thermal deformation of main shaft

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      Table 1. Measuring instruments for thermal deformation of main shaft

      实验器材模 块参数设置
      RS485热采集模块YanHua 821112个温度输入接口和采样精度±0.01 ℃
      红外仪器Fluke TV46测量范围:-20~150 ℃,精度为“A”级(0.15℃+ 0.002 t) IEC-751标准
      电容传感器Displacement HA-100最大采样频率为50 kHz;线性测量精度±0.5×10-6
    • Table 2. Experimental parameters for thermal deformation measurement of main shaft

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      Table 2. Experimental parameters for thermal deformation measurement of main shaft

      转速/(r·min-1运转时间/min冷却时间/min环境温度/℃总时间 /min应用
      1 50010010020±1200训练
      50010010020±1200实验
      1 00010010020±1200实验
      1 50010010020±1200实验
      2 00010010020±1200实验
      2 50010010020±1200实验
      3 00010010020±1200实验
    • Table 3. Comprehensive test parameters

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      Table 3. Comprehensive test parameters

      转速/(r·min-1时间/min
      50040
      1 50040
      1 00040
      1 50040
      040
    • Table 4. Thermal error compensation results

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      Table 4. Thermal error compensation results

      误差值方法一方法二提 升
      AE/mm0.004 30.002 346.5%
      AC/mm0.002 40.001 441.7%
      AD/mm0.003 70.002 143.2%
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    Peitong WANG, Jinwei FAN, Xingfei REN, Zhuang LI. Thermal error prediction for grinding machine spindle based on heat conduction and convolutional neural network[J]. Optics and Precision Engineering, 2023, 31(1): 129

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    Paper Information

    Category: Micro/Nano Technology and Fine Mechanics

    Received: Jul. 29, 2022

    Accepted: --

    Published Online: Feb. 9, 2023

    The Author Email: FAN Jinwei (jwfan@bjt.edu.cn)

    DOI:10.37188/OPE.20233101.0129

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