Zihang Li^1,#, Zexin Wang^1,#, Zi Wang², Zijun Qin¹, Feng Liu¹, Liming Tan^1,*, Xiaochao Jin^3,*, 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 >

Yabin Xu^1,*, Lulu Cui¹, Xiaowei Xu²

Computer Systems Science and Engineering, Vol.42, No.1, pp. 121-132, 2022, DOI:10.32604/csse.2022.022758

Abstract Fracture toughness plays a vital role in damage tolerance design of materials and assessment of structural integrity. To solve these problems of complexity, time-consuming, and low accuracy in obtaining the fracture toughness value of nickel-based superalloys through experiments. A combination prediction model is proposed based on the principle of materials genome engineering, the fracture toughness values of nickel-based superalloys at different temperatures, and different compositions can be predicted based on the existing experimental data. First, to solve the problem of insufficient feature extraction based on manual experience, the Deep Belief Network (DBN) is used to extract features, and an attention… More >