Results 211 to 220 of about 116,477 (304)

Low Cycle Repetitive Loading of Ti‐6Al‐4V‐Epoxy Composite Lattice Structures for Enhanced Energy Dissipation and Damage Tolerance

open access: yesAdvanced Engineering Materials, EarlyView.
Composite Ti–6Al–4V–epoxy lattice structures are additively manufactured and epoxy infiltrated for cyclic loading. At low lattice volume fractions, hybridization produces synergistic gains in stiffness and energy dissipation. At higher volume fractions, synergy diminishes, although composites still exceed metallic lattices in specific energy ...
Joey Tallon   +3 more
wiley   +1 more source

Accuracy of genomic prediction for milk production traits in Mehsana buffalo. [PDF]

open access: yesFront Genet
Patel MR   +7 more
europepmc   +1 more source

Microstructure Reconstruction in Battery Electrodes Using Machine Learning Based on Low‐Voltage Focused Ion Beam–Scanning Electron Microscopy Tomography Images

open access: yesAdvanced Engineering Materials, EarlyView.
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

A comparison of different selection indexes for some economic traits in Holstein Friesian cows. [PDF]

open access: yesJ Anim Sci Technol
Kassab RA   +8 more
europepmc   +1 more source

Experimental Characterization of Mycelium‐Based Composites Under Multiple Loading Conditions

open access: yesAdvanced Engineering Materials, EarlyView.
This study examines the mechanical response of mycelium‐based composites under compression, shear, and tension using mechanical testing and imaging methods. The comparison between unpressed and hot‐pressed specimens shows that hot pressing is associated with higher compression and shear stiffnesses.
Shaghayegh Elahi   +5 more
wiley   +1 more source

Machine Learning‐Supported Analysis for Predicting and Visualizing Nonlinear Relationships Between Material Properties in Electroplated Chromium Layers

open access: yesAdvanced Engineering Materials, EarlyView.
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

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