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Towards comprehensive digital evaluation of low-carbon machining process planning
Published online by Cambridge University Press: 25 July 2022
- Zhaoming Chen*
- Affiliation:Chongqing University, Chongqing 400044, ChinaChongqing School, University of Chinese Academy of Sciences, Chongqing 400714, China
- Jinsong Zou
- Affiliation:Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
- Wei Wang
- Affiliation:College of Computer Science and Engineering, Chongqing University of Technology, Chongqing 400054, China
- *
- Author for correspondence: Zhaoming Chen, E-mail:zhaomingc_sc@163.com
Abstract
Low-carbon process planning is the basis for the implementation of low-carbon manufacturing technology. And it is of profound significance to improve process executability, reduce environmental pollution, decrease manufacturing cost, and improve product quality. In this paper, based on the perceptual data of parts machining process, considering the diversity of process planning schemes and factors affecting the green manufacturing, a multi-level evaluation criteria system is established from the aspects of processing time, manufacturing cost and processing quality, resource utilization, and environmental protection. An integrated evaluation method of low-carbon process planning schemes based on digital twins is constructed. Each index value is normalized by the polarized data processing method, its membership is determined by the fuzzy statistical method, and the combination weight of each index is determined by the hierarchical entropy weight method to realize the organic combination of theoretical analysis, practical experience, evaluation index, and process factors. The comprehensive evaluation of multi-process planning schemes is realized according to the improved fuzzy operation rules, and the best process planning solution is finally determined. Finally, taking the low-carbon process planning of an automobile part as an example, the feasibility and effectiveness of this method are verified by the evaluation of three alternative process planning schemes. The results show that the method adopted in this paper is more in line with the actual production and can provide enterprises with the optimal processing scheme with economic and environmental benefits, which may be helpful for more data-driven manufacturing process optimization in the future.
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- Research Article
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- Copyright
- Copyright © The Author(s), 2022. Published by Cambridge University Press
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