Impact of surface machining complexity on energy consumption and efficiency in CNC milling

  • Junhua Zhao
  • Li LiEmail author
  • Yue Wang
  • John W. Sutherland


Energy consumption of machining systems has been a great concern of many manufacturing enterprises. It is pointed out that complex properties of sculptured surface have important influence on CNC machining process where energy consumption and machining efficiency are treated as two evaluation indicators of machining system performance. This paper studies the impact of Surface Machining Complexity (SMC) on energy consumption and efficiency in CNC machining. By analyzing critical factors that influence machining power and efficiency, a pentagon model that refers to the workpiece, equipment, cutter, goal, and process is provided. Based on the pentagon model, a model for calculating SMC, which reflects the difficulty level of CNC machining, is developed. Furthermore, a detailed process of the solution using Fuzzy c-means clustering algorithm is introduced with a case study. Finally, the impact of SMC on energy consumption of machining system is discussed via a group of experiments. The experiments verified the effectiveness of the proposed method and present the increased trend between surface machining complexity and energy consumption, in particular considering the effect of surface curvature on machining energy consumption.


Energy consumption Surface machining complexity (SMC) Machining efficiency Surface partitioning Fuzzy c-means clustering algorithm 


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We would like to thank Miss Magdalene Jackson for her helpful language revision. We are grateful to other people in the research group of “Environmental and Ecological Engineering of Purdue University” for collaboration and discussion.

Funding information

This research was supported by National Natural Science Foundation of China (No.51875480), National Natural Science Foundation of China (No.51405396), Fundamental Science and Frontier Technology Foundation of Chongqing (No. cstc2016jcyjA0422), and Fundamental Research Funds for the Central Universities National Natural Science Foundation of China (XDJK2017B051).


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Junhua Zhao
    • 1
  • Li Li
    • 1
    Email author
  • Yue Wang
    • 2
  • John W. Sutherland
    • 2
  1. 1.College of Engineering and TechnologySouthwest UniversityChongqingChina
  2. 2.Environmental & Ecological EngineeringPurdue UniversityWest LafayetteUSA

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