The primary objective of this research is to optimize the dynamic load capacity of a deep groove ball bearing. The dynamic load capacity is formulated as an objective function along with the prescribed geometric, kinematics and strength constraints. The non-linear constrained optimization problem is solved using particles swarm optimization (PSO). The algorithm incorporates the generalized method to handle mixed integer design variables and ranked based method of constraint handling. Encouraging results in terms of objective function value and CPU time are reported in this study. The optimum design result shows that the system life of an optimally designed roller element bearing is enhanced in comparisons with that of the current design without constraint violations. It is believed that the proposed algorithm can be applied to other roller element design applications.
CITATION STYLE
Panda, S., Mohanty, T., Mishra, D., & Biswal, B. B. (2015). Optimum design of rolling element bearing. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8947, pp. 156–163). Springer Verlag. https://doi.org/10.1007/978-3-319-20294-5_14
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