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Improved data-driven performance of Charpy impact toughness via literature-assisted production data in pipeline steel

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Abstract

Pipeline transportation is one of the most economical ways to transport crude oil and natural gas over long distances. High toughness is one of the important qualities of pipeline steel to ensure safe transportation, wherein a key factor characterizing toughness is Charpy impact toughness (CIT). In this work, according to the production line data provided by a steel mill and the experimental data collected in literature, two machine learning model construction strategies were proposed. One was based solely on the production line dataset, and the other was based on the production line dataset together with the literature dataset. In these two strategies, the random forest model displayed the best prediction results, the accuracy of strategy I was 0.58, and the accuracy of strategy II was 0.90, wherein literature data effectively improved the CIT prediction accuracy. Finally, an optimized CIT model based on machine learning algorithms was established. The proposed strategy of literature data-assisted production line data provides a new perspective for optimizing and predicting the performance of traditional structural materials.

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Authors and Affiliations

  1. Beijing Advanced Innovation Center for Materials Genome Engineering, Innovation Research Institute for Carbon Neutrality, University of Science and Technology Beijing, Beijing, 100083, China

    ChunLei Shang, HongHui Wu, YiMian Chen, GuangFei Pan, ShuiZe Wang, GuiLin Wu, JunHeng Gao, HaiTao Zhao, ChaoLei Zhang & XinPing Mao

  2. State Key Laboratory of Metal Materials for Marine Equipment and Application, Anshan, 114009, China

    ChuanJun Wang & WenYue Liu

  3. Ansteel Beijing Research Institute Co. Ltd., Beijing, 102200, China

    ChuanJun Wang & WenYue Liu

Authors
  1. ChunLei Shang
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  2. ChuanJun Wang
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  3. HongHui Wu
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Corresponding authors

Correspondence to HongHui Wu, WenYue Liu or ShuiZe Wang.

Additional information

This work was supported by the National Natural Science Foundation of China (Grant Nos. 52122408, 51901013, 52071023), H.H. Wu also thanks the financial support from the Fundamental Research Funds for the Central Universities (University of Science and Technology Beijing) (Grant Nos. FRF-TP-2021-04C1, and 06500135). The computing work is supported by USTB MatCom of Beijing Advanced Innovation Center for Materials Genome Engineering.

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The supporting information is available online at https://tech.scichina.com and https://link.springer.com. The supporting materials are published as submitted, without typesetting or editing. The responsibility for scientific accuracy and content remains entirely with the authors.

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Shang, C., Wang, C., Wu, H. et al. Improved data-driven performance of Charpy impact toughness via literature-assisted production data in pipeline steel. Sci. China Technol. Sci. 66, 2069–2079 (2023). https://doi.org/10.1007/s11431-023-2372-x

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  • DOI: https://doi.org/10.1007/s11431-023-2372-x

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