Optimizing MRF foam Damper Parameters using Artificial Neural Networks and Genetic Algorithms for Improved Damping Force Performance

A hybrid approach combining Artificial Neural Network (ANN) and Genetic Algorithm (GA) was proposed to optimize Magnetorheological Fluid (MRF) foam damper parameters for the maximum damping force. The hybrid approach improved the accuracy and efficiency of the prediction of damping force values, making it a valuable tool for designing and optimizing MRF foam dampers. The influence of each parameter for an optimized damper is studied and the values obtained using GA and ANN led to an increase in damping force. The study has significant implications for the design and evaluation of MR foam dampers for various applications, including vibration control and energy dissipation in structures.