Results 191 to 200 of about 604 (261)

Materials‐Driven Additive Manufacturing for Electrical Machines: Qualification‐Oriented Design Beyond Geometric Freedom

open access: yesAdvanced Engineering Materials, EarlyView.
This perspective reframes additive manufacturing for electrical machines as a qualification‐limited materials and architecture design problem. It links process–structure–property–performance relationships to magnetic, conducting, dielectric, and thermal property windows, highlighting where AM can enable segmented magnetic circuits, permanent magnet ...
Dénes Fodor, Loránd Szabó
wiley   +1 more source

Supporting AI Readiness Through Digital Workflows in Materials Science

open access: yesAdvanced Engineering Materials, EarlyView.
Digitalization drives innovation in materials science by connecting data silos and turning heterogeneous processes into reusable research pipelines. Across 13 MaterialDigital projects, digital workflows reveal complementary pathways toward AI‐ready materials research, founded on structured data, persistent artifacts, executable orchestration, and ...
Marian Bruns   +67 more
wiley   +1 more source

Cryogenic Deformation Behavior of TiAl and Insights Into Embrittlement After High‐Temperature Exposure Combined With Secondary Ion Mass Spectroscopy of Near Surface Chemistry

open access: yesAdvanced Engineering Materials, EarlyView.
Near‐surface deuterium enrichment profiles for different microstructural types of TiAl after exposure at 700 °C in a heavy water‐containing environment. The deuterium levels are significantly higher than expected from natural occurrence, indicating that the heavy water dissociated during the exposure treatment and entered the specimens.
Jonathan D. H. Paul   +5 more
wiley   +1 more source

Enhancement of the Through‐Thickness Electrical Conductivity of Carbon‐Fiber‐Reinforced Plastics Using Large Graphite Particles

open access: yesAdvanced Engineering Materials, EarlyView.
To enhance through‐thickness conductivity without sacrificing impregnation, large spherical graphite particles are intentionally employed in a low‐viscosity resin. Unlike finer conductive fillers, these particles remain outside the fiber bundles and accumulate in resin‐rich interlaminar regions during molding.
Keito Hosoe   +6 more
wiley   +1 more source

3D‐Printed Titanium Gyroid Scaffold Structure Integrated With Tough Hybrid Materials for Cartilage Replacement

open access: yesAdvanced Engineering Materials, EarlyView.
This study proposes a potential device design for joint cartilage replacement. Silica‐polytetrahydrofuran (SiO2‐PolyTHF) hybrids with customizable mechanical properties were developed to mimic the characteristics of a natural meniscus. These were synthesized through a two‐pot sol–gel hybrid process.
Yu‐Chien Lin   +12 more
wiley   +1 more source

Impact of the Process Atmosphere During Hot Isostatic Pressing on the Near‐Surface Composition of an Additively Manufactured Austenitic High‐Interstitial Steel

open access: yesAdvanced Engineering Materials, EarlyView.
A high interstital austenitic steel is additively manufactured and postdensified by HIP to eliminate gas porosity. Thereby, the loss of N and Mn in Ar HIP atmosphere is investigated. Further, HIP using N2 atmosphere is investigated which led to excessive nitride formation.
F. Großwendt   +4 more
wiley   +1 more source

A Multi‐Scale Machine Learning Framework for the Inverse Design of High Entropy Alloys

open access: yesAdvanced Engineering Materials, EarlyView.
High‐entropy alloys offer vast potential for various applications, including electrocatalysis; however, their compositional complexity challenges conventional screening. We introduce an inverse‐design framework combining two neural networks to determine optimal compositions and reconstruct nanoparticle geometry from targeted properties and conventional
Mikael Takoutsin   +14 more
wiley   +1 more source

Detecting Anomalous Cell Behavior in Electrochemical Battery Testing Using Machine Learning

open access: yesAdvanced Engineering Materials, EarlyView.
Machine‐learning‐based screening enables automated identification of anomalous battery cells from complementary electrochemical tests. A curated battery database supports configuration‐aware comparison of rate‐capability and impedance data. Supervised classification of rate‐test data achieves 90% accuracy, while CNN‐VAE‐based impedance analysis reaches
Minu Rose   +7 more
wiley   +1 more source

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