Results 71 to 80 of about 47,657 (234)
Materials Genome Engineering Advances, EarlyView.DeepSeek‐Lattice‐KG integrates a domain‐adapted 14B LLM with a Neo4j lattice knowledge graph distilled from 50,000 papers. It analyzes queries, retrieves supporting subgraphs, and generates grounded answers; on a 2100‐question, six‐domain benchmark, it achieves 94.8% accuracy.
Zhiyang Shu +6 more
wiley +1 more source
Investigation in the microstructure formation of titanium products obtained by additive manufacturing [PDF]
, 2011 The regularities in the microstructure and phase composition of the 3D printed commercially pure titanium and Ti-6Al-4V titanium alloy specimens are studied using optical and transmission electron microscopes as well as X-ray ...
Мартынов, С. А. +1 more
core +2 more sources
Journal of Materials Research and TechnologyPassivated Ti alloys typically demonstrate enhanced corrosion resistance due to the formation of passive films. Intriguingly, in comparison to the annealed Ti–6Al–4V, laser powder bed fusion (LPBF) produced Ti–6Al–4V exhibits significantly attenuated ...
Yi-Fan Zhang +7 more
doaj +1 more source
Criticality of porosity defects on the fatigue performance of wire + arc additive manufactured titanium alloy [PDF]
, 2019 This study was aimed at investigating the effect of internal porosity on the fatigue strength of wire + arc additive manufactured titanium alloy (WAAM Ti-6Al-4V).
Awd, Mustafa +6 more
core +2 more sources
Small Structures, Volume 6, Issue 5, May 2025.Herein, AlCoCrFeNi2.1 is selected to produce lattice structures by selective laser melting. The as‐printed lattice structures consist of numerous colonies of nanosized eutectic lamellar structures which are bent and interwind with each other to form a woven microstructure. The unique microstructure leads to unprecedented effective metal strength, yield
Zhiyong Ji +6 more
wiley +1 more source
Micro‐Mechanism Informed Neural Networks for Process‐Property Prediction in Laser Powder Bed Fusion
Artificial Intelligence for Engineering, EarlyView.Hard physics embedding, where neural networks learn residuals relative to analytical baselines, substantially outperforms soft loss‐function constraints for extrapolation in LPBF process–property prediction. Physics integration architecture determines generalization capability more than constraint quantity.
Yo‐Lun Yang
wiley +1 more source
Functional Ecology, EarlyView.Read the free Plain Language Summary for this article on the Journal blog. Abstract In the context of anthropogenic eutrophication of the biosphere, understanding the impact of nutrient addition on plant diversity–productivity relationships remains a major challenge.
Dai F. Saito +7 more
wiley +1 more source
Corrosion and Tensile Behaviors of Ti-4Al-2V-1Mo-1Fe and Ti-6Al-4V Titanium Alloys
Metals, 2019 X-ray diffraction (XRD), scanning electron microscope (SEM), immersion, electrochemical, and tensile tests were employed to analyze the phase constitution, microstructure, corrosion behaviors, and tensile properties of a Ti-6Al-4V alloy and a newly ...
Yanxin Qiao +6 more
doaj +1 more source
Selected fretting-wear-resistant coatings for titanium - 6-percent-aluminum - 4-percent-vanadium alloy [PDF]
A titanium - 6-percent-aluminum - 4-percent-vanadium alloy (Ti-6Al-4V) was subjected to fretting-wear exposures against uncoated Ti-6Al-4V as a baseline and against various coatings and surface treatments applied to Ti-6Al-4V.
Bill, R. C.
core +1 more source
Challenges in Additive Manufacturing of space parts: Powder feedstock cross-contamination and its impact on end products [PDF]
, 2017 This work studies the tensile properties of Ti-6Al-4V samples produced by laser powder bed based Additive Manufacturing (AM), for different build orientations.
Beretta, Stefano +6 more
core +1 more source