Abstract
One of most critical performance parameters of deep groove ball bearing is the achievable highest fatigue life. The life of the bearing may be affected by more than one factor as types of lubrication, thermal attributes, etc. This present work emphasizes on optimization of life factors, life of bearing along with dynamic load capacity using metaheuristic algorithm based upon the particle swarm methodology (PSO). The two objectives as life factor and dynamic load capacity have been simultaneously optimized using a multi-objective approach under realistic constraint conditions, and also for efficient constraint handling, penalty function method has been used. Right here for formulation of life factors, critical assumptions of operating condition, reliability and materials and processing are being considered. From the above assumptions, it is also considered that the total bearing system is following a series combination of subsystems. A convergence study has been applied to the stated optimization problem. The outcome of this multi-objective approach shows the better efficacy and generosity of the particle swarm algorithm, which can also be implemented for future engineering problems.
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Panda, S.N., Panda, S., Khamari, D.S. (2020). A Multi-objective Approach to Study the Effects of Ball Race Conformity on Optimum Design of Rolling Element Bearing Using Metaheuristics. In: Pati, B., Panigrahi, C., Buyya, R., Li, KC. (eds) Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 1089. Springer, Singapore. https://doi.org/10.1007/978-981-15-1483-8_4
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DOI: https://doi.org/10.1007/978-981-15-1483-8_4
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