Feature-based carbon emission quantitation strategy for the part machining process

Abstract

As global warming is becoming remarkably serious, the issue of low-carbon manufacturing has attracted numerous researchers’ attention. Meanwhile, manufacturing industries require the carbon emission data of their products to cope with the coming carbon policy such as carbon labelling. Since products are made of parts, one of the major difficulties is the lack of a well-established approach to obtain the carbon emissions of machining a part. In this paper, a carbon emission quantitation strategy is proposed to quantify the overall carbon emissions of a part machining process. In the quantitation strategy, firstly, the calculation models for various types of carbon emissions are presented to analyse the carbon footprint. A formalised approach is then proposed to quantify the overall carbon emissions by designing a c-PBOM (carbon emissions-Process Bill of Material) based on machining features. Particularly, in order to reduce the computation complexity and clarify the computation process, a c-PBOM is established to support the quantitation strategy by decomposing a part into an aggregation of machining features. In addition, the machining process is considered as a cooperation of the energy consumers decomposed from the machine tool. Finally, the strategy is applied in the machining of a bearing seat. The results show it can be used to predict the carbon emissions for a part machining process conveniently and efficiently, which would provide a data base for the research of low-carbon process planning and scheduling.

Keywords:

• carbon emissions
• energy models
• machining features
• c-PBOM
• quantitation strategy

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant No. 51575435 and No.71671136; Program for New Century Excellent Talents in University by China Ministry of Education under Grant No. NCET-12-0452.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China under Grant Nos [51575435 and 71671136] and Program for New Century Excellent Talents in University by China Ministry of Education under Grant No. [NCET-12-0452].