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S. N

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Open access Aug 2026

Multi-objective response surface optimization with mechanical, microstructural, and thermal evaluation of areca fiber–SiC reinforced hybrid polymer composites for automotive components

This study investigates the development and comprehensive characterization of areca fibre–silicon carbide (SiC) reinforced hybrid epoxy composites for lightweight automotive applications. Hybrid composites containing 10–25 wt% areca fibre and 5–15 wt% SiC were fabricated and evaluated through mechanical, tribological, thermal, and microstructural analyses. The tensile strength increased from 32.4 MPa for AF1 to 45.2 MPa for AF3, while Young’s modulus improved from 1.8 to 2.8 GPa, demonstrating enhanced load transfer and improved fibre–matrix interfacial bonding. The maximum flexural strength and hardness of 64.8 MPa and 74 Shore D, respectively, were obtained for the AF3 composite. Tribological testing revealed a reduction in wear rate from 4.8 × 10⁻⁴ to 2.5 × 10⁻⁴ mm³/N·m, accompanied by a decrease in the coefficient of friction from 0.62 to 0.47, indicating superior wear resistance. Thermal characterization showed that AF3 exhibited improved thermal stability with an initial degradation temperature of 301 °C and a residual mass of 55%. Differential scanning calorimetry demonstrated an increase in glass transition temperature from 72 °C to 85 °C, suggesting restricted polymer chain mobility due to effective reinforcement. Scanning electron microscopy revealed uniform reinforcement distribution and strong interfacial adhesion for AF3, whereas higher reinforcement levels resulted in particle agglomeration and localized defects. Response Surface Methodology (RSM) was employed to examine the influence of reinforcement composition on the measured responses, confirming that the AF3 composite (20 wt% areca fibre and 15 wt% SiC) provided the most balanced combination of mechanical strength, wear resistance, and thermal stability. These findings demonstrate the potential of areca fibre–SiC hybrid epoxy composites as sustainable materials for lightweight automotive applications.

Praveena B. A., S. N, Kiran Kumar K. U et al. · Journal of Materials Science... · 1 citation

Conference Jul 2026

DT-MR-FALCON: Digital Twin–Assisted Mixed Reality Emergency Corridor Optimization for Intelligent Ambulance Navigation

Urban traffic congestion critically impairs emergency medical services (EMS) response times, often preventing ambulances from reaching patients within the life-saving “golden hour.” Existing traffic management systems are predominantly reactive and infrastructure-focused, lacking integrated support for real-time emergency vehicle navigation. Although reinforcement learning-based signal control and Digital Twin modeling have each demonstrated promise independently, their separate deployment fails to deliver coordinated, predictive, and driveraware emergency routing. This paper presents DT-MR-FALCON, a unified framework for Emergency Corridor Optimization (ECO) that simultaneously addresses predictive traffic modeling, distributed signal coordination, and driver-centric navigation. ECO is formally defined as a dynamic, congestion-sensitive path optimization problem on urban road networks. The proposed solution integrates: (i) a Digital Twin for short-horizon traffic state forecasting, (ii) a Federated Multi-Agent Reinforcement Learning (FMARL) framework for scalable, privacy-preserving signal coordination, and (iii) a Mixed Reality (MR) interface for real-time visualization of dynamically generated emergency corridors. The framework establishes a closed-loop system coupling prediction, optimization, and human-centered decision-making, supported by theoretical guarantees on corridor optimality and delay reduction under bounded prediction error. Large-scale SUMO simulations on real-world urban networks demonstrate that DT-MR-FALCON reduces average intersection delay by 35.0%, queue length by 37.5%, and ambulance travel time by 46.2% relative to fixed-time control, achieving a 95% corridor-clearance success rate.

P. Sathish, S. N · 2026 International Conferenc... · 0 citations