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Supervisor：Jiangxi University Of Science And Technology
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Jiangxi Nonferrous Metals Society
ISSN：1674-9669
CN：36-1311/TF
CODEN YJKYA9
Volume 13 Issue 6
Dec.  2022
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Abstract
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MIAO Haibin, XIANG Chaojian, ZHANG Zhikuo, LIU Shengnan, HUANG Dongnan, WU Yongfu. Modeling analysis of yield strength and earing ratio of sheet metal based on machine learning algorithm[J]. Nonferrous Metals Science and Engineering, 2022, 13(6): 67-73. DOI: 10.13264/j.cnki.ysjskx.2022.06.009
Citation: MIAO Haibin, XIANG Chaojian, ZHANG Zhikuo, LIU Shengnan, HUANG Dongnan, WU Yongfu. Modeling analysis of yield strength and earing ratio of sheet metal based on machine learning algorithm[J]. Nonferrous Metals Science and Engineering, 2022, 13(6): 67-73. DOI: 10.13264/j.cnki.ysjskx.2022.06.009
shu

Modeling analysis of yield strength and earing ratio of sheet metal based on machine learning algorithm

  • 1.

    CHINALCO Materials Application Research Institute Co., Ltd., Beijing 102209, China

  • 2.

    Beijing Science and Technology Cooperation Center, Beijing 100195, China

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  • Received Date: March 17, 2021
  • Revised Date: January 25, 2022
  • Available Online: January 15, 2023
    Graphical Abstract
    • Abstract
  • In view of the high earing ratio and unstable yield strength during 1070 sheet metal production, a "composition-process-performance" model was built with a random forest algorithm based on production data. The following factors were chosen as independent variables: cold rolling reduction, hot rolling finishing temperature, Fe content, Fe/Si, etc., and the accuracies of the yield strength model (R2) and earing model were 0.75 and 0.87, respectively. Based on those models, the influence of various parameters on yield strength and earing was quantitatively studied. The variable weight coefficients and SHAP values of their variables were calculated with the model. The most remarkable factor for the yield strength was the cold rolling reduction, which was positively correlated, and the most remarkable factor for earing ratio was the Fe content, which was negatively correlated. Models were used for property predictions with given process parameters, and optimized parameters were achieved.
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    • References (22)
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