Design predictive tool and optimization of journal bearing using neural network model and multi-objective 
genetic algorithm

Abstract

In this paper, rapid and globally convergent predictive tool for dynamically loaded journal bearing design 
is developed. For accomplishment of such an aim, a neural network model of crankshaft and connecting rod 
bearings in an internal combustion engine is developed as an alternative for the complicated and time-consuming 
models. Six most important parameters are selected as inputs of neural network. These parameters are: oil 
viscosity, engine speed, bearing radial clearance, bearing diameter, slenderness ratio and maximum force 
applied on bearings. Also, some significant parameters are calculated as neural network outputs. These 
parameters include: all components of friction loss, all components of oil consumption, minimum oil film 
thickness, eccentricity, oil temperature rise and displacement relative to shell. In addition, an optimum 
analysis is performed. To achieve such a target, multi-objective optimization methodology is a good approach 
inasmuch as several types of objective are minimized or maximized simultaneously. The optimization goal is to 
minimize friction loss and lubricant flow as the two objectives and develop a Pareto optimal front.