Results 91 to 100 of about 15,545 (216)

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

Precipitation Simulations of the O‐Phase in Ti2AlNb Alloys Processed by Laser Powder Bed Fusion

open access: yesAdvanced Engineering Materials, EarlyView.
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

Inverse Identification of Energy‐Dependent Laser Absorptivity in NiTi Laser Powder‐Bed Fusion via Calibrated Melt Pool Simulation

open access: yesAdvanced Engineering Materials, EarlyView.
A combined experimental–computational framework identifies energy‐dependent laser absorptivity for NiTi in laser powder‐bed fusion, applicable to conduction and transition modes. Single‐track experiments and thermofluid smoothed particle hydrodynamics simulations are coupled through inverse analysis of melt pool geometry.
Mohamadreza Afrasiabi   +3 more
wiley   +1 more source

Superconductivity suppression and bilayer decoupling in Pr-substituted YBa<sub>2</sub>Cu<sub>3</sub>O<sub>7-<i>δ</i></sub>. [PDF]

open access: yesProc Natl Acad Sci U S A
Yang J   +21 more
europepmc   +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

Commissioning of an expanded-field radiation technique using a gimbal-mounted linear accelerator. [PDF]

open access: yesPhys Imaging Radiat Oncol
Ono T   +8 more
europepmc   +1 more source

Advanced Electron Microscopy and Microcomputed Tomography Analysis of Single‐Crystal Ni‐Base Superalloys After Short‐Term Multiaxial Creep

open access: yesAdvanced Engineering Materials, EarlyView.
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

A Practical Noise2Noise Denoising Pipeline for High‐Throughput Raman Spectroscopy

open access: yesAdvanced Engineering Materials, EarlyView.
A lightweight and reproducible denoising pipeline for high‐throughput Raman spectroscopy is introduced, based on a 1D convolutional autoencoder trained with a Noise2Noise strategy. Using only repeated short‐exposure acquisitions, the method suppresses stochastic noise without reference spectra, enabling reliable spectral reconstruction while preserving
David Martin‐Calle   +5 more
wiley   +1 more source

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