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Energy consumption optimisation for machining processes based on numerical control programs.

Authors :
Feng, Chunhua
Wu, Yilong
Li, Weidong
Qiu, Binbin
Zhang, Jingyang
Xu, Xun
Source :
Advanced Engineering Informatics. Aug2023, Vol. 57, pN.PAG-N.PAG. 1p.
Publication Year :
2023

Abstract

• An energy model is set up based on energy consumption analyses of machining processes; • NC codes are structurally evaluated and then popularised into the energy model; • A hybrid GA-ACA algorithm is designed to minimise air-cutting toolpaths to optimise the energy model. Machining processes comprise numerous energy consumption activities. Given the significance of the circular economy and manufacturing sustainability to modern societies, it is paramount to design effective methodologies to accomplish energy-efficient machining processes. With this aim, this research presents a new approach of energy consumption optimisation for machining processes based on numerical control (NC) programs. In the approach, the following innovative characteristics are exhibited: (i) An energy model is systematically established based on a detailed analysis of energy consumption activities in machining processes; (ii) NC programs for specific machining processes are assessed in detail and popularised into the energy model for instantiation; (iii) An optimisation algorithm hybridising the genetic algorithm and the ant colony algorithm is designed to minimise air-cutting toolpaths to optimise the energy model. Two case studies were conducted to validate the presented approach. The case studies revealed that the accuracy of the energy model was 95.3% of the actual energy consumption. The studies also showed that, based on the optimised energy model, the total length of air-cutting toolpaths was reduced by 43.8%, and the total machining time was diminished by 25.8%. It can be concluded that the developed approach can achieve substantial energy savings, and therefore it is highly promising to support machining industries to meet their sustainable targets. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
14740346
Volume :
57
Database :
Academic Search Index
Journal :
Advanced Engineering Informatics
Publication Type :
Academic Journal
Accession number :
171827840
Full Text :
https://doi.org/10.1016/j.aei.2023.102101