Improving the galvanized roll stock production technology by using machine learning methods: a case study of the novolipetsk steel (NLMK) continuous hot-dip galvanizing unit (CHGU-1)

Author:

Toroptseva Yu. S.,Kuznetsov A. V.,Kotikov A. L.

Publisher

Springer Science and Business Media LLC

Reference9 articles.

1. Wang Y, Chen C, Wang J (2014) Temperature prediction in hot dip galvanizing using neural network models. J Mater Process Techn 214(10):2071–2079

2. Shen Y, Chen L, Yang Y, Li X (2019) A machine learning model for predicting the quality of hot-dip galvanized coating. J Mater Eng Perform: 2184–2191

3. Colla V et al (2020) A modular machine-learning-based approach to improve tensile properties uniformity along hot dip galvanized steel strips for automotive applications. Metals 10(7):923. https://doi.org/10.3390/met10070923

4. Proskurkin YV, Popovich VA, Moroz AT (1988) Galvanization: handbook. Metallurgiya, Moscow

5. Nucor corporation. https://nucor.com/. Accessed 1 Oct 2023

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