2023
DOI: 10.1590/1980-5373-mr-2022-0306
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Applications of Artificial Neural Network Simulation for Prediction of Wear Rate and Coefficient of Friction Titanium Matrix Composites

Abstract: The Artificial Neural Network (ANN) techniques were utilized to predict wear rate and CoF of the Ti-5Al-2.5Sn matrix reinforced with B 4 C particle manufactured by the powder metallurgy. TMCs and wear test samples were characterized by the Scanning Electron Microscope (SEM). Dry sliding wear narrative of the composites was estimated on a pin-on-disc machine at various loads of 20-60N, sliding velocity of 2-6m/s and sliding distance from 1000m-3000m. The wear rate of the composite was reduced by augmentation in… Show more

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Cited by 2 publications
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“…When a greater load is imposed, the contact pressure along the interface of the sliding components is elevated, resulting in an increase in adhesion across the pin and disc. The occurrence of cracks is facilitated by the heightened adhesive force in the direction of sliding 48 50 . Increased friction at the contact during extreme conditions leads to the displacement of particles, resulting in the formation of cavities, as illustrated in Fig.…”
Section: Resultsmentioning
confidence: 99%
“…When a greater load is imposed, the contact pressure along the interface of the sliding components is elevated, resulting in an increase in adhesion across the pin and disc. The occurrence of cracks is facilitated by the heightened adhesive force in the direction of sliding 48 50 . Increased friction at the contact during extreme conditions leads to the displacement of particles, resulting in the formation of cavities, as illustrated in Fig.…”
Section: Resultsmentioning
confidence: 99%