In this research, we study four influence factors of the damping performance of ferrofluid dynamic vibration
absorber, as well as predict and optimize the damping performance by machine learning method. The vibration
absorber in our research is based on the second order buoyancy principle, which consists of a non-magnetic
container, a small amount of ferrofluid and a permanent magnet. The effects of the initial amplitude, the
cone angle of the cover, the thickness of the gasket and the mass of the ferrofluid on the damping performance
are investigated by experiments. Based on the experiment data, we use BP neural network to establish a prediction
model between the four influence factors and the damping performance. The prediction error of damping
efficiency predicted by BP neural network is mainly within ± 0.4%. Meanwhile, the determination
coefficient R² of test data is 0.96242. The both indicate that BP neural network has a good performance in predicting
the damping efficiency. Furthermore, we use the search algorithm to find the optimized values of each
influence factor through the prediction model and the high damping efficiency is confirmed by experiments.
Our work introduces machine learning into the field of vibration absorber designing, which provides an innovative
method for the rapid design of high efficiency vibration absorber.