Results 211 to 220 of about 1,108,433 (294)

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

Advanced 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, Vincent Nebel, Dominik Perius, Kshitij Kumar, Yannis Heim, Laura Bies, Tobias Kraus   +6 more
wiley   +1 more source

A multisource dataset for anomaly detection and fault prediction in urban water distribution networks. [PDF]

Sci Data
Babela J, Munk M, Munkova D.
europepmc   +1 more source

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

Advanced 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, Hadi Moutablaleh, Ignatius A. Setiawan, Mehrshad Mehrpouya   +3 more
wiley   +1 more source

Study on the Dynamic Characteristics of Rub-Impact and Bearing Defect Coupled Faults in a Single-Disk Double-Bearing Rotor System. [PDF]

Materials (Basel)
Liu J, Zhang H, Sun H, Wang H, Chang Z.
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

Unsupervised Multi-Sensor Condition Monitoring of AODD Pump Systems Using Physics-Informed Health Indices and Gaussian Mixture Models. [PDF]

Sensors (Basel)
Kim SW, Kareem AB, Hur JW.
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

From Robust Control to Cyber-Resilience: A Comprehensive Overview of the Polytopic Framework for Safety-Critical Systems. [PDF]

Sensors (Basel)
Rebai SB.
europepmc   +1 more source

An Explicit Constitutive Reconstruction Framework for Mechanical Characterization of Miniaturized 3D‐Printed Specimens

Advanced Engineering Materials, EarlyView.
An explicit constitutive reconstruction framework enables accurate tensile characterization of miniaturized additively manufactured specimens using only the grip‐to‐grip displacement measurements. Progressive validation through self‐consistency, analytical comparison, and inverse finite element method (iFEM) benchmarking demonstrates constitutive ...
Junqing Leng, Caralyn Collins, Pengpeng Zhang, Joanna Zhang, Cheng Sun   +4 more
wiley   +1 more source

Multi-Modal GAN-Based Anomaly Detection for Signal Reliability Assessment in Fabric-Integrated Smart Textiles. [PDF]

Sensors (Basel)
Wu J, Fan R, Ren X.
europepmc   +1 more source