Results 81 to 90 of about 48,732 (203)
Coarse‐grained (left) and atomistic (right) models of the shape memory polymer ESTANE ETE 75DT3 are shown schematically. The two representations bridge molecular detail and mesoscopic description. Both models capture shape memory behavior, linking segmental mobility and conformational relaxation of anisotropic chains to macroscopic recovery, and ...
Fathollah Varnik
wiley +1 more source
Low‐voltage FIB‐SEM tomography combined with a image preprocessing pipeline improves phase contrast and enables reliable machine‐learning segmentation of conductive networks in lithium‐ion battery electrodes. Structural descriptors are extracted from segmented images, done semimanually and automated, and compared.
Lisa Beran +6 more
wiley +1 more source
Precipitation Simulations of the O‐Phase in Ti2AlNb Alloys Processed by Laser Powder Bed Fusion
Simulated and experimental evolution of the O‐phase volume fraction during postprocessing of a Ti‐21Al‐25Nb (at.%) alloy processed by laser powder bed fusion. With results of sensitivity to input parameters from a thorough and quantified analysis, the interfacial energy matrix/precipitate is the most relevant input parameter for the simulation of the O‐
Silvana Tumminello +7 more
wiley +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
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
New AI‐Assisted Approach for Expanding the Solution Space: Application to Lattice Structure Design
This work introduces an innovative framework for designing structured materials by ex panding the design space through reparameterization of qualitative variables into continuous structural descriptors. Combined with machine‐learning‐based prediction and multi‐objective optimization, the approach enables the discovery of novel lattice architectures ...
G. H. Gahimbare +5 more
wiley +1 more source
Schematic representation of modes of microcrack nucleation, growth and temporary arrestment at different boundaries, with the criteria for crack propagation in three scenarios: (1) large notch, (2) defect/small sharp notch, and (3) long crack. This work revisits and integrates results on the fatigue behavior of advanced bainitic steels (in particular ...
Lucia Morales‐Rivas
wiley +1 more source
We apply a foundational machine‐learning interatomic potential based on the graph atomic cluster expansion (GRACE) to simulate the commercial Ni‐based single‐crystal superalloy CMSX‐4. Hybrid Monte‐Carlo/molecular dynamics sampling resolves short‐range order in the γ phase and L12 sublattice occupancies in the γ’ phase and connects them to stacking ...
Aditya Vishwakarma +4 more
wiley +1 more source
Dynamic performance enhancement of adjustable blade pitch angle for wind generation system applications based on artificial neural network control techniques. [PDF]
Ameen AG +3 more
europepmc +1 more source
A swelling‐programmed micropatterned hydrogel guides adherent cells through a controlled transition from cell–matrix anchoring to cadherin‐mediated cell–cell compaction, enabling rapid assembly of high‐viability spheroids with defined size and morphology.
Han Gyeol Nam +8 more
wiley +1 more source

