A multi-dimension coupling model for energy-efficiency of a machining process
Junhua Zhao,
Li Li,
Lingling Li,
Yunfeng Zhang,
Jiang Lin,
Wei Cai and
John W. Sutherland
Energy, 2023, vol. 274, issue C
Abstract:
Energy-efficient machining has become imperative for energy conservation of manufacturing sectors. The energy characteristics of machining process tend to be very complex, varying substantially with respect to different configurations of machine tool, workpiece and process parameters. This paper undertakes this challenge and explores the energy consumption characteristics of machining process adaptive to different machine tools, workpieces and process parameters. A multi-dimension coupling model of energy consumption for machining process is first established by considering specifications of machine tools, workpieces and processes. Then the influence factors of energy consumption are systematically analyzed from a multi-dimensional perspective. The internal interact relationship among each dimensional parameter is illustrated. To validate the effectiveness of the proposed energy model and determine the energy-efficient machining configurations with related to machine tools, workpieces and process parameters, a series of experiments are carried out on a CNC vertical machining center. Experimental results show that the optimal machining configurations can effectively reduce energy consumption and simultaneously improve energy-efficiency of CNC machining.
Keywords: Energy-efficiency; Multi-dimension coupling; Machining energy consumption; Multi-objective optimization; CNC machining (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544223006382
Full text for ScienceDirect subscribers only
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:eee:energy:v:274:y:2023:i:c:s0360544223006382
DOI: 10.1016/j.energy.2023.127244
Access Statistics for this article
Energy is currently edited by Henrik Lund and Mark J. Kaiser
More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().