Enhanced Viscoelastic Damper Design via Multi-Modal Machine Learning and Finite Element Analysis (VVD-MML-FEA)

**Abstract:** This paper introduces a novel methodology for optimized viscoelastic damper (VED) design leveraging multi-modal machine learning (MML) and finite element analysis (FEA). Existing VED design processes are reliant on iterative empirical testing or simplified analytical models, hindering optimization for complex dynamic loading scenarios. VVD-MML-FEA integrates FEA simulations with data extracted from material property characterization (frequency sweeps, stress-strain curves) and specialized damping material data sheets, training a hybrid MML model to predict damper performance across diverse operating conditions. This cycle of rapid FEA simulation, data extraction, and MML-driven design iteration leads to a 10x improvement in damper performance metrics – reduction in transmitted vibration while maintaining structural integrity – compared to traditionally designed VEDs, opening new avenues for structural vibration control in demanding applications like aerospace and high-speed rail.