Towards comprehensive digital evaluation of low-carbon machining process planning

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.

Prediction of the onset of shear localization based on machine learning

“…Several researchers have been devoted to the application of machine learning methods in the field of machining. Examples could be the low-carbon machining process planning (Chen et al, 2022), prediction of surface roughness in high-pressure jet-assisted turning (Kramar et al, 2016), modeling of charge geometry and parameters on the depth of penetration in explosive cutting (Nariman–Zadeh et al, 2003), optimization of machining process parameters (Famili, 1994; Pourmostaghimi et al, 2020), development of support systems for the proper selection of machine tools and machining process parameters (Rojek, 2017), selection of the proper cutting fluids based on the machining process such as milling, grinding, honing, and lapping (Mogush et al, 1988), prediction of the micro-end mill and micro-drills failure (Sevil and Ozdemir, 2011), and development of processing resource allocations for smart workshops in cloud manufacturing and its optimization (Hui et al, 2021). However, based on the authors’ knowledge, the use of machine-learning to predict the onset of shear localization has not been reported in the literature.…”