Results 91 to 100 of about 498,787 (263)

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

Influence of Si Content and Milling Duration on the Microstructure and Mechanical–Tribological Properties of AlCoCrFeNiSi High‐Entropy Alloys

open access: yesAdvanced Engineering Materials, EarlyView.
Si‐doped AlCoCrFeNi high‐entropy alloys are synthesized by mechanical alloying to reveal the effect of Si content and milling time on phase evolution, microstructural refinement, and tribological behavior. A transition from FCC to BCC structure, significant grain refinement, and enhanced hardness and wear resistance are achieved, with the 4 at% Si ...
Mustafa Okumuş   +2 more
wiley   +1 more source

Defect Evolution and Mechanical Performance of Fused Filament Fabrication‐Manufactured 17‐4PH Stainless Steel Revealed by X‐Ray Computed Tomography

open access: yesAdvanced Engineering Materials, EarlyView.
X‐ray computed tomography reveals how process‐induced defects evolve from green to sintered states in Fused Filament Fabrication (FFF)‐manufactured 17‐4PH stainless steel. Internal porosity, weakest cross‐sections, and fracture locations show strong correlation with tensile performance, demonstrating the potential of computed tomography (CT)‐based ...
György Ledniczky   +3 more
wiley   +1 more source

Active Corrosion Protection of Sintered AA7075 Aluminum Alloy via Mn Powder Addition

open access: yesAdvanced Engineering Materials, EarlyView.
AA7075 containing Mn‐rich particles is fabricated via spark plasma sintering using AA7075 and Mn powders. Corrosion resistance is evaluated through dip‐and‐dry tests using 0.1 M NaCl (pH 6.0), and mass loss decreases with increasing Mn addition. Mn‐rich particles function as a source of Mn ions, and formation of Mn‐accumulation films on Cu‐containing ...
Ko Ebina, Masashi Nishimoto, Izumi Muto
wiley   +1 more source

External and Internal Cracking Caused by Heat Treatment in Laser Powder Bed Fusion‐Fabricated Inconel 738LC Components

open access: yesAdvanced Engineering Materials, EarlyView.
Manufacturing problems such as heat treatment‐induced cracking hinder the widespread application of the laser powder bed fusion (LPBF) process to superalloys. In this study, cracks in the LPBF components of Inconel 738LC superalloy are characterized after heat treatment at various temperature ranges, revealing two distinct cracking behaviors.
Kosuke Kuwabara   +4 more
wiley   +1 more source

Additive Manufacturing of Alumina‐Reinforced Elastomers

open access: yesAdvanced Engineering Materials, EarlyView.
Vat‐photopolymerized elastomers reinforced with platelet‐shaped alumina exhibited preferential orientation, reduced porosity, and significantly enhanced mechanical performance. A 3 wt% platelet loading increased tensile strength from 12.4 to 45.7 MPa, highlighting the critical role of filler morphology in elastomeric VPP composites.
Majid Barzegar Keyvani   +6 more
wiley   +1 more source

Extrusion‐Based Additive Manufacturing of Advanced Ceramics: Strengthening Mechanisms and Process Optimization Review

open access: yesAdvanced Engineering Materials, EarlyView.
This review comprehensively evaluates extrusion‐based additive manufacturing for advanced ceramics, detailing feedstock options and key process parameters. By critically addressing defect mechanisms like porosity and cracking, the work highlights optimization strategies through machine learning and advanced postprocessing.
Meisam Bakhtiari   +4 more
wiley   +1 more source

Foundational Machine‐Learning Interatomic Potential for Simulating Chemically Complex Ni‐Based Superalloys

open access: yesAdvanced Engineering Materials, EarlyView.
We apply a foundational machine‐learning interatomic potential based on the graph atomic cluster expansion (GRACE) to simulate the commercial Ni‐based single‐crystal superalloy CMSX‐4. Hybrid Monte‐Carlo/molecular dynamics sampling resolves short‐range order in the γ phase and L12 sublattice occupancies in the γ’ phase and connects them to stacking ...
Aditya Vishwakarma   +4 more
wiley   +1 more source

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