Vibration suppression of printed circuit boards using an external particle damper

Particle damping is an effective method of passive vibration control, of recent research interest. The novel use of particle damper capsule on a Printed Circuit Board (PCB) and the development of Radial Basis Function neural network to accurately predict the acceleration response is presented here. The prediction of particle damping using this neural network is studied in comparison with the Back Propagation Neural network. Extensive experiments are carried out on a PCB for different combinations of particle damper parameters such as particle size, particle density, packing ratio, and the input force during the primary modes of vibration and the obtained results are used for training and testing of neural networks. Based on the prediction from the better trained network, useful design guidelines for the particle damper suitable for PCB are arrived at. The effectiveness of particle dampers for vibration suppression of PCB under random vibration environment is demonstrated based on these design guidelines.