Browsing by Author "Yang, Yifan"

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    Interplay between wear and thermal expansion in 6082 aluminum: a simulation and experimental study
    (MDPI, 2024-11-23) Tian, Yang; Kalifa, Mohamed; Khan, Muhammad; Yang, Yifan
    This study investigates how wear and thermal expansion interact in 6082 aluminum, utilizing a pin-on-disc tribometer and finite element analysis under diverse mechanical conditions. The findings show that thermal expansion reduces contact area by forming a protrusion at the contact interface. This interaction between wear and thermal expansion causes dynamic shifts in the contact region and pressure distribution, affecting the disc center and altering wear progression and temperature patterns. High thermal expansion shifts maximum wear from the contact center to outer regions, especially at higher speeds and loads. Without thermal expansion, wear-only conditions overestimate friction dissipation, resulting in a higher peak temperature. These results highlight the critical role of thermal expansion in shaping wear patterns and contact behavior in sliding applications. This research offers insights for optimizing tribological performance in 6082 aluminum, with potential applications in other materials.
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    Real-time prediction of wear morphology and coefficient of friction using acoustic signals and deep neural networks in a tribological system
    (MDPI, 2025-06-01) Tian, Yang; Zheng, Bohao; Khan, Muhammad; Yang, Yifan
    Predicting real-time wear depth distribution and the coefficient of friction (COF) in tribological systems is challenging due to the dynamic and complex nature of surface interactions, particularly influenced by surface roughness. Traditional methods, relying on post-test measurements or oversimplified assumptions, fail to capture this dynamic behavior, limiting their utility for real-time monitoring. To address this, we developed a deep neural network (DNN) model by integrating experimental tribological testing and finite element method (FEM) simulations, using acoustic signals for non-invasive, real-time analysis. Experiments with brass pins (UNS C38500) of varying surface roughness (240, 800, and 1200 grit) sliding against a 304 stainless steel disc provided data to validate the FEM model and train the DNN. The DNN model predicted wear morphology with accuracy comparable to FEM simulations but at a lower computational cost, and the COF with relative errors below 10% compared to experimental measurements. This approach enables real-time monitoring of wear and friction, offering significant benefits for predictive maintenance and operational efficiency in industrial applications.