Ensemble machine learning for predicting and enhancing tribological performance of Al5083 alloy with HEA reinforcement

Proceedings of the Institution of Mechanical Engineers Part J Journal of Engineering Tribology, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 10, 2025

This study investigates the tribological behavior of Al5083 alloy reinforced with AlCoCrFeNiSi high-entropy (HEA) particles using friction stir processing (FSP). Wear characteristics were analyzed pin-on-disc experiments across varying HEA volume percentages, disc speeds, and test durations, revealing significant improvements in wear resistance increasing content. Machine learning techniques, including artificial neural networks (ANN) long short-term memory (LSTM) networks, employed to predict specific rate (SWR) coefficient (COF) high accuracy. The ensemble model combining ANN LSTM architectures achieved R-squared values 0.9653 for SWR 0.9718 COF, a root mean square error (RMSE) 0.024 0.017 COF respectively, indicating robust predictive capabilities. Cross-validation further validated model's effectiveness, achieving an average prediction 2.13% 1.89% COF. Response surface methodology (RSM) optimization refined process parameter relationships, identifying conditions that minimize 3.57 × 10 – 6 mm³/Nm 0.237. Scanning electron microscopy (SEM) analysis worn surfaces confirmed effectiveness reinforcement mitigating mechanisms, enhancing material's durability by 45% compared unreinforced alloy. comprehensive approach advances understanding HEA-reinforced composites. It provides practical insights optimizing material performance industrial applications, contributing developing high-performance materials tailored resistance.

Language: Английский

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Dependence of molybdenum content on the cavitation corrosion synergy and wear failure of laser cladded CoCrFeNiMnMo high entropy alloy coatings

Engineering Failure Analysis, Journal Year: 2024, Volume and Issue: 165, P. 108824 - 108824

Published: Aug. 30, 2024

Language: Английский

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Bimodal structure, strength-plasticity synergy and exceptional wear behavior in the spark plasma sintered (CoCrFeNi)84(AlTi)16 medium/high entropy alloy systems

Intermetallics, Journal Year: 2024, Volume and Issue: 177, P. 108600 - 108600

Published: Dec. 18, 2024

Language: Английский

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