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A new method for optimizing the topology of hinge‐free and fully decoupled compliant mechanisms with multiple inputs and multiple outputs
Author(s) -
Rong Jianhua,
Rong Xuanpei,
Peng Luo,
Yi Jijun,
Zhou Quan
Publication year - 2021
Publication title -
international journal for numerical methods in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.421
H-Index - 168
eISSN - 1097-0207
pISSN - 0029-5981
DOI - 10.1002/nme.6644
Subject(s) - hinge , topology optimization , compliant mechanism , topology (electrical circuits) , constraint (computer aided design) , mathematical optimization , control theory (sociology) , computer science , coupling (piping) , range (aeronautics) , mathematics , engineering , control (management) , finite element method , structural engineering , mechanical engineering , artificial intelligence , aerospace engineering , geometry , combinatorics
Compliant mechanisms with multiple inputs and multiple outputs have a wide range of applications in precision mechanics, for example, cell manipulations, electronic microscopes, and so on. The movement uncoupling and maximum desired output displacements among these devices all become critical because many inputs and outputs are involved. The topology optimization design of compliant mechanisms, which can solve output coupling and input coupling problems, hinge and gray region problems, and the multiple‐objective optimal problem, is an important topic of researches for achieving fully decoupled motion. It is also a challenge area of research due to serious conflicts of between the four of output and input uncoupling constraints, volume constraint, the hinge‐free requirement, and the good black/white solution requirement. In order to comprehensively solve these problems, a simple optimization model overcoming these serious conflicts is posed, which includes small change rate constraints of structural compliances corresponding to the driving input loads and output point virtual loads. Then, the multiple output displacement functions of the model are equivalently converted into non‐negative functions. The multiple‐objective model is converted into a single‐objective optimization model by using a bound variable. The method of moving asymptotes (MMA) algorithm is adopted to solve it. Several examples are presented to demonstrate the validity of the proposed method.