Simulation of Hydrodynamic Pressure Bearing in Forestry Engineering Based On Multi Objective Function Optimization and Artificial Neural Network

Abstract

In order to optimize the performance in the design process, simulation of hydrodynamic
pressure bearing in forestry engineering is researched in this paper. The multi objective function
optimization and artificial neural network is used in this paper to improve the accuracy and
efficiency of simulation. This present study might be of special interests to designers who need
a synchronized improvement of their sliding bearing performance as well as the reduction of the
lubricant flow for their precision machines or high-speed devices and engines. However, the
method described within this paper successfully bridges the gap between the experimental and
the pragmatic and makes the optimization of journal bearings not only highly doable, but cost
efficient as well, combining technological aspects and artificial intelligence tools for function
minimization.