Neural Networks-Based In-Process Surface Roughness Adaptive Control System in Turning Operations

  • Julie Z. Zhang
  • Joseph C. Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)


Using a back-propagation neural networks algorithm and accelero-meter sensor technique, this research developed an in-process surface roughness adaptive control (IPSRAC) system in turning operations. This system not only can predict surface roughness in real time, but can also provide an adaptive feed rate change in finishing turning to ensure the surface roughness can meet requirements.


Surface Roughness Feed Rate Spindle Speed Vibration Signal Turning Operation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Julie Z. Zhang
    • 1
  • Joseph C. Chen
    • 2
  1. 1.37 ITC, University of Northern IowaCedar FallsUSA
  2. 2.221 I. Ed. II, Iowa State UniversityAmesUSA

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