Results 21 to 30 of about 777 (185)
Transferable Hot‐Deformation Flow‐Curve Modeling Across Steels Through Transfer Learning
We propose a transferable sequence model that moves steel hot‐deformation ML beyond the “one‐case, one‐model” paradigm. Pretrained across compositions/windows and personalized with four extreme‐condition curves, it improves accuracy and robustness in 10‐fold evaluation.
Changqing Shu +9 more
wiley +1 more source
The study investigates the influence of heat treatment and scan rotation on the microstructure of a modified H13 tool steel manufactured using laser powder bed fusion. Key findings include the refinement of prior austenite grains (PAGs) due to recrystallization and carbide pinning, and the significant impact of scan rotation on the size distribution of
Negar Panahi +3 more
wiley +1 more source
Maraging steels are used in several industries, namely in the molds industry. The determination of fatigue crack propagation resistance in 18Ni300 maraging steel at the Paris regime is a vital issue for safety-relevant components, which are designed to ...
Rui F. Fernandes +5 more
doaj +1 more source
This work investigates the wear resistance and nitriding behavior of PBF‐LB/M‐processed Osprey HWTS 50, a lean hot work tool steel. Three heat treatment conditions are compared to conventional wrought H13, H11, and PBF‐LB/M 18Ni300. The study highlights HWTS 50's enhanced nitrogen diffusion, deeper nitriding hardness depth, and comparable or improved ...
Jonathan Hann +6 more
wiley +1 more source
High Nb (2.4 wt.%) addition to Maraging 300 steel drives lattice distortion and nanoscale Nb–Mo‐rich precipitation, confirmed by energy‐dispersive X‐ray spectroscopy mapping (Mo ~5.4 wt.%, Nb ~2.5 wt.%). Nanoindentation reveals strong matrix hardening (H >4.8 GPa) at 480°C aging, while 560°C induces ~1.92 vol.% reverted austenite, enabling tunable ...
Laylla Sharon B. Peixoto +9 more
wiley +1 more source
Micro‐Mechanism Informed Neural Networks for Process‐Property Prediction in Laser Powder Bed Fusion
Hard physics embedding, where neural networks learn residuals relative to analytical baselines, substantially outperforms soft loss‐function constraints for extrapolation in LPBF process–property prediction. Physics integration architecture determines generalization capability more than constraint quantity.
Yo‐Lun Yang
wiley +1 more source
Ductility Tuning via Cluster Network Characteristics of Porous Components
Network optimization via cluster characteristics induced by interaction of stress concentration is proposed, demonstrating increased cluster size and dispersion in non‐uniform porous components. The optimized structures exhibit, for the first time, that enhanced ductility and damage progression is controllable through zigzag cluster network designed by
Ryota Toyoba +4 more
wiley +1 more source
The effect of solid-solution-treated temperature 1050-1150℃ on structure and corrosion resistance in artificial seawater of maraging steel 00Cr13Ni7Co5Mo4W has been studied by analysis on microstructure, using potentiodynamic polarization curves and ...
姜越, 黎士强, 张月, 祖红梅
doaj
This work demonstrates that replacing Ti with Nb in maraging steel forms nanoscale Nb–Mo particles along boundaries, strengthening the alloy without relying on Ti phases. Heat‐treatment tuning promotes uniform crystal orientation and balanced grain boundaries, enabling strong yet ductile behavior.
Mohamad Masoumi +14 more
wiley +1 more source
Electrochemical Corrosion Characteristics of Maraging Steel Welds Depending on Chromium Content
The corrosion behavior of maraging steel weldments which depend on Cr content has been studied, and electrochemical experiments were conducted changing the immersion time in chloride solution.
S.W. Kim, H.W. Lee
doaj +1 more source

