A carbon emission quantitation model and experimental evaluation for machining process considering tool wear condition
- 59 Downloads
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.
KeywordsProcessing carbon emissions Tool wear Predictive model Orthogonal experiment
Unable to display preview. Download preview PDF.
This research is supported by the National Natural Science Foundation of China (grant no. 51575435).
- 4.Li CB, Li LL, Tang Y, Zhu YT, Li L (2016) A comprehensive approach to parameters optimization of energy-aware CNC milling. J Intell Manuf 1–16Google Scholar
- 6.Kordonowy DN (2002) A power assessment of machining tools. Massachusetts Institute of Technology, CambridgeGoogle Scholar
- 7.Dietmair A, Verl A (2009) Energy consumption forecasting and optimisation for tool machines. MM Sci J 3:62–67Google Scholar
- 9.Gutowski T, Dahmus J, Thiriez A (2006) Electrical energy requirements for manufacturing processes. Proceedings of the 13th CIRP International Conference on Life Cycle Engineering, Leuven, Belgium, May 31–June 2, 2006Google Scholar
- 17.Wang Q, Liu F, Wang X (2013) Multi-objective optimization of machining parameters considering energy consumption. Int J Adv Manuf Technol 71(5–8):1133–1142Google Scholar
- 19.Altıntaş RS, Kahya M, Ünver HÖ (2016) Modelling and optimization of energy consumption for feature based milling. Int J Adv Manuf Technol 86(9):3345–3363Google Scholar
- 21.Zhang BJ, Song SM, Chen M (2010) Study of cutting force model based on tool condition. Tool Eng. (02):27–30. (In Chinese)Google Scholar
- 22.Rizal M, Ghani JA, Nuawi M, Che Haron CH (2013) The application of I-kazTM-based method for tool wear monitoring using cutting force signal. MITC2013 68:461–468Google Scholar
- 25.Uehara K, Kiyosawa F, Takeshita H (1979) Automatic tool wear monitoring in NC turning. CIRP 28(1):38–42Google Scholar