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A carbon emission quantitation model and experimental evaluation for machining process considering tool wear condition

  • Guanghui Zhou
  • Shengze Yuan
  • Qi Lu
  • Xiaoping Xiao
ORIGINAL ARTICLE
  • 59 Downloads

Abstract

Nowadays, the accurate calculation and evaluation of processing carbon emissions (which refer to the total carbon emissions emitted by CNC consuming electrical energy during machining process) have become a hot topic owning to their great role on optimizing cutting processes, and thus reducing the global carbon dioxide emissions. However, the existing carbon emission calculation models for machining process do not pay much attention to the effect of tool wear on processing carbon emissions, which leads to the inaccurate evaluation. So in this paper, a practical carbon emission model for machining process is carried out. The model consists of two parts: (1) a relationship between processing carbon emissions and cutting power (which is the power only caused by removing materials from workpiece) and (2) a novel cutting power model considering tool wear condition. Afterwards, orthogonal experiments are performed on three different CNC machine tools in order to fit cutting power model’s constants and coefficients. Experiment results and related data analysis indicate that the presented cutting power model and the experimental evaluation method are accurate, and the flank wear length (VB), which is the index of evaluating tool wear condition, is necessary to be introduced as an independent variable. Compared with other models which do not consider the tool wear condition, this model succeeds to improve the calculation precision of processing carbon emissions, and provides more accurate data supporting the cutting parameter optimization.

Keywords

Processing carbon emissions Tool wear Predictive model Orthogonal experiment 

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Notes

Acknowledgments

This research is supported by the National Natural Science Foundation of China (grant no. 51575435).

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Copyright information

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

Authors and Affiliations

  • Guanghui Zhou
    • 1
    • 2
  • Shengze Yuan
    • 1
  • Qi Lu
    • 1
  • Xiaoping Xiao
    • 3
  1. 1.School of Mechanical EngineeringXi’an Jiaotong UniversityXi’anPeople’s Republic of China
  2. 2.State Key Laboratory of Manufacturing Systems EngineeringXi’an Jiaotong UniversityXi’anPeople’s Republic of China
  3. 3.Hunan Institute of Metrology and TestChangshaPeople’s Republic of China

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