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Creep rupture life predictions for Ni-based single crystal superalloys with automated machine learning

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摘要

数据驱动蠕变断裂寿命预测的最先进技术将微观结构和变形机制纳入机器学习。然而, 在进行实验之前, 未知合金的微观结构和变形机制是不可获得和不确定的, 因此限制了模型的外推。我们报道了一个简化、准确和可推广的替代模型, 使用在Autogluon中实现的自动机器学习算法预测镍基单晶高温合金的蠕变断裂寿命。在不将微观结构信息或变形机制纳入机器学习框架的情况下, 我们证明了可以在独立的测试数据上提高蠕变断裂寿命的预测精度。避免了将微观结构和变形机制与机器学习相结合, 不仅使替代模型易于适用于广阔的未探索的成分/加工空间, 而且有助于新合金的有效逆向设计。我们的工作表明, 自动机器学习软件可以在最少的人为干预下创建代理模型, 在高温合金的设计中显示出良好前景。

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Acknowledgements

This study was financially supported by the National Key Research and Development Program of China (No. 2021YFB3702601), the National Science and Technology Major Project of China (No. J2019-VI-0023-0140), the National Natural Science Foundation of China (No. 52002326) and the Natural Science Foundation of Chongqing (No. cstc2021jcyj-msxmX0602).

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Authors and Affiliations

  1. State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi’an, 710072, China

    Chang-Lu Zhou, Rui-Hao Yuan, Wei-Jie Liao, Ting-Huan Yuan, Jiang-Kun Fan, Bin Tang, Ping-Xiang Zhang & Jin-Shan Li

  2. Chongqing Innovation Center, Northwestern Polytechnical University, Chongqing, 401120, China

    Rui-Hao Yuan

  3. AiMaterials Research LLC, Santa Fe, NM, 87501, USA

    Turab Lookman

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  1. Chang-Lu Zhou
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Correspondence to Rui-Hao Yuan or Jin-Shan Li.

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Zhou, CL., Yuan, RH., Liao, WJ. et al. Creep rupture life predictions for Ni-based single crystal superalloys with automated machine learning. Rare Met. 43, 2884–2890 (2024). https://doi.org/10.1007/s12598-023-02559-8

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