| Title |
Energy consumption optimisation for machining processes based on numerical control programs |
| ID_Doc |
10414 |
| Authors |
Feng, CH; Wu, YL; Li, WD; Qiu, BB; Zhang, JY; Xu, X |
| Title |
Energy consumption optimisation for machining processes based on numerical control programs |
| Year |
2023 |
| Published |
|
| DOI |
10.1016/j.aei.2023.102101 |
| Abstract |
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. |
| Author Keywords |
Machining processes; NC programs; Energy consumption optimisation |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:001050639300001 |
| WoS Category |
Computer Science, Artificial Intelligence; Engineering, Multidisciplinary |
| Research Area |
Computer Science; Engineering |
| PDF |
|