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Volume 28 Issue 8
Aug.  2021
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An-jun Xuand Yan-ping Bao, Editorial for special issue on metallurgical process engineering and intelligent manufacturing, Int. J. Miner. Metall. Mater., 28(2021), No. 8, pp. 1249-1252. https://doi.org/10.1007/s12613-021-2333-z
Cite this article as:
An-jun Xuand Yan-ping Bao, Editorial for special issue on metallurgical process engineering and intelligent manufacturing, Int. J. Miner. Metall. Mater., 28(2021), No. 8, pp. 1249-1252. https://doi.org/10.1007/s12613-021-2333-z
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  • Editorial

    Editorial for special issue on metallurgical process engineering and intelligent manufacturing

    + Author Affiliations
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    • [1]
      R.Y. Yin, Review on the study of metallurgical process engineering, Int. J. Miner. Metall. Mater., 28(2021), No. 8, p. 1253. doi: 10.1007/s12613-020-2220-z
      [2]
      L. Lin and J.Q. Zeng, Consideration of green intelligent steel processes and narrow window stability control technology on steel quality, Int. J. Miner. Metall. Mater., 28(2021), No. 8, p. 1264. doi: 10.1007/s12613-020-2246-2
      [3]
      J.H. Chu and Y.P. Bao, Mn evaporation and denitrification behaviors of molten Mn steel in the vacuum refining with slag, Int. J. Miner. Metall. Mater., 28(2021), No. 8, p. 1288. doi: 10.1007/s12613-021-2311-5
      [4]
      J.J. Wang, L.F. Zhang, G. Cheng, Q. Ren, and Y. Ren, Dynamic mass variation and multiphase interaction among steel, slag, lining refractory and nonmetallic inclusions: Laboratory experiments and mathematical prediction, Int. J. Miner. Metall. Mater., 28(2021), No. 8, p. 1298. doi: 10.1007/s12613-021-2304-4
      [5]
      S.W. Wu, J. Yang, and G.M. Cao, 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., 28(2021), No. 8, p. 1309. doi: 10.1007/s12613-020-2168-z
      [6]
      F. Yuan, A.J. Xu, and M.Q. Gu, Development of an improved CBR model for predicting steel temperature in ladle furnace refining, Int. J. Miner. Metall. Mater., 28(2021), No. 8, p. 1321. doi: 10.1007/s12613-020-2234-6
      [7]
      Y.F. Yan and Z.M. Lü, Multi-objective quality control method for cold-rolled products oriented to customized requirements, Int. J. Miner. Metall. Mater., 28(2021), No. 8, p. 1332. doi: 10.1007/s12613-021-2292-4
      [8]
      Z.M. Lü, T.R. Jiang, and Z.W. Li, Multiproduct and multistage integrated production planning model and algorithm based on an available production capacity network, Int. J. Miner. Metall. Mater., 28(2021), No. 8, p. 1343. doi: 10.1007/s12613-021-2310-6
      [9]
      J.P. Yang, Q. Liu, W.D. Guo, and J.G. Zhang, Quantitative evaluation of multi-process collaborative operation in steelmaking–continuous casting sections, Int. J. Miner. Metall. Mater., 28(2021), No. 8, p. 1353. doi: 10.1007/s12613-020-2227-5
      [10]
      H.N. He, X.C. Wang, G.Z. Peng, D. Xu, Y. Liu, M. Jiang, Z.D. Wu, D. Zhang, and H. Yan, Intelligent logistics system of steel bar warehouse based on ubiquitous information, Int. J. Miner. Metall. Mater., 28(2021), No. 8, p. 1367. doi: 10.1007/s12613-021-2325-z
      [11]
      Z.J. Xu, Z. Zheng, and X.Q. Gao, Operation optimization of the steel manufacturing process: A brief review, Int. J. Miner. Metall. Mater., 28(2021), No. 8, p. 1274. doi: 10.1007/s12613-021-2273-7
      [12]
      S. Liu, S. Xie, and Q. Zhang, Multi-energy synergistic optimization in steelmaking process based on energy hub concept, Int. J. Miner. Metall. Mater., 28(2021), No. 8, p. 1378. doi: 10.1007/s12613-021-2281-7
      [13]
      T. Xu, G. Song, Y. Yang, P.X. Ge, and L.X. Tang, Visualization and simulation of steel metallurgy processes, Int. J. Miner. Metall. Mater., 28(2021), No. 8, p. 1387. doi: 10.1007/s12613-021-2283-5

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