• CN:11-2187/TH
  • ISSN:0577-6686

›› 2000, Vol. 36 ›› Issue (1): 92-95.

• 论文 • 上一篇    下一篇

神经网络补偿机床热变形误差的机器学习技术

杨庆东;Van Den Bergh C;Venherck P;Kruth J P   

  1. 北京机械工业学院机械系;比利时鲁文大学
  • 发布日期:2000-01-15

LEARNING APPROACH TO COMPENSATE MACHINE TOOLS THERMAL DEFORMATION BASED ON NEURAL NETWORK

Yang Qingdong;Van Den Bergh C;Venherck P;Kruth J P   

  1. Beijing Institute of Machinery Industry Katholieke University of Leuven, Belgium
  • Published:2000-01-15

摘要: 研究了以神经网络(NN)为模型的软件补偿不同机床热误差。提出知识获取是神经网络建模的关键环节。两种数控机床被用来研究分析热变形,通过测量实验获取学习数据,特别是温度测量和传感器数目的选取。介绍了机器学习技术和学习数据组织方法,包括归纳学习和推理学习。给出了预报补偿的结果和精度评价。

关键词: 热变形, 神经网络, 误差补偿, 知识获取

Abstract: Software based on neural network (NN) to compensate machine tools thermal deformation is studied. The knowledge acquisition which plays very important part in modeling a neural network is presented. Two kinds of machine tools are analyzed and the methods of learning data acquisition from experiment, especially temperature measurement and the choice of the number of temperature sensors are discussed. The learning data organization technique, including inductive and deductive learning is introduced. The results of neural network prediction and accuracy evaluation are given.

Key words: Error compensation, Knowledge acquisition, Neural network, Thermal deformation

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