Results 171 to 180 of about 8,661,099 (309)

Impact of Hydrogen Incorporation on Electronic and Magnetic Structure of X2CrNi18‐9 Stainless Steel

open access: yesAdvanced Engineering Materials, EarlyView.
Hydrogen uptake in austenitic stainless steel alters both its electronic and magnetic structure. Seebeck measurements reveal hydrogen‐induced shifts in the electronic density of states, while neutron scattering uncovers nanoscale inhomogeneities that grow upon hydrogen incorporation, with the most pronounced effect observed in the PBF as‐built state ...
Torben Tappe   +9 more
wiley   +1 more source

Printability Metrics and Strain Rate Sensitivity of Multirole PVDF in Extrusion-Based Additive Manufacturing. [PDF]

open access: yesPolymers (Basel)
Vidakis N   +8 more
europepmc   +1 more source

Thermal Cycle–Microstructure–Property Relationships in Rail Steel Heat‐Affected Zone Simulated by Gleeble

open access: yesAdvanced Engineering Materials, EarlyView.
Microstructural evolution of premium rail steel during flash butt welding (FBW), comparing physically simulated heat‐affected zones with actual weld regions. Thermal cycles induce phase transformations and microstructural heterogeneity that influence weld quality and mechanical performance. These findings provide insights into optimizing FBW parameters
Emmanoelle Cintra da Cunha Araújo   +15 more
wiley   +1 more source

Strain rate sensitivity of germanium-selenium glasses

open access: yes, 2018
As describe in a previous publication of Su and al. [Su2013], we determine by constant strain rate indentation path the power law’s creep exponent of low T g binary chalcogenide glasses (Se and Ge-Se). We made this choice of composition for the various atomic structures taken by Ge-Se glasses when the composition is modified.
Trenvouez, Gwenole   +4 more
openaire   +2 more sources

Investigation of Resonance Characteristics of More Than 10 000 Metal‐Coated Silicon MEMS Cantilevers Using Machine Learning

open access: yesAdvanced Engineering Materials, EarlyView.
More than 10 000 metal‐coated silicon MEMS cantilevers are fabricated and characterized to build a large‐scale experimental dataset spanning multiple vibrational modes. Machine learning algorithms are benchmarked to predict resonance frequency and quality factor, with ensemble based models achieving accuracy approaching experimental uncertainty.
Sina Zare Pakzad   +8 more
wiley   +1 more source

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