Methodology and implementation of a vision-oriented open CNC system for profile grinding

  • L. M. XuEmail author
  • F. Fan
  • Z. Zhang
  • Y. Chen
  • D. J. Hu
  • L. Shi


CNC systems with the vision function have become very valuable for intelligence machine tools because machine vision is a fast-growing intelligent feature for machines. A novel vision-oriented open CNC system was developed in this study and used in profile grinding machines for the precise machining of parts with contour surfaces, such as complex molds and cutting tools. The system is an innovation to the conventional profile grinding method and enabled the profile error to be visually detected and compensated during the machining process. In this study, a novel design methodology for a machine-vision-oriented CNC system was proposed. An Ethernet-based hardware architecture was constructed for the vision-oriented CNC system. The software characteristics of the developed CNC system were analyzed, including a new type of multi-thread software architecture, a seamless handover approach for multi-thread accessing of the memory space, the integration of the human-machine interface with image processing, and virtual-axis-based online error compensation. The running efficiency test of the software development platforms, time-consumption analysis of the measurement and control in the vision-oriented CNC system, and verification of the effectiveness of the developed vision-oriented CNC system were performed. The results indicate that the proposed vision-oriented open CNC system can effectively fuse image processing with motion control, meet the efficiency requirement, and improve the machining precision of profile grinding. The results are also valuable for developing other machine-vision-based intelligent machine tools.


Profile grinding Image processing Machine vision Open CNC Contour error On-machine measurement 


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Funding information

This study was funded by the National Natural Science Foundation of China (No. 51575350).


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • L. M. Xu
    • 1
    Email author
  • F. Fan
    • 1
  • Z. Zhang
    • 1
  • Y. Chen
    • 1
  • D. J. Hu
    • 1
  • L. Shi
    • 1
  1. 1.School of Mechanical EngineeringShanghai Jiao Tong UniversityShanghaiChina

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