Superior Mechanical Properties of Aluminium Matrix Composites Fabricated through Modified Matrix Encapsulated Feeding Method

Prakash, S.; Suresh, P.; Sasikumar, R.; Suresha, B.

doi:10.1007/s12666-023-03144-4

Trans Indian Inst Met

2023

DOI: 10.1007/s12666-023-03144-4

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Superior Mechanical Properties of Aluminium Matrix Composites Fabricated through Modified Matrix Encapsulated Feeding Method

S. Prakash,

P. Suresh,

R. Sasikumar

et al.

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2024

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Cited by 3 publications

References 19 publications

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Recent Progress in Aluminium Matrix Composites: A Review on Tribological Performance

Aydın

2024

Trans Indian Inst Met

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Recent Progress in Aluminium Matrix Composites: A Review on Tribological Performance

Aydın

2024

Trans Indian Inst Met

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Triboinformatics Approach for Prediction of High-Stress Abrasive Wear and Coefficient of Friction in Al/TiC Nanocomposites Using Machine Learning Techniques

Golla,

Narasimha Rao,

Ismail

2024

Journal of Tribology

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This study highlights the importance of Al–Fe–Si alloys in modern engineering for their enhanced hardness, strength, and wear resistance, improving fuel efficiency in the aerospace and automotive sectors. Data-driven analysis and machine learning methods can help understand tribological occurrences by identifying links between material characteristics and tribological behavior. The research examined TiC reinforcement in aluminum nanocomposites synthesized via ultrasonic-assisted stir casting, creating five composites with TiC weight percentages from 0% to 8%. Tests conducted using pin-on-disc equipment under various conditions, including loads of 5–15 N, sliding velocities of 0.5–1.5 m/s, sliding distances of 80–120 m, and abrasive grit sizes of 80–150 µm, revealed significant findings. The Al–6TiC nanocomposite demonstrated an 18% reduction in wear-rate at 80 µm, 28.2% at 120 µm, and 24.5% at 150 µm under a 15 N load and 120 m sliding distance compared to the pure alloy. There was also a 22% friction coefficient reduction with increased loads and grit sizes. Scanning electron microscope (SEM) analysis of the worn surfaces and abrasive papers was conducted. Wear-rate data were analyzed using six machine learning models, with the gradient boosting regressor (GBR) identified as the most accurate, achieving an R2 value of 0.95. This study emphasizes the impact of the TiC content, loading conditions, and hardness on wear and friction coefficient, and shows how machine learning techniques can predict and optimize advanced aluminum nanocomposite design for engineering applications.

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A Comprehensive Evaluation of Electrochemical Performance of Aluminum Hybrid Nanocomposites Reinforced with Alumina (Al2O3) and Graphene Oxide (GO)

Khan,

Mohammed,

Toor

2024

Metals

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The electrochemical performance of in-house developed, spark plasma-sintered, Aluminum metal–matrix composites (MMCs) was evaluated using different electrochemical techniques. X-ray diffraction (XRD) and Raman spectra were used to characterize the nanocomposites along with FE-SEM and EDS for morphological, structural, and elemental analysis, respectively. The highest charge transfer resistance (Rct), lowest corrosion current density, lowest electrochemical potential noise (EPN), and electrochemical current noise (ECN) were observed for GO-reinforced Al-MMC. The addition of honeycomb-like structure in the Al matrix assisted in blocking the diffusion of Cl− or SO4−2. However, poor wettability in between Al matrix and Al2O3 reinforcement resulted in the formation of porous interface regions, leading to a degradation in the corrosion resistance of the composite. Post-corrosion surface analysis by optical profilometer indicated that, unlike its counterparts, the lowest surface roughness (Ra) was provided by GO-reinforced MMC.

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Superior Mechanical Properties of Aluminium Matrix Composites Fabricated through Modified Matrix Encapsulated Feeding Method

Cited by 3 publications

References 19 publications

Recent Progress in Aluminium Matrix Composites: A Review on Tribological Performance

Recent Progress in Aluminium Matrix Composites: A Review on Tribological Performance

Triboinformatics Approach for Prediction of High-Stress Abrasive Wear and Coefficient of Friction in Al/TiC Nanocomposites Using Machine Learning Techniques

A Comprehensive Evaluation of Electrochemical Performance of Aluminum Hybrid Nanocomposites Reinforced with Alumina (Al2O3) and Graphene Oxide (GO)

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