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

WANG Peitong; FAN Jinwei; REN Xingfei; LI Zhuang

doi:10.37188/OPE.20233101.0129

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

Micro/Nano Technology and Fine Mechanics | 更新时间：2023-02-07

- Thermal error prediction for grinding machine spindle based on heat conduction and convolutional neural network
- Optics and Precision Engineering Vol. 31, Issue 1, Pages: 129-140(2023)
- 作者机构：
  
  北京工业大学先进制造技术北京市重点实验室，北京 100124
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20233101.0129
  CLC： TH161
- Received：29 July 2022，
  
  Revised：31 August 2022，
  
  Published：10 January 2023
- 稿件说明：
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王培桐,范晋伟,任行飞等.基于热传导和卷积神经网络的磨床主轴热误差预测[J].光学精密工程,2023,31(01):129-140.

WANG Peitong,FAN Jinwei,REN Xingfei,et al.Thermal error prediction for grinding machine spindle based on heat conduction and convolutional neural network[J].Optics and Precision Engineering,2023,31(01):129-140.
王培桐,范晋伟,任行飞等.基于热传导和卷积神经网络的磨床主轴热误差预测[J].光学精密工程,2023,31(01):129-140. DOI： 10.37188/OPE.20233101.0129.

WANG Peitong,FAN Jinwei,REN Xingfei,et al.Thermal error prediction for grinding machine spindle based on heat conduction and convolutional neural network[J].Optics and Precision Engineering,2023,31(01):129-140. DOI： 10.37188/OPE.20233101.0129.

摘要

热变形是影响磨床加工精度的主要因素，严重制约了机床精度的进一步提高。为了提高热误差预测的精度，提出了一种基于热传导和卷积神经网络的磨床主轴热误差预测方法。根据热传导理论推导出主轴表面和外部环境的温差和热变量的映射关系，揭示了材料热变形本质。然后，建立了以温差为输入和主轴热变形量为输出的神经网络热误差预测模型。该模型拥有4个神经网络层，分别对应温差、热能增量、时间变量以及热变形量。运用反向传播算法对该预测模型进行训练并计算模型参数。最后，基于SINUMERIK 840D数控控制器开发了一套磨床主轴热误差补偿系统，并在某一数控磨床上进行了验证。结果表明，通过主轴热误差补偿后，磨床的加工精度提升了41.7%，验证了本文提出的主轴热误差预测模型的有效性和可行性。

Abstract

Thermal deformation is the main factor affecting the machining accuracy of grinding machines， which severely limits further improvements in the accuracy of machine tools. However， only a few studies have investigated thermal error prediction， and the prediction accuracy has been low. Therefore， this paper proposes a method to predict the thermal error of grinding machine spindles based on the heat conduction theory and a convolutional neural network. First， according to the heat conduction theory， the mapping relationship between the thermal variables and the temperature difference between the surface of the main axis of Chu and the external environment is deduced， revealing the thermal deformation nature of the materials. Second， a neural network model for thermal error prediction with temperature difference as the input and thermal deformation of the main shaft as the output is established. The model has four neural network layers corresponding to temperature difference， thermal energy increment， time variable， and thermal deformation. The back-propagation algorithm is then used to train the prediction model and calculate the model parameters. Finally， based on the SINUMERIK 840D CNC controller， a set of thermal error compensation systems for grinding machine spindles are developed and verified using a CNC grinding machine. The results show that the machining accuracy of the grinder is improved by 41.7% following thermal error compensation for the spindle， thus confirming the validity and feasibility of the spindle thermal error prediction model proposed in this paper.

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references

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