Results 151 to 160 of about 121,938 (266)
Building machine‐readable vocabularies for materials science is slow, expert‐driven work. This study benchmarks 13 large language models on two of its first steps: finding candidate terms in engineering articles and deciding where they belong in a class hierarchy.
Thomas Bjarsch +3 more
wiley +1 more source
PruDensNet: a parameter efficient depthwise separable CNN for MRI-based brain tumor classification. [PDF]
Arman M +5 more
europepmc +1 more source
Hydrogen‐Assisted Fracture of Iron‐Based Fe–Ni–Al Alloys
Principal relations and fracture mechanisms of single‐phase and precipitate‐strengthened Fe–Ni–Al alloys subjected to prior electrochemical hydrogen charging are identified. The mechanisms of hydrogen effect on strength and microhardness are discussed, including hydrogen‐induced increase in microhardness and the role of hydrogen in fracture behavior ...
Nataliya Yadzhak +3 more
wiley +1 more source
In Situ Micromechanical Study of Bimodal γ′–γ″ Precipitate Assemblies in Ni–Cr–Al–Nb Superalloy
A Ni–Cr–Al–Nb superalloy with a bimodal γ′–γ″ precipitate distribution is developed. Composite precipitate assemblies form through heterogeneous nucleation, effectively impeding dislocation motion. Micropillar compression reveals high strength at room and elevated temperatures, governed by precipitate shearing, with coupled faulting mechanisms ...
Ujjval Bansal +4 more
wiley +1 more source
Service-oriented vulnerability assessment for the larger-scale high speed railway infrastructure network: a case in China. [PDF]
Zhang H, Xing H, Ma X, Jia L.
europepmc +1 more source
Dislocation cutting of γ′ precipitates in Ni‐based superalloys is investigated by linking atomistic simulations with discrete dislocation dynamics. The critical cutting stress is shown to be governed by the antiphase boundary energy, while line tension effects promote edge‐preferred cutting.
Frédéric Houllé +9 more
wiley +1 more source
Adaptive Multi-Wave Sampling for Efficient Chart Validation. [PDF]
Hahn G, Schneeweiss S, Wang SV.
europepmc +1 more source
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer +4 more
wiley +1 more source
DynamicFU: Contribution-Aware Dynamic Federated Unlearning for Industrial IoT. [PDF]
Wu Z, He B, Si Z, Liao X, Su C.
europepmc +1 more source
This work studies the anisotropic behavior of circular notched tensile specimens of Ni‐base superalloy single crystals during high‐temperature tensile creep along [001], [110], and [111]. Correlative scale‐bridging imaging of specimens reveals early rupture along [110] because 1) plastic deformation proceeds faster at notch center; 2) more (brittle ...
Leonardo Agudo Jácome +6 more
wiley +1 more source

