Browsing by Author "Yu, T"

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    Domain knowledge discovery from abstracts of scientific literature on Nickel-based single crystal superalloys
    (Springer Nature, 2023-04-27) Liu, Y; Ding, L; Yang, ZW; Ge, XY; Liu, DH; Liu, W; Yu, T; Avdeev, M; Shi, SQ
    Despite the huge accumulation of scientific literature, it is inefficient and laborious to manually search it for useful information to investigate structure-activity relationships. Here, we propose an efficient text-mining framework for the discovery of credible and valuable domain knowledge from abstracts of scientific literature focusing on Nickel-based single crystal superalloys. Firstly, the credibility of abstracts is quantified in terms of source timeliness, publication authority and author’s academic standing. Next, eight entity types and domain dictionaries describing Nickel-based single crystal superalloys are predefined to realize the named entity recognition from the abstracts, achieving an accuracy of 85.10%. Thirdly, by formulating 12 naming rules for the alloy brands derived from the recognized entities, we extract the target entities and refine them as domain knowledge through the credibility analysis. Following this, we also map out the academic cooperative “Author-Literature-Institute” network, characterize the generations of Nickel-based single crystal superalloys, as well as obtain the fractions of the most important chemical elements in superalloys. The extracted rich and diverse knowledge of Nickel-based single crystal superalloys provides important insights toward understanding the structure-activity relationships for Nickel-based single crystal superalloys and is expected to accelerate the design and discovery of novel superalloys. © Science China Press. © 2024 Springer Nature.
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    Predicting creep rupture life of Ni-based single crystal superalloys using divide-and-conquer approach based machine learning
    (Elsevier, 2020-05-17) Liu, Y; Wu, JM; Wang, ZC; Lu, XG; Avdeev, M; Shi, S; Wang, CY; Yu, T
    Creep rupture life is a key material parameter for service life and mechanical properties of Ni-based single crystal superalloy materials. Therefore, it is of much practical significance to accurately and efficiently predict creep life. Here, we develop a divide-and-conquer self-adaptive (DCSA) learning method incorporating multiple material descriptors for rational and accelerated prediction of the creep rupture life. We characterize a high-quality creep dataset of 266 alloy samples with such features as alloy composition, test temperature, test stress, and heat treatment process. In addition, five microstructural parameters related to creep process, including stacking fault energy, lattice parameter, mole fraction of the γ' phase, diffusion coefficient and shear modulus, are calculated and introduced by the CALPHAD (CALculation of PHAse Diagrams) method and basic materials structure-property relationships, that enables us to reveal the effect of microstructure on creep properties. The machine learning explorations conducted on the creep dataset demonstrate the potential of the approach to achieve higher prediction accuracy with RMSE, MAPE and R2 of 0.3839, 0.0003 and 0.9176 than five alternative state-of-the-art machine learning models. On the newly collected 8 alloy samples, the error between the predicted creep life value and the experimental measured value is within the acceptable range (6.4486 h–40.7159 h), further confirming the validity of our DCSA model. Essentially, our method can establish accurate structure-property relationship mapping for the creep rupture life in a faster and cheaper manner than experiments and is expected to serve for inverse design of alloys. © 2020 Acta Materialia Inc. Published by Elsevier Ltd.