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

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 gamma 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 coefficients are obtained based on Labusch's theory and experimental data, indicating higher accuracy. Furthermore, six machine learning algorithms are established, providing a more accurate prediction compared with traditional physical models and fitted physical models. The results show that the coupling of high-throughput experiments and machine learning has great potential in the field of performance prediction and alloy design.