Application of GRAM and AFSACA-BPN to thermal error optimization modeling of CNC machine tools

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• 作者：Qianjian Guo ; Rufeng Xu ; Tingyi Yang ; Lei He…
• 关键词：CNC machine tool ; Thermal error modeling ; Artificial fish swarm ; Ant colony ; Artificial neural networks
• 刊名：The International Journal of Advanced Manufacturing Technology
• 出版年：2016
• 出版时间：March 2016
• 年：2016
• 卷：83
• 期：5-8
• 页码：995-1002
• 全文大小：1,209 KB
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• 作者单位：Qianjian Guo (1)
  Rufeng Xu (1)
  Tingyi Yang (1)
  Lei He (1)
  Xiang Cheng (1)
  Zhiyong Li (1)
  JG Yang (2)
  
  1. School of Mechanical Engineering, Shandong University of Technology, Zibo, People’s Republic of China
  2. School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai, People’s Republic of China
  
• 刊物类别：Engineering
• 刊物主题：Industrial and Production Engineering
  Production and Logistics
  Mechanical Engineering
  Computer-Aided Engineering and Design
  
• 出版者：Springer London
• ISSN：1433-3015

文摘

Thermal errors are major contributor to dimensional errors of a part during precision machining. Error compensation is an effective method to reduce thermal errors. Accurate modeling of thermal errors is a prerequisite for thermal error compensation. In this paper, five key temperature points of a computer numerical control (CNC) machine tool were selected based on grey relational analysis method (GRAM). One thermal error model based on the five key temperature points was proposed using artificial fish swarm and ant colony algorithm-based back-propagation neural network (AFSACA-BPN). AFS is applied to generate initial pheromone value of ACA, which improves the computational efficiency of BPNs and prediction accuracy of thermal error modeling. One thermal error real-time compensation system was developed based on the proposed model. An experiment was carried out to verify the performance of the compensation system. Experiment results show that the diameter error of the workpiece reduced from 23 to 10 μm after compensation.