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Materials Science and Technology Volume 36, 2020 - Issue 17
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Review

Advances in neural networks and potential for their application to steel metallurgy

J. L. SmithDepartment of Materials Science & Metallurgy, University of Cambridge, Cambridge, UKCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0002-0483-4382
ORCID Icon
Pages 1805-1819 | Received 20 Jul 2020, Accepted 15 Oct 2020, Published online: 05 Nov 2020

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J. F. S. Markanday. (2022) Applications of alloy design to cracking resistance of additively manufactured Ni-based alloys. Materials Science and Technology 38:16, pages 1300-1314.
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Rongshan Qin. (2021) Artificial neural network study of the electrical conductivity of mould flux. Materials Science and Technology 37:18, pages 1476-1482.
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Vladimir Bukhtoyarov, Vladimir Nelyub, Dmitry Evsyukov, Sergei Nelyub & Andrey Gantimurov. 2024. High-Performance Computing Systems and Technologies in Scientific Research, Automation of Control and Production. High-Performance Computing Systems and Technologies in Scientific Research, Automation of Control and Production 143 157 .
X.L. Luan & Z.L. Huang. (2023) Stationary random response evaluation of nonlinear oscillators with Preisach hysteresis discretized by RNN model. International Journal of Non-Linear Mechanics 154, pages 104449.
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Eli Saùl Puchi-Cabrera, Edoardo Rossi, Giuseppe Sansonetti, Marco Sebastiani & Edoardo Bemporad. (2023) Machine learning aided nanoindentation: A review of the current state and future perspectives. Current Opinion in Solid State and Materials Science 27:4, pages 101091.
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Linfang Fang, Fuyong Su, Zhen Kang & Haojun Zhu. (2023) Artificial Neural Network Model for Temperature Prediction and Regulation during Molten Steel Transportation Process. Processes 11:6, pages 1629.
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Vladimir V. Bukhtoyarov, Vadim S. Tynchenko, Vladimir A. Nelyub, Igor S. Masich, Aleksey S. Borodulin & Andrei P. Gantimurov. (2023) A Study on a Probabilistic Method for Designing Artificial Neural Networks for the Formation of Intelligent Technology Assemblies with High Variability. Electronics 12:1, pages 215.
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Yumeng Tan, Xiaowei Wang, Zitong Kang, Fei Ye, Yefeng Chen, Dewen Zhou, Xiancheng Zhang & Jianming Gong. (2022) Creep lifetime prediction of 9% Cr martensitic heat-resistant steel based on ensemble learning method. Journal of Materials Research and Technology 21, pages 4745-4760.
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Xiu Liu & Chris Aldrich. (2022) Assessing the Influence of Operational Variables on Process Performance in Metallurgical Plants by Use of Shapley Value Regression. Metals 12:11, pages 1777.
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Jingxiao Li, Xiaofang Yang, Yulong Zhu, Yongfa Zhang, Youcai Qiu & Robert Edward SandersJr.Jr.. (2022) Study of Thermal Compression Constitutive Relation for 5182-Sc-Zr Alloy Based on Arrhenius-Type and ANN Model. Crystals 12:5, pages 611.
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Xiaorong Li, Lijun Zhang, Dongchen Li & Dan Guo. (2022) Construction and Simulation of a Strategic HR Decision Model Based on Recurrent Neural Network. Journal of Mathematics 2022, pages 1-11.
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Marco Vannucci & Valentina Colla. (2021) Automatic steel grades design for Jominy profile achievement through neural networks and genetic algorithms. Neural Computing and Applications 33:23, pages 16451-16470.
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