Predicting damping performance of MRF foam using artificial neural networks and damper parameters.

This article introduces the utilization of Artificial Neural Network (ANN), an algorithm within the domain of machine learning, to establish a forecasting framework for the damping efficacy of Magnetorheological Fluid (MRF) foam dampers. MRF foam stands as an intelligent material that allows for the modulation of its rheological characteristics through the influence of an external magnetic field. Predicting the damping performance is crucial for the development of effective MRF foam dampers for real-time applications. The proposed ANN-based model considers the damping force as the response of interest, which is influenced by the damper parameters such as foam density, viscosity of oil, and size of iron particles. Anticipated as a dependable instrument, the MATLAB simulation is poised to serve as a trustworthy resource for forecasting and approximating the damping force exerted by MRF foam dampers across a spectrum of pragmatic damper parameter configurations. [ABSTRACT FROM AUTHOR]