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The coarsening of γ' precipitates in superalloys involves multiple factors and impacts the performance of mechanical properties, such as strength and creep resistance. Classical kinetic theory describes the coarsening of γ' under limited conditions, as it shows deficiencies in γ' precipitation with excessive coarsening. Finding effective factors underlying the kinetic behavior significantly affecting precipitate coarsening remains largely unsolved. We address this by using machine learning models to identify materials descriptors describing γ' precipitate coarsening in Co-based superalloys. Using descriptors that include Young's modulus difference and valence electron number, we obtain an explicit relation assisted by symbolic regression that fits the experimental γ' size data far better than classical kinetic theory in describing γ' coarsening. We infer that the Young's modulus difference dominates γ' coarsening mechanism in different Co-based superalloys. Moreover, alloys containing additional elements with large Young's modulus should favor smaller γ' precipitates size, thus providing guidance for designing advanced multicomponent Co-based superalloys with high γ' coarsening resistance. [Display omitted] We utilize a computer vision framework incorporating the deep learning-based convolutional neural networks to extract accurate microstructural information from γ/γ' microstructure images for analysis in classical kinetic theories and machine learning (ML) models. Coupled with the classical kinetic theory analysis, it can well fit the earlier coarsening of regular γ' precipitates in some Co-based superalloys. However, the classical kinetic theory can not give a reasonable fitting and explanation on the fast coarsening of irregular γ' precipitate, which fully exposes its deficiency in dealing with practical problems. We use ML models and proposed descriptor selection strategy by fitting experimental γ' size data to describe γ' precipitate coarsening using descriptors such as Young's modulus difference (σ E) and valence electron number (V E N), which we identify from ML models. The relation we propose for γ' size prediction equation in terms of aging temperature (T), aging time (t), σ E and V E N is far superior in fitting to the data than classical kinetic theory, and Co-based superalloys with large σ E value tend to follow the γ' coarsening predicted by LSW theory. For strengthening materials, this model for γ' size, which inversely depends on σ E , predicts that the addition of elements with large Young's modulus (E), such as W, Mo, Re and Ru will favor smaller γ' precipitates in Co-based superalloys. This provides a means to design multicomponent Co-based superalloys with high γ' coarsening resistance. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, the tensile mechanical properties and fatigue strengths of single-crystal and columnar-grained polycrystalline Cu-12wt.%Al alloy rods prepared by Ohno continuous casting (OCC) technology were investigated and compared with those properties of polycrystalline Cu-12wt.%Al alloy prepared by conventional casting. The results of tensile tests indicated that single-crystal and columnar-grained samples had better mechanical properties comparing with the conventional polycrystalline sample. For single-crystal samples, the elongation was 8.7% and the tensile strength reached to 717MPa, which was 2.2 times as much as that of conventional polycrystalline samples (σb=332MPa). For columnar-grained samples, the tensile strength was 380MPa and the elongation reached 24.2%, which was 6.5 times higher than that of conventional polycrystalline alloy (δ=3.7%). The fatigue limits of single-crystal, columnar-grained and conventional polycrystalline alloys measured by up and down method were 413MPa, 303MPa and 256MPa, respectively. Compared with the conventional polycrystalline sample, the single-crystal and columnar-grained samples had higher fatigue limits, which were 1.6 times and 1.2 times as that of conventional polycrystalline sample. Comparing with the aging state of QBe2.0 with the tensile strength of 1250MPa and fatigue strength of 200MPa, the tensile strengths of single-crystal and columnar-grained Cu-12wt.%Al alloys were about 70% and 30% of that of QBe2.0, but the fatigue strengths were twice and 1.5 times of that of QBe2.0, respectively. [Copyright &y& Elsevier]

Abstract: The present work conducts crystal characterization by High Resolution Transmission Electron Microscopy (HRTEM) on Cu/Sn-nanolayer/Ni sandwich structures associated with the use of Energy Dispersive X-ray (EDX) analysis. The results show that DO22–(Cu,Ni)3Sn intermetallic compound (IMC) ordered structure is formed in the sandwich structures at the as-electrodeposited state. The formed DO22-(Cu,Ni)3Sn IMC is a homogeneous layer with a thickness about 10nm. The DO22–(Cu,Ni)3Sn IMC nanolayer is stable during annealing at 250°C for 810min. The formation and stabilization of the metastable DO22–(Cu,Ni)3Sn IMC nanolayer are attributed to the less strain energy induced by lattice mismatch between the DO22 IMC and fcc Cu crystals in comparison with that between the equilibrium DO3 IMC and fcc Cu crystals. [Copyright &y& Elsevier]

Abstract: The Energy-Dispersive-X-ray-based permeation and oxidation test has been further developed by an improved theoretical analysis, in which chemical potential gradients rather than concentration gradients are employed. The developed test is able to characterize diffusion kinetics in diffusion barriers at the nanometer scale. The Cu flux coefficient in (Cu, Ni)3Sn intermetallic compound nanolayers was determined from the test to be 8.48×10−15 mol·(m·s·J/mol)–1 exp(−52.3kJ·mol−1/RT) in a temperature range of 250°C–400°C. [Copyright &y& Elsevier]

This research aims to provide a solution to the scarcity and fragmentation of industrial data on die casting aluminum alloys. Quantifying the coupling between die casting process-composition-properties of aluminum alloys through small datasets, is a critical step in predicting part properties and optimizing process selection. To visualize the connections and discuss the effect of the interaction between different parameters on the property, data is fed into a self-organizing mapping model. Whereafter, an innovative data extension method is proposed to predict both yield and tensile strengths with more than 96% accuracy using a small data set. Moreover, two novel methods of multi-parameter combined range selection, the multi-objective optimization based on the agent model and the superimposition of the contour map, are guided by being informed in which region the mechanical properties fall. Finally, the feasibility of application range selection method is verified experimentally. Mapping based on data-driven process-composition-properties relationships and the free combination of application ranges are reliable theoretical solutions which are valuable for practical applications. [ABSTRACT FROM AUTHOR]

[Display omitted] • We have proposed an augmentation strategy for empirical formula based on machine learning mothed. • A novel empirical formula of Ms as the function of compositions was fitted based on an augmented dataset combined experimental data with predicted data. • Compared with previous empirical formula, the accuracy and robustness of the novel empirical formula we proposed was significantly improved without additional experimental cost. • This strategy offers a recipe to build empirical formula based on a small experimental dataset. Tuning the martensite transformation temperature through composition design has become an important way to broaden the applicable temperature range of shape memory alloys (SMAs). The empirical formula based on traditional statistics is a key reference for composition design. Due to the lack of experimental data, a large deviation may exist among the prediction results from the empirical formulas obtained by different data sources. In present work, we proposed an augmentation strategy of empirical formula based on a machine learning method to build the relationship between martensite transformation start temperature (Ms) and compositions in Cu-Al-based SMA system. A series of ML models were established by physical and chemical features and a Gaussian radial basis kernel function support vector machine (SVR.rbf) model was screened out based on mathematical and domain knowledge criteria. An augmented empirical formula of Ms as the function of compositions was fitted based on the abundant augmented dataset combined experimental data with predicted data by the SVR.rbf model. Compared with previous empirical formula fitted by small experimental dataset, the accuracy and robustness of the augmented empirical formula was significantly improved without additional experimental cost. This strategy offers a recipe to build empirical formula based on a small experimental dataset. [ABSTRACT FROM AUTHOR]