Results 161 to 170 of about 5,124 (208)

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

Electrical properties and surface roughness of carbon fibre reinforced polymer under progressive surface abrasion. [PDF]

Data Brief
Osama M, Jones CE, Stephen B.
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

Fault diagnosis of permanent magnet synchronous motor based on MTF fusion image and NRBO-SCN method. [PDF]

Sci Rep
Yu Y, Ge H, Carbone G, Zeng D, Hu Y, Yang J.   +5 more
europepmc   +1 more source

A Numerical–Experimental Approach for Multi‐Matrix Fiber‐Reinforced Plastics Characterization Using Finite Element Model Updating

Advanced Engineering Materials, EarlyView.
A numerical–experimental framework is developed for characterizing multi‐matrix fiber‐reinforced polymers (MM‐FRPs) combining epoxy and polyurethane matrices. Harmonic bending tests are integrated with finite element model updating (FEMU) to simultaneously identify elastic and viscoelastic material parameters.
Rodrigo M. Dartora, Tales V. Lisbôa, Axel Spickenheuer, Rogério J. Marczak, Jakson M. Vassoler   +4 more
wiley   +1 more source

ETNeXt: integrated feature engineering and classification framework for BLDC motor fault detection. [PDF]

Sci Rep
Celik B, Taskin E, Akbal A, Ozdemir M.
europepmc   +1 more source

Field Report from Collaborative Research Center 1625: Heterogeneous Research Data Management Using Ontology Representations

Advanced Engineering Materials, EarlyView.
A unified research data management framework for heterogeneous materials data is presented. The system integrates multimodal datasets using ontologies and knowledge graphs, enabling interoperability and FAIR (findable, accessible, interoperable, reusable) data principles. By linking data across scales and workflows, it supports reproducible, Artifitial
Doaa Mohamed, Samuel García Vázquez, Behnam Mardani, Victor Dudarev, Alfred Ludwig, Maribel Acosta, Markus Stricker   +6 more
wiley   +1 more source

Fault-Tolerant Control of AGVs via Deep Feature Enhancement and Multi-Source Verification in Complex Industrial Environments. [PDF]

Sensors (Basel)
Zhou Y, Peng S, Wang Y, Zhou N, Shan F.
europepmc   +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