Results 131 to 140 of about 1,033 (275)

In Situ Micromechanical Study of Bimodal γ′–γ″ Precipitate Assemblies in Ni–Cr–Al–Nb Superalloy

open access: yesAdvanced Engineering Materials, EarlyView.
A Ni–Cr–Al–Nb superalloy with a bimodal γ′–γ″ precipitate distribution is developed. Composite precipitate assemblies form through heterogeneous nucleation, effectively impeding dislocation motion. Micropillar compression reveals high strength at room and elevated temperatures, governed by precipitate shearing, with coupled faulting mechanisms ...
Ujjval Bansal   +4 more
wiley   +1 more source

An Experimental High‐Throughput Approach for the Screening of Hard Magnet Materials

open access: yesAdvanced Engineering Materials, EarlyView.
An entire workflow for the high‐throughput characterization and analysis of compositionally graded magnetic films is presented. Characterization protocols, data management tools and data analysis approaches are illustrated with test case Sm(Fe, V)12 based films.
William Rigaut   +16 more
wiley   +1 more source

Besøgstjenesten - i et styringsperspektiv

open access: yes, 2012
This project identifies the governmental rationalities and governmental technologies, which through history have been dominating in relation to voluntary social work.
Roelsgaard, Krista   +2 more
core  

Innovation in traditional sport: applying the delphi method to strategic designs in basque hand-ball. [PDF]

open access: yesFront Sports Act Living
Sanchez Mencia E   +2 more
europepmc   +1 more source

Experimental Characterization and Constitutive Modeling of the Effect of Prior Inelastic Strain on the Viscoplastic Behavior of AD730 at 700 °C

open access: yesAdvanced Engineering Materials, EarlyView.
This study shows that superalloys used in aircraft engine disks become much more prone to deformation at high temperatures if they have been strained during manufacturing. This effect increases with the level of prior strain but eventually reaches a limit.
Fabio Machado Alves da Fonseca   +9 more
wiley   +1 more source

Machine Learning‐Supported Analysis for Predicting and Visualizing Nonlinear Relationships Between Material Properties in Electroplated Chromium Layers

open access: yesAdvanced 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   +4 more
wiley   +1 more source

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