Results 171 to 180 of about 178,870 (238)

What Do Large Language Models Know About Materials?

Advanced Engineering Materials, EarlyView.
If large language models (LLMs) are to be used inside the material discovery and engineering process, they must be benchmarked for the accurateness of intrinsic material knowledge. The current work introduces 1) a reasoning process through the processing–structure–property–performance chain and 2) a tool for benchmarking knowledge of LLMs concerning ...
Adrian Ehrenhofer, Thomas Wallmersperger, Gianaurelio Cuniberti   +2 more
wiley   +1 more source

AI algorithms and IoT platforms for anomaly and failure prediction in industrial machinery-systematic review. [PDF]

Front Artif Intell
Marín Vásquez ME, Blandón Andrade JC, Toro Lazo A, López Sotelo JA.   +3 more
europepmc   +1 more source

Electrochemical Evaluation of Compressed Selective Laser Melted AlSi7Mg and AlSi10Mg Alloys in Chloride Environment

Advanced Engineering Materials, EarlyView.
The corrosion performance of AlSi7Mg and AlSi10Mg alloys produced through selective laser melting (SLM) was examined under compressive stress in a chloride environment. Electrochemical analyses, including open‐circuit potential (OCP), potentiodynamic polarization (CPP), and electrochemical impedance spectroscopy (EIS), were complemented by scanning ...
Femi John Akinfolarin, Samson Dare Oguntuyi, Peter Apata Olubambi   +2 more
wiley   +1 more source

Dual graph attention network for robust fault diagnosis in photovoltaic inverters. [PDF]

Sci Rep
Bhadra AB, Rime MHK, Sarker Y, Bhuiyan EA, Hossen MJ, Morol MK.   +5 more
europepmc   +1 more source

A Workflow to Accelerate Microstructure‐Sensitive Fatigue Life Predictions

Advanced Engineering Materials, EarlyView.
This study introduces a workflow to accelerate predictions of microstructure‐sensitive fatigue life. Results from frameworks with varying levels of simplification are benchmarked against published reference results. The analysis reveals a trade‐off between accuracy and model complexity, offering researchers a practical guide for selecting the optimal ...
Luca Loiodice, Krzysztof S. Stopka, Michael D. Sangid   +2 more
wiley   +1 more source

Revolutionizing nanosatellites' data integrity with SEEnet: A real-time ensemble learning approach for Single-Event Effect (SEE) prediction. [PDF]

PLoS One
Karim S, Tusher EH, Rahman A, Rabbi RI, Anwar KO, Othman KR.   +5 more
europepmc   +1 more source

Prediction of Surface Topography Parameters in Direct Laser Interference Patterning of Stainless Steel Using Infrared Monitoring and Convolutional Neural Networks

Advanced Engineering Materials, EarlyView.
This study presents an infrared monitoring approach for direct laser interference patterning (DLIP) combined with a convolutional neural network (CNN). Thermal emission data captured during structuring are used to predict surface topography parameters.
Lukas Olawsky, Marcelo Sallese, Leander Kläber, Clemens Kuhn, Keming Du, Andrés Fabián Lasagni   +5 more
wiley   +1 more source

Ball bearing fault detection using an acoustic based machine learning approach. [PDF]

Sci Rep
Chandrakala CB, Karumanchi SS, Raghudathesh GP, Madhwesh N.   +3 more
europepmc   +1 more source

A Thermodynamic 3D Model for the Simulation of Diffusion‐Controlled Alloying Processes in Heterogeneous Material Structures

Advanced Engineering Materials, EarlyView.
A numerical model resulting from irreversible thermodynamics for describing transport processes is introduced, focusing on thermodynamic activity gradients as the actual driving force for diffusion. Implemented in CUDA C++ and using CalPhaD methods for determining the necessary activity data, the model accurately simulates interdiffusion in aluminum ...
Ulrich Holländer, Leonhard Holländer, André Langohr, Hans Jürgen Maier   +3 more
wiley   +1 more source