Feature-based carbon emission quantitation strategy for the part machining process. Issue 4 (3rd April 2018)

• Record Type: Journal Article
• Authors: Zhou, Guanghui; Zhou, Ce; Lu, Qi; Tian, Changle; Xiao, Zhongdong
• Is Part Of: International journal of computer integrated manufacturing. Volume 31:Issue 4/5(2018)
• Page Start: 406
• Page End: 425
• Publication Date: 2018-04-03
• Subjects: carbon emissions -- energy models -- machining features -- c-PBOM -- quantitation strategy; Computer integrated manufacturing systems -- Periodicals; 670.427
• DOI: 10.1080/0951192X.2017.1328561
• Languages: English
• ISSNs: 0951-192X

Abstract

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.