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    ARTICLE

    Quantifying Solid Solution Strengthening in Nickel-Based Superalloys via High-Throughput Experiment and Machine Learning

    Zihang Li1,#, Zexin Wang1,#, Zi Wang2, Zijun Qin1, Feng Liu1, Liming Tan1,*, Xiaochao Jin3,*, Xueling Fan3, Lan Huang1

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.2, pp. 1521-1538, 2023, DOI:10.32604/cmes.2022.021639

    Abstract Solid solution strengthening (SSS) is one of the main contributions to the desired tensile properties of nickel-based superalloys for turbine blades and disks. The value of SSS can be calculated by using Fleischer’s and Labusch’s theories, while the model parameters are incorporated without fitting to experimental data of complex alloys. In this work, four diffusion multiples consisting of multicomponent alloys and pure Ni are prepared and characterized. The composition and microhardness of single γ phase regions in samples are used to quantify the SSS. Then, Fleischer’s and Labusch’s theories are examined based on high-throughput experiments, respectively. The fitted solid solution… More >

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