Revealing the hot deformation behavior of AZ42 Mg alloy by using 3D hot processing map based on a novel NGO-ANN model

The hot deformation behavior of AZ42 alloy was observed using thermal compression tests at a temperature scope of 250–400 °C and strain rate scope of 0.001–1 s−1. True stress-strain curves exhibited a combination of work hardening and dynamic softening features. A Northern Goshawk algorithm (NGO)-op...

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Published in:Journal of Materials Research and Technology
Main Authors: Mengtao Ning, Xiaomin Chen, Yongcheng Lin, Hongwei Hu, Xiaojie Zhou, Jian Zhang, Xianzheng Lu, You Wu, Jian Chen, Qiang Shen
Format: Article in Journal/Newspaper
Language:English
Published: Elsevier 2023
Subjects:
Online Access:https://doi.org/10.1016/j.jmrt.2023.10.073
https://doaj.org/article/1753bb929ab649dfae31f103e23c7ea8
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spelling ftdoajarticles:oai:doaj.org/article:1753bb929ab649dfae31f103e23c7ea8 2024-09-15T18:25:45+00:00 Revealing the hot deformation behavior of AZ42 Mg alloy by using 3D hot processing map based on a novel NGO-ANN model Mengtao Ning Xiaomin Chen Yongcheng Lin Hongwei Hu Xiaojie Zhou Jian Zhang Xianzheng Lu You Wu Jian Chen Qiang Shen 2023-11-01T00:00:00Z https://doi.org/10.1016/j.jmrt.2023.10.073 https://doaj.org/article/1753bb929ab649dfae31f103e23c7ea8 EN eng Elsevier http://www.sciencedirect.com/science/article/pii/S223878542302522X https://doaj.org/toc/2238-7854 2238-7854 doi:10.1016/j.jmrt.2023.10.073 https://doaj.org/article/1753bb929ab649dfae31f103e23c7ea8 Journal of Materials Research and Technology, Vol 27, Iss , Pp 2292-2310 (2023) AZ42 alloy Northern Goshawk optimization Artificial neural network model Processing map Mining engineering. Metallurgy TN1-997 article 2023 ftdoajarticles https://doi.org/10.1016/j.jmrt.2023.10.073 2024-08-05T17:49:59Z The hot deformation behavior of AZ42 alloy was observed using thermal compression tests at a temperature scope of 250–400 °C and strain rate scope of 0.001–1 s−1. True stress-strain curves exhibited a combination of work hardening and dynamic softening features. A Northern Goshawk algorithm (NGO)-optimized artificial neural network (ANN) model was proposed. The established NGO-ANN model demonstrated impressive prediction accuracy, achieving a high determination coefficient of 0.991, a mean absolute percentage error of 3.51 %, and a root mean square error of 2.73. Subsequently, three-dimensional (3D) hot processing map based on the dynamic material model (DMM) theory was created. There were three different regions within the processing maps: the flow instability region (region A: 250–260 °C, 0.02–1 s−1, and region B: 300–400 °C, 0.01–0.1 s−1), high-power dissipation coefficient region (region C: 350–400 °C, 0.001–0.02 s−1, and region D: 300–350 °C, 0.5–1 s−1), and low power dissipation efficiency safety region (region E: the rest ones). Microstructural analysis revealed significant local plastic flow features in the flow instability region and a combination of coarse initial deformation grains and fine dynamic recrystallization (DRX) grains in the low power dissipation efficiency safety region. Fine and uniform grains were observed in the high-power dissipation efficiency region with DRX degree VDRX as high as 85.6 %, resulting in the best mechanical properties. Based on the established 3D hot processing map, the optimal process domains were determined. Article in Journal/Newspaper Northern Goshawk Directory of Open Access Journals: DOAJ Articles Journal of Materials Research and Technology 27 2292 2310
institution Open Polar
collection Directory of Open Access Journals: DOAJ Articles
op_collection_id ftdoajarticles
language English
topic AZ42 alloy
Northern Goshawk optimization
Artificial neural network model
Processing map
Mining engineering. Metallurgy
TN1-997
spellingShingle AZ42 alloy
