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Experimental Feedback to Improve the Extrapolation of Machine Learning: Application to Design of PM Cu–Al–Fe–Ni Alloys

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Abstract

To improve the engineering application of powder metallurgy Cu–12Al alloys in bearing workpieces, a machine learning (ML) procedure was proposed. The experimental feedback data were used to improve the ML extrapolation. Binary and ternary alloys data were used to build the ML model to predict quaternary alloys. A multi-objective express index was used to assess the comprehensiveness of the searched alloys. The experimental result of predicted alloy updates training data to search next alloy. Cu–13Al–5Fe–5Ni alloy was found to have higher tensile strength (381.6 MPa) and hardness (140.1 HB) than those of traditional Cu–12Al alloy. The microstructure indicated that the designed alloy has three precipitation phases to strengthen matrix. Further, the designed alloy has the balance between tensile strength and hardness. This work is of much practical significance in reducing trial-and-error tests to improve design efficiency for material design.

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Data availability

The data that supports the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgments

This work was supported by the Beijing Municipal Natural Science Foundation (No. 2212042), National Natural Science Foundation of China (No. U2141205), the USTB Project for fundamental scientific research (No. 06109125), Key-Area Research and Development Program of Guangdong Province (No. 2019B010942001), and the Natural Science Foundation of Chongqing (cstc2021jcyj-msxmX0899). The computing work was supported by USTB MatCom of Beijing Advanced Innovation Center for Materials Genome Engineering.

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Authors and Affiliations

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

    Bin Xu, Haiqing Yin & Wei Li

  2. Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing, 100083, China

    Bin Xu, Haiqing Yin, Ruijie Zhang & Wei Li

  3. School of Mechanical Engineering, Chongqing Three Gorges University, Chongqing, 404000, China

    Zhenghua Deng

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  1. Bin Xu
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  2. Haiqing Yin
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  3. Ruijie Zhang
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  4. Zhenghua Deng
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  5. Wei Li
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Correspondence to Haiqing Yin or Wei Li.

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Xu, B., Yin, H., Zhang, R. et al. Experimental Feedback to Improve the Extrapolation of Machine Learning: Application to Design of PM Cu–Al–Fe–Ni Alloys. Trans Indian Inst Met 76, 1781–1787 (2023). https://doi.org/10.1007/s12666-023-02881-w

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