Results 181 to 190 of about 57,151 (280)

Microstructure Evolution in Directionally Solidified Fe–C–Mn–(Si) Peritectic Steels

open access: yessteel research international, EarlyView.
This study investigates how growth velocity and silicon additions affect dendritic morphology, primary arm spacing, and mushy‐zone evolution in directionally solidified quaternary peritectic steels. Silicon substantially broadens the freezing range and lengthens the mushy zone, increasing susceptibility to interdendritic cracking.
André Phillion   +4 more
wiley   +1 more source

Novel Spinel Li-Cr Nano-Ferrites: Structure, Morphology, and Electrical/Dielectric Properties. [PDF]

open access: yesInt J Mol Sci
Mataev M   +8 more
europepmc   +1 more source

Effect of Postweld Heat Treatment on Laser‐Welded Duplex Stainless Steel: Crystallographic Patterns and Fatigue Behavior

open access: yessteel research international, EarlyView.
Laser welding of duplex stainless steel disrupts phase balance, promotes nitride precipitation, and reduces fatigue resistance. Postweld heat treatment restores austenite and dissolves nitrides, but the resulting crystallography and phase morphology still impair fatigue performance.
Aparecida Silva Magalhães   +6 more
wiley   +1 more source

Low temperature synthesis and investigations of magnetic properties of cobalt ferrite nanoparticles

open access: diamond, 2020
L B Jadhavar   +5 more
openalex   +1 more source

Correlated Transient Response of Buried Cables and Lightning Channel Radiation of Return Strokes of Five Rocket‐Triggered Lightning Strikes

open access: yesHigh Voltage, EarlyView.
ABSTRACT Data from five rocket‐triggered lightning flashes are adopted to analyse the correlated transient response of buried cables and lightning channel radiations. These five flashes have the same termination point that is right above the buried cables and involve a total of 32 return strokes.
Mi Zhou   +9 more
wiley   +1 more source

Deep learning model for enhanced power loss prediction in the frequency domain for magnetic materials

open access: yesIET Power Electronics, EarlyView.
This paper outlines the methodology for predicting power loss in magnetic materials. A neural network based method is introduced, which adopts a long short‐term memory network, expressing the core loss as a function of magnetic flux density in the frequency domain, temperature, frequency, and classification of the waveforms.
Dixant Bikal Sapkota   +3 more
wiley   +1 more source

Home - About - Disclaimer - Privacy