Northern Goshawk optimization
Artificial neural network model
Processing map
Mining engineering. Metallurgy
TN1-997
Mengtao Ning
Xiaomin Chen
Yongcheng Lin
Hongwei Hu
Xiaojie Zhou
Jian Zhang
Xianzheng Lu
You Wu
Jian Chen
Qiang Shen
Revealing the hot deformation behavior of AZ42 Mg alloy by using 3D hot processing map based on a novel NGO-ANN model
topic_facet AZ42 alloy
Northern Goshawk optimization
Artificial neural network model
Processing map
Mining engineering. Metallurgy
TN1-997
description The hot deformation behavior of AZ42 alloy was observed using thermal compression tests at a temperature scope of 250–400 °C and strain rate scope of 0.001–1 s−1. True stress-strain curves exhibited a combination of work hardening and dynamic softening features. A Northern Goshawk algorithm (NGO)-optimized artificial neural network (ANN) model was proposed. The established NGO-ANN model demonstrated impressive prediction accuracy, achieving a high determination coefficient of 0.991, a mean absolute percentage error of 3.51 %, and a root mean square error of 2.73. Subsequently, three-dimensional (3D) hot processing map based on the dynamic material model (DMM) theory was created. There were three different regions within the processing maps: the flow instability region (region A: 250–260 °C, 0.02–1 s−1, and region B: 300–400 °C, 0.01–0.1 s−1), high-power dissipation coefficient region (region C: 350–400 °C, 0.001–0.02 s−1, and region D: 300–350 °C, 0.5–1 s−1), and low power dissipation efficiency safety region (region E: the rest ones). Microstructural analysis revealed significant local plastic flow features in the flow instability region and a combination of coarse initial deformation grains and fine dynamic recrystallization (DRX) grains in the low power dissipation efficiency safety region. Fine and uniform grains were observed in the high-power dissipation efficiency region with DRX degree VDRX as high as 85.6 %, resulting in the best mechanical properties. Based on the established 3D hot processing map, the optimal process domains were determined.
format Article in Journal/Newspaper
author Mengtao Ning
Xiaomin Chen
Yongcheng Lin
Hongwei Hu
Xiaojie Zhou
Jian Zhang
Xianzheng Lu
You Wu
Jian Chen
Qiang Shen
author_facet Mengtao Ning
Xiaomin Chen
Yongcheng Lin
Hongwei Hu
Xiaojie Zhou
Jian Zhang
Xianzheng Lu
You Wu
Jian Chen
Qiang Shen
author_sort Mengtao Ning
title Revealing the hot deformation behavior of AZ42 Mg alloy by using 3D hot processing map based on a novel NGO-ANN model
title_short Revealing the hot deformation behavior of AZ42 Mg alloy by using 3D hot processing map based on a novel NGO-ANN model
title_full Revealing the hot deformation behavior of AZ42 Mg alloy by using 3D hot processing map based on a novel NGO-ANN model
title_fullStr Revealing the hot deformation behavior of AZ42 Mg alloy by using 3D hot processing map based on a novel NGO-ANN model
title_full_unstemmed Revealing the hot deformation behavior of AZ42 Mg alloy by using 3D hot processing map based on a novel NGO-ANN model
title_sort revealing the hot deformation behavior of az42 mg alloy by using 3d hot processing map based on a novel ngo-ann model
publisher Elsevier
publishDate 2023
url https://doi.org/10.1016/j.jmrt.2023.10.073
https://doaj.org/article/1753bb929ab649dfae31f103e23c7ea8
genre Northern Goshawk
genre_facet Northern Goshawk
op_source Journal of Materials Research and Technology, Vol 27, Iss , Pp 2292-2310 (2023)
op_relation http://www.sciencedirect.com/science/article/pii/S223878542302522X
https://doaj.org/toc/2238-7854
2238-7854
doi:10.1016/j.jmrt.2023.10.073
https://doaj.org/article/1753bb929ab649dfae31f103e23c7ea8
op_doi https://doi.org/10.1016/j.jmrt.2023.10.073
container_title Journal of Materials Research and Technology
container_volume 27
container_start_page 2292
op_container_end_page 2310
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