Prediction of flow stress in Mg-3Dy alloy based on constitutive equation and PSO-SVR model

被引:1
|
作者
Liu, Yafei [1 ]
Feng, Yunduo [1 ]
Liu, Qiangbing [1 ]
Luan, Shiyu [2 ]
Li, Xiaowei [1 ]
Liu, Xiaoyu [1 ]
Zhang, Lei [1 ]
Wang, Jinhui [1 ]
机构
[1] Qinghai Univ, Qinghai Prov Engn Res Ctr High Performance Light M, Qinghai Prov Key Lab New Light Alloys, Xining 810016, Peoples R China
[2] Shenyang Univ Technol, Coll Mat Sci & Engn, Shenyang 110870, Peoples R China
关键词
magnesium alloy; constitutive model; PSO-SVR model; thermal processing map; dynamic recrystallization mechanism; MAGNESIUM ALLOY; MICROSTRUCTURE; BEHAVIOR;
D O I
10.1088/2053-1591/ad48de
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
This study conducted hot compression experiments on as-cast Mg-3Dy alloy under deformation parameters of 380 degrees C-470 degrees C and 0.001-1 s-1. The microstructure of the alloy was observed using EBSD, and the flow stress of the Mg-3Dy alloy was predicted using the Arrhenius model and the particle swarm optimization-support vector regression (PSO-SVR) model. The organizational analysis results showed that the main recrystallization mechanism in the alloy is the discontinuous dynamic recrystallization (DDRX) mechanism. The generation of twins in the alloy was mostly the result of local stress action. The optimal processing window for this alloy was determined to be 380 degrees C-470 degrees C and 0.001-0.01 s-1 through the thermal processing map. The prediction accuracies of the Arrhenius model and PSO-SVR model were evaluated using the correlation coefficient R2 and mean squared error MSE. The results showed that the PSO-SVR model significantly outperforms the Arrhenius model in prediction accuracy, with R2 value of 0.99982 and MSE of 0.074.
引用
收藏
页数:17
相关论文
共 50 条
  • [1] Forecasting tourism flow based on seasonal PSO-SVR model
    [J]. Chen, R, 1600, Systems Engineering Society of China (34):
  • [2] Wind Power Prediction Based on PSO-SVR and Grey Combination Model
    Zhang, Yi
    Sun, Hexu
    Guo, Yingjun
    [J]. IEEE ACCESS, 2019, 7 : 136254 - 136267
  • [3] Parameters optimization of air conditioning load prediction model based on PSO-SVR
    Zhou Xuan
    Yang Jian-cheng
    [J]. 2013 32ND CHINESE CONTROL CONFERENCE (CCC), 2013, : 1777 - 1782
  • [4] Energy Performance Curves Prediction of Centrifugal Pumps Based on Constrained PSO-SVR Model
    Luo, Huican
    Zhou, Peijian
    Shu, Lingfeng
    Mou, Jiegang
    Zheng, Haisheng
    Jiang, Chenglong
    Wang, Yantian
    [J]. ENERGIES, 2022, 15 (09)
  • [5] Research and analysis of the prediction model of wiped film evaporation process based on PSO-SVR
    Li, Hui
    Xu, Hailiang
    Zhao, Qiliang
    Wang, Hao
    [J]. PROCEEDINGS OF THE 33RD CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2021), 2021, : 5738 - 5742
  • [6] Prediction of Carbon Emissions Level in China's Logistics Industry Based on the PSO-SVR Model
    Chen, Liang
    Pan, Yitong
    Zhang, Dongqing
    [J]. MATHEMATICS, 2024, 12 (13)
  • [7] Power load combination forecasting based on triangular fuzzy discrete difference equation forecasting model and PSO-SVR
    Liu, Jinpei
    Wang, Piao
    Huang, Yanyan
    Wu, Peng
    Xu, Qin
    Chen, Huayou
    [J]. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2019, 36 (06) : 5889 - 5898
  • [8] Water quality prediction of copper-molybdenum mining-beneficiation wastewater based on the PSO-SVR model
    Xiaohua Fu
    Qingxing Zheng
    Guomin Jiang
    Kallol Roy
    Lei Huang
    Chang Liu
    Kun Li
    Honglei Chen
    Xinyu Song
    Jianyu Chen
    Zhenxing Wang
    [J]. Frontiers of Environmental Science & Engineering, 2023, 17
  • [9] Water quality prediction of copper-molybdenum mining-beneficiation wastewater based on the PSO-SVR model
    Fu, Xiaohua
    Zheng, Qingxing
    Jiang, Guomin
    Roy, Kallol
    Huang, Lei
    Liu, Chang
    Li, Kun
    Chen, Honglei
    Song, Xinyu
    Chen, Jianyu
    Wang, Zhenxing
    [J]. FRONTIERS OF ENVIRONMENTAL SCIENCE & ENGINEERING, 2023, 17 (08)
  • [10] Water quality prediction of copper-molybdenum mining-beneficiation wastewater based on the PSO-SVR model
    Fu Xiaohua
    Zheng Qingxing
    Jiang Guomin
    Roy Kallol
    Huang Lei
    Liu Chang
    Li Kun
    Chen Honglei
    Song Xinyu
    Chen Jianyu
    Wang Zhenxing
    [J]. Frontiers of Environmental Science & Engineering, 2023, 17 (08)