Enhancing Tribocorrosion Resistance of VCoNi Alloys in Artificial Seawater via Nitrogen Alloying

Corrosion Science, Journal Year: 2024, Volume and Issue: unknown, P. 112600 - 112600

Published: Nov. 1, 2024

Language: Английский

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Strengthening mechanisms of laser cladding TiC/FeCoCrNiCu high-entropy composite coatings: Microstructure evolution and wear behaviors

Tribology International, Journal Year: 2024, Volume and Issue: 199, P. 109979 - 109979

Published: July 9, 2024

Language: Английский

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Coupling different strengthening mechanisms with transformation-induced plasticity (TRIP) effect in advanced high-entropy alloys: a comprehensive review

Materials Science and Engineering A, Journal Year: 2025, Volume and Issue: unknown, P. 147914 - 147914

Published: Jan. 1, 2025

Language: Английский

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A Comprehensive Study on Additive Manufacturing Techniques, Machine Learning Integration, and Internet of Things-Driven Sustainability Opportunities

Journal of Materials Engineering and Performance, Journal Year: 2025, Volume and Issue: unknown

Published: March 5, 2025

Language: Английский

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Corrosion behavior of magnetron sputtering Fe31Cr20Al17Ti16Nb16 high entropy coating in liquid lead-bismuth eutectic with different oxygen concentration at 500 °C

Surface and Coatings Technology, Journal Year: 2024, Volume and Issue: 480, P. 130579 - 130579

Published: Feb. 28, 2024

Language: Английский

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Multi-phase FCC-based AlxCrFeNi2Si(1-x) medium entropy alloy coatings design for tailoring toughness and wear resistance of CSS-42L bearing steel

Tribology International, Journal Year: 2025, Volume and Issue: unknown, P. 110548 - 110548

Published: Jan. 1, 2025

Language: Английский

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Microstructure and Wear and Corrosion Resistance of CoCrFeMoNiSix (x = 0.25, 0.50, 0.75) HEACs Prepared by Plasma Cladding

Crystals, Journal Year: 2025, Volume and Issue: 15(2), P. 123 - 123

Published: Jan. 24, 2025

CoCrFeMoNiSix (x = 0.25, 0.50, 0.75) HEACs were successfully prepared on Q235 steel substrates by the plasma cladding method. The phase structure, microstructure, element distribution, and wear corrosion resistance of these coatings investigated XRD, OM, SEM, EDS, a friction tester, an electrochemical workstation. results show that are composed major FCC minor BCC phase. With increase in Si content, lattice constant cell volume both phases content alloys gradually increase, while enthalpy mixing, Gibbs free energy, atomic radius difference, VEC, density decrease. All three exhibit typical dendritic structures. enrichment Mo interdendrite region is significantly reduced. coefficients trend first increasing, then decreasing, stabilizing with time, 0.604, 0.526, 0.534, respectively. mainly related to changes crystallinity high-strength caused different contents. polarization curves high-entropy alloy obvious passivation zone, better than substrate. CoCrFeMoNiSi0.75 coating has highest self-corrosion potential, smallest current, largest capacitive reactance arc radius, best 3.5% NaCl solution.

Language: Английский

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Effect of Nb0.5 and Mo0.75 addition on in-vitro corrosion and wear resistance of high-speed laser metal deposited Al0.3CrFeCoNi high-entropy alloy coatings

Applied Surface Science Advances, Journal Year: 2025, Volume and Issue: 26, P. 100710 - 100710

Published: Feb. 6, 2025

Language: Английский

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Enhancing corrosion resistance and radiation shielding of AISI 304 SS with Nb and Mo-added Al0.3CrFeCoNi-based high-entropy alloy coatings in 3.5 wt% NaCl: The effect of environmental temperature

Journal of Alloys and Compounds, Journal Year: 2025, Volume and Issue: unknown, P. 179432 - 179432

Published: Feb. 1, 2025

Language: Английский

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Predictive analytics of wear performance in high entropy alloy coatings through machine learning

Physica Scripta, Journal Year: 2024, Volume and Issue: 99(7), P. 076014 - 076014

Published: June 10, 2024

Abstract High-entropy alloys (HEAs) are increasingly renowned for their distinct microstructural compositions and exceptional properties. These HEAs employed surface modification as coatings exhibit phenomenal mechanical characteristics including wear corrosion resistance which extensively utilized in various industrial applications. However, assessing the behaviour of HEA through conventional methods remains challenging time-consuming due to complexity structures. In this study, a novel methodology has been proposed predicting using Machine Learning (ML) algorithms such Support Vector (SVM), Linear Regression (LR), Gaussian Process (GPR), Least Absolute Shrinkage Selection Operator (LASSO), Bagging (BR), Gradient Boosting Tree (GBRT), Robust regressions (RR). The analysis integrates 75 combinations with processing parameters test results from peer-reviewed journals model training validation. Among ML models utilized, GBRT was found be more effective rate Coefficient Friction (COF) highest correlation coefficient R 2 value 0.95 ∼ 0.97 minimal errors. optimum is used predict unknown properties conducted experiments validate results, making crucial resource engineers materials sector.

Language: Английский

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