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 desired tensile properties nickel-based superalloys for turbine blades and disks. The value SSS can be calculated by using Fleischer’s Labusch’s theories, while model parameters are incorporated without fitting experimental data complex alloys. In this work, four diffusion multiples consisting multicomponent alloys pure Ni prepared characterized. composition microhardness single γ phase regions in samples used quantify SSS. Then, theories examined based on high-throughput experiments, respectively. fitted solid coefficients obtained theory data, indicating higher accuracy. Furthermore, six machine learning algorithms established, providing a more accurate prediction compared with traditional physical models models. results show that coupling experiments has great potential field performance alloy design.