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Design optimization of an angular contact ball bearing for the main shaft of a grinder.pdf

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Design optimization of an angular contact ball bearing for the main shaft of a grinder.pdf

Mechanism and Machine Theory 104 (2016) 287–302 Contents lists available at ScienceDirect Mechanism and Machine Theory journal homepage: /locate/mechmt Design optimization of an angular contact ball bearing for the main shaft of a grinder Seung-Wook Kim a, Kibong Kang a, Kichan Yoon b, Dong-Hoon Choi c,? a Graduate School of Mechanical Convergence Engineering, Hanyang University, Seoul, South Korea b BU Chassis System, Automotive, Schaef?er Greater China, Shanghai, China c School of Mechanical Engineering, Hanyang University, Seoul, South Korea article info Article history: Received 13 July 2015 Received in revised form 19 April 2016 Accepted 7 June 2016 Available online 23 June 2016 Keywords: Angular contact ball bearing Multi-objective discrete optimization Quasi-static analysis Grinder abstract A conventional trial and error approach toward the design of non-standard bearings takes a signi?cant amount of time to obtain an adequate design. In this study, a non-standard angular contact ball bearing for the main shaft of a grinder was optimized using design automation and optimization techniques. To manufacture a product as precisely as possible with a grinder, the radial and axial stiffness values of the grinder bearing must be selected as objective functions. To treat two objective functions, this study employed a global criterion method as a multiobjective optimization methodology. Eight constraints on the manufacturing, ?lm thickness, friction, and fatigue life were imposed. Six geometric variables and an axial preload were selected as design variables. All design variables were regarded as discrete because they should have manufacture-possible dimensions. Quasi-static analysis taking dynamic effects into account was employed to analyze bearing performance. For ef?cient discrete optimization, this study proposed a hybrid method in which a micro-genetic algorithm and regression-based sequential approximate optimizer were both employed. Optimization results reveal