Results 181 to 190 of about 1,259,133 (314)

Influence of Geometric Design on Mechanical Performance of Auxetic Metastructure

Advanced Engineering Materials, EarlyView.
Strategic geometric reinforcement transforms auxetic performance. This study evaluates 3D‐printed arrowhead metastructures, revealing that a modified design with local ring reinforcement suppresses premature failure to achieve superior energy absorption and structural efficiency.
Muhammad Gulzari, Supharoek Sornsomrit, Muhammad Ayaz Akbar, Stephan Rudykh   +3 more
wiley   +1 more source

Multimodal machine learning for integrating heterogeneous analytical systems. [PDF]

Anal Sci
Muroga S, Nakajima H, Shimizu T, Kobashi K, Hata K.   +4 more
europepmc   +1 more source

Modeling Dislocation Cutting of γ′ Precipitates in Ni‐Base Superalloys: Linking Atomistic and Dislocation Dynamics Simulations

Advanced Engineering Materials, EarlyView.
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, Siwen Gao, Marc Fivel, Anxin Ma, Daria Smirnova, Sriram Anand, Arun Prakash, Julien Guénolé, Alexander Hartmaier, Erik Bitzek   +9 more
wiley   +1 more source

Editorial: Robust machine learning. [PDF]

Front Artif Intell
An Y, Zhao X, Du M.
europepmc   +1 more source

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

Advanced 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, Christopher Mai, Luca Eisentraut, Andreas Bund, Ricardo Buettner   +4 more
wiley   +1 more source

Machine learning applications in sport: a scoping review. [PDF]

Front Psychol
Cattle A, Johnston K, McAuley ABT, Kelly A, Baker J.   +4 more
europepmc   +1 more source

A Practical Noise2Noise Denoising Pipeline for High‐Throughput Raman Spectroscopy

Advanced 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, Cesar Alvarez‐Llamas, Vincent Motto‐Ros, Christophe Dujardin, Jérémie Margueritat, David Rodney   +5 more
wiley   +1 more source

An Exploration of Machine Learning Methods in Human Biomonitoring. [PDF]

Int J Environ Res Public Health
Singh K, Bi J, Musende M, Collins SP, Borghese MM, Hu JMY, Pollock T, St-Amand A, Hennessy D, Buckeridge DL.   +9 more
europepmc   +1 more source

New AI‐Assisted Approach for Expanding the Solution Space: Application to Lattice Structure Design

Advanced Engineering Materials, EarlyView.
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, Y. Beideler, D. Chune, R. De Guio, T. Roland, H. Chibane   +5 more
wiley   +1 more source

Prediction of infection in the emergency department-a machine learning model. [PDF]

Front Artif Intell
Søgaard SN, Skjøt-Arkil H, Mogensen CB, Rosenvinge FS, Kronborg T.   +4 more
europepmc   +1 more source