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Optimizing Weight of Housing Elements of Two-stage Reducer by Using the Topology Management Optimization Capabilities Integrated in SOLIDWORKS: A Case Study

Department of Mechanical Engineering and Machine Tools, Technical University of Varna, 9010 Varna, Bulgaria
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Machines 2019, 7(1), 9; https://doi.org/10.3390/machines7010009
Received: 15 December 2018 / Revised: 8 January 2019 / Accepted: 11 January 2019 / Published: 15 January 2019
(This article belongs to the Special Issue Advances in CAD/CAM/CAE Technologies)
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Abstract

This paper presents the results of a conducted topology management optimization study based on the finite element analysis on a two-stage spur gear reducer housing body and cover using the SOLIDWORKS Simulation module. The main goal of the study is to optimize the overall weight of the reducer by thinning specific areas of the casted gearbox housing elements according to the calculated minimal strain energy. The topology optimization algorithm that is used in current research gives an optimal structural shape of the housing elements of the reducer with the largest stiffness, considering the given amount of mass that will be removed from the initial design space. The complete sequence of steps for conducting the topology management optimization study is shown, taking into account the constraints arising from the construction features and the method of manufacturing the housing elements of the gear reducer. Conclusions on the use of the topology optimization results are given and potential directions for further development of the approach are also identified. View Full-Text
Keywords: topology management optimization; gear reducer housings; power transmission; finite element analysis; SOLIDWORKS topology management optimization; gear reducer housings; power transmission; finite element analysis; SOLIDWORKS
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Slavov, S.; Konsulova-Bakalova, M. Optimizing Weight of Housing Elements of Two-stage Reducer by Using the Topology Management Optimization Capabilities Integrated in SOLIDWORKS: A Case Study. Machines 2019, 7, 9.

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