Energy modelling and energy saving strategy analysis of a machine tool during non-cutting status

Xiaona Luan, Song Zhang, Jie Chen and Gang Li

International Journal of Production Research, 2019, vol. 57, issue 14, 4451-4467

Abstract: Increasing energy cost and environmental problems push forward research on energy modelling and saving strategy in the sustainable manufacturing field. A manufacturing process usually includes cutting process and non-cutting status. Energy consumption of non-cutting status accounts for a large amount of the total energy consumption during a machining process. This paper focuses on the energy modelling and saving potential analysis in non-cutting status. First, power of non-cutting status was modelled, which included the fixed power, spindle idle power, feed motion power and rapid feed power. Secondly, milling experiments were conducted to study the characteristic and modelling method of the non-cutting status power. The experiments were divided into two types, some experiments were used to calculate the coefficients, and the others were applied to verify the proposed model. Finally, regression analysis and Analysis of Variance (ANOVA) were applied to illustrate the prediction accuracy of the proposed model. The energy saving strategy was developed for the non-cutting status, which includes shortening the air cutting time and not frequently changing the spindle speed to avoid a power peak. It indicates that the proposed model can predict the power consumption of non-cutting status accurately.

Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2018.1436787 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tprsxx:v:57:y:2019:i:14:p:4451-4467

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TPRS20

DOI: 10.1080/00207543.2018.1436787

Access Statistics for this article

International Journal of Production Research is currently edited by Professor A. Dolgui

More articles in International Journal of Production Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().