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Journal of Intelligent Manufacturing

, Volume 30, Issue 1, pp 123–138 | Cite as

A comprehensive approach to parameters optimization of energy-aware CNC milling

  • Congbo LiEmail author
  • Lingling Li
  • Ying Tang
  • Yantao Zhu
  • Li Li
Article

Abstract

Cutting parameters are important components in the process of computer numerical control (CNC) machining, and reasonable choice of cutting parameters can significantly affect the energy efficiency. This paper presents a multi-objective parameter optimization method for energy efficiency in CNC milling process. Firstly, the energy consumption composition characteristics and temporal characteristics in CNC milling are analyzed, respectively. The energy model of CNC milling is then established, of which the correlation coefficient is obtained through nonlinear regression fitting. Then a multi-objective optimization model is proposed to take the highest energy efficiency and the minimum production time as the optimization objectives, which is solved based on Tabu search algorithm. Finally, a case study is conducted to validate the proposed multi-objective optimization model and the optimal parameter solutions of maximum energy efficiency and minimum production time is obtained. Moreover, the parametric influence on specific energy consumption and production time are explicitly analyzed. The experiment results show that cutting depth and width are the most influential parameters for specific energy consumption, and spindle speed ranks the first for the production time.

Keywords

CNC milling Cutting parameters Energy efficiency Multi-objective optimization 

Abbreviations

CNC

Computerized numerical control

TS

Tabu search

SEC

Specific energy consumption

GRA

Grey relational analysis

RSM

Response surface method

MRR

Material removal rate

MRV

Material removal volume

NP-hard

Non-deterministic polynomial hard

Notes

Acknowledgments

This work was supported in part by the National High-Tech R&D Program of China under Grant 2014AA041506, and the National Natural Science Foundation of China (NSFC) under Grant 51475059.

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.State Key Laboratory of Mechanical TransmissionChongqing UniversityChongqingChina
  2. 2.Department of Electrical and Computer EngineeringRowan UniversityGlassboroUSA
  3. 3.College of Engineering and TechnologySouthwest UniversityChongqingChina

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