Multi-Objective Optimization of Needle Roller Bearings Based on Fatigue and Wear Using Evolutionary Algorithm


The important considerations for a long-lasting service of needle roller bearings (NRBs) are the long fatigue and wear lives. Keeping in mind these life factors, a design methodology has been proposed to optimize the dynamic capacity (C-d) and the elasto-hydrodynamic minimum film thickness (h(min)). These are taken for the optimization since the fatigue life is related to C-d and the wear life to h(min). In the optimization problem, a total of seven design variables (which comprises of bearing pitch diameter, roller mean diameter, effective roller length, number of rollers and three other unknown constraint factors), two objectives and 16 constraints are considered. These two objectives are optimized individually (single objective optimization) and simultaneously (multi-objective optimization). The non-linear constrained optimization problem of NRBs has been solved using a multi-objective evolutionary algorithm, called as the Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II). Optimization has been carried out on the set of standard catalogue NRBs. Data obtained from NSGA-II runs of the multi-objective optimization is used to draw the Pareto-optimal fronts (POF). Optimum bearing dimensions are selected by considering the knee point solution on the POF for the comparison with standard bearings. Results showed that optimized bearings got improved lives than standard bearings. Life comparison factors have been calculated and compared with available literatures and are found to be better. Finally, for visualization of optimized NRBs, radial dimensions of three bearings designed by optimizing C-d, h(min) and C-d - h(min) are shown, for one of the standard NRB. The sensitivity analysis is performed to see the sensitivity of two objectives with design variables.