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Energy consumption optimisation for machining processes based on numerical control programs

delete2023-08-01
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PRE
AI
C
Chunhua Feng
Y
Yilong Wu
李卫东 cover
李卫东 (Weidong Li) *
B
Binbin Qiu
张靖阳 cover
张靖阳 (Jingyang Zhang)
X
Xun Xu
DOI:10.1016/j.aei.2023.102101delete
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Abstract

Abstract

En 中文
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 aircutting 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.
Keywords:
Machining processes
NC programs
Energy consumption optimisation

Journal

Advanced Engineering Informatics cover
Advanced Engineering Informatics
IF:
9.9
Papers:
4.0K
Citations:
1.7W

Organization

U
University of Auckland
Scholars:
2.3W
Papers: 2.4W
Citations: 3.3W