Results 91 to 100 of about 248,320 (248)
Reproduction of stacking fault energy calculations from literature with a semi‐automated large language model‐assisted extraction procedure: extraction of simulation protocol, atomistic structures, computational parameters, and reported results, ontology alignment, knowledge graph construction and, finally, recomputation forvalidation.
Sepideh Baghaee Ravari +5 more
wiley +1 more source
Background Data preparation is a fundamental aspect of data engineering, a prerequisite for later tasks such as data visualization, reporting, and training machine learning models.
Paul Heinzlreiter, Károly Bósa
doaj +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
Background Data preparation is a fundamental aspect of data engineering, a prerequisite for later tasks such as data visualization, reporting, and training machine learning models.
Paul Heinzlreiter, Károly Bósa
doaj +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
A Practical Noise2Noise Denoising Pipeline for High‐Throughput Raman Spectroscopy
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
Practical big data techniques for end-to-end machine learning deployment: a comprehensive review
In recent years, data generated from diverse sources has grown exponentially, giving rise to new challenges for processing and analysis under the umbrella of “big data".
Ahmed El-Sayed +2 more
doaj +1 more source
X‐ray computed tomography reveals how process‐induced defects evolve from green to sintered states in Fused Filament Fabrication (FFF)‐manufactured 17‐4PH stainless steel. Internal porosity, weakest cross‐sections, and fracture locations show strong correlation with tensile performance, demonstrating the potential of computed tomography (CT)‐based ...
György Ledniczky +3 more
wiley +1 more source
Manufacturing problems such as heat treatment‐induced cracking hinder the widespread application of the laser powder bed fusion (LPBF) process to superalloys. In this study, cracks in the LPBF components of Inconel 738LC superalloy are characterized after heat treatment at various temperature ranges, revealing two distinct cracking behaviors.
Kosuke Kuwabara +4 more
wiley +1 more source
This review comprehensively evaluates extrusion‐based additive manufacturing for advanced ceramics, detailing feedstock options and key process parameters. By critically addressing defect mechanisms like porosity and cracking, the work highlights optimization strategies through machine learning and advanced postprocessing.
Meisam Bakhtiari +4 more
wiley +1 more source

