Editorial for special issue on metallurgical process engineering and intelligent manufacturing

An-jun Xu , Yan-ping Bao

International Journal of Minerals, Metallurgy, and Materials ›› 2021, Vol. 28 ›› Issue (8) : 1249 -1252.

PDF
International Journal of Minerals, Metallurgy, and Materials ›› 2021, Vol. 28 ›› Issue (8) : 1249 -1252. DOI: 10.1007/s12613-021-2333-z
Article

Editorial for special issue on metallurgical process engineering and intelligent manufacturing

Author information +
History +
PDF

Cite this article

Download citation ▾
An-jun Xu, Yan-ping Bao. Editorial for special issue on metallurgical process engineering and intelligent manufacturing. International Journal of Minerals, Metallurgy, and Materials, 2021, 28(8): 1249-1252 DOI:10.1007/s12613-021-2333-z

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Yin RY. Review on the study of metallurgical process engineering. Int. J. Miner. Metall. Mater., 2021, 28(8): 1253.

[2]

Lin L, Zeng JQ. Consideration of green intelligent steel processes and narrow window stability control technology on steel quality. Int. J. Miner. Metall. Mater., 2021, 28(8): 1264.

[3]

Chu JH, Bao YP. Mn evaporation and denitrification behaviors of molten Mn steel in the vacuum refining with slag. Int. J. Miner. Metall. Mater., 2021, 28(8): 1288.

[4]

Wang JJ, Zhang LF, Cheng G, Ren Q, Ren Y. Dynamic mass variation and multiphase interaction among steel, slag, lining refractory and nonmetallic inclusions: Laboratory experiments and mathematical prediction. Int. J. Miner. Metall. Mater., 2021, 28(8): 1298.

[5]

Wu SW, Yang J, Cao GM. Prediction of the Charpy V-notch impact energy of low carbon steel using a shallow neural network and deep learning. Int. J. Miner. Metall. Mater., 2021, 28(8): 1309.

[6]

Yuan F, Xu AJ, Gu MQ. Development of an improved CBR model for predicting steel temperature in ladle furnace refining. Int. J. Miner. Metall. Mater., 2021, 28(8): 1321.

[7]

Yan YF, ZM. Multi-objective quality control method for cold-rolled products oriented to customized requirements. Int. J. Miner. Metall. Mater., 2021, 28(8): 1332.

[8]

ZM, Jiang TR, Li ZW. Multiproduct and multistage integrated production planning model and algorithm based on an available production capacity network. Int. J. Miner. Metall. Mater., 2021, 28(8): 1343.

[9]

Yang JP, Liu Q, Guo WD, Zhang JG. Quantitative evaluation of multi-process collaborative operation in steelmaking-continuous casting sections. Int. J. Miner. Metall. Mater., 2021, 28(8): 1353.

[10]

He HN, Wang XC, Peng GZ, Xu D, Liu Y, Jiang M, Wu ZD, Zhang D, Yan H. Intelligent logistics system of steel bar warehouse based on ubiquitous information. Int. J. Miner. Metall. Mater., 2021, 28(8): 1367.

[11]

Xu ZJ, Zheng Z, Gao XQ. Operation optimization of the steel manufacturing process: A brief review. Int. J. Miner. Metall. Mater., 2021, 28(8): 1274.

[12]

Liu S, Xie S, Zhang Q. Multi-energy synergistic optimization in steelmaking process based on energy hub concept. Int. J. Miner. Metall. Mater., 2021, 28(8): 1378.

[13]

Xu T, Song G, Yang Y, Ge PX, Tang LX. Visualization and simulation of steel metallurgy processes. Int. J. Miner. Metall. Mater., 2021, 28(8): 1387.

AI Summary AI Mindmap
PDF

139

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/