Results 191 to 200 of about 2,956,075 (276)

Revisiting Stability Criteria in Ball‐Milled High‐Entropy Alloys: Do Hume–Rothery and Thermodynamic Rules Equally Apply?

open access: yesAdvanced Engineering Materials, Volume 27, Issue 6, March 2025.
The stability criteria affecting the formation of high‐entropy alloys, particularly focusing in supersaturated solid solutions produced by mechanical alloying, are analyzed. Criteria based on Hume–Rothery rules are distinguished from those derived from thermodynamic relations. The formers are generally applicable to mechanically alloyed samples.
Javier S. Blázquez   +5 more
wiley   +1 more source

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

open access: yesAdvanced Engineering Materials, EarlyView.
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt   +8 more
wiley   +1 more source

Autonomous sampling and SHAP interpretation of deposition-rates in bipolar HiPIMS.

open access: yesDigit Discov
Wieczorek A   +4 more
europepmc   +1 more source

Innovative Processing of Compacted Waste Aluminum Alloy Powders via Controlled Remelting and Solidification

open access: yesAdvanced Engineering Materials, EarlyView.
This study demonstrates an efficient recycling route for out‐of‐spec AlSi10Mg atomized powders through compaction and arc remelting followed by suction casting. By correlating compaction load, cooling rate, and resulting microstructure, we show that intermediate pressures (50–80 kN) and rapid cooling refine dendrites, reduce porosity, and enhance ...
Mila Christy de Oliveira   +4 more
wiley   +1 more source

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

open access: yesAdvanced 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   +5 more
wiley   +1 more source

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