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- Title
Dynamic parameters identification for sliding joints of surface grinder based on deep neural network modeling.
- Authors
Zhang, Wei; Liu, Xurong; Huang, Zhiwen; Zhu, Jianmin
- Abstract
Dynamic parameters of joints are indispensable factors affecting performance of machine tools. In order to obtain the stiffness and damping of sliding joints between the working platform and the machine tool body of the surface grinder, a new method of dynamic parameters identification is proposed that based on deep neural network (DNN) modeling. Firstly, the DNN model of dynamic parameters for working platform-machine tool body sliding joints is established by taking the stiffness and damping parameters as the input and the natural frequencies as the output. Secondly, the number of hidden layers in DNN topology is optimally selected in order to the optimal training results. Thirdly, combining the predicted results by DNN model with experimental results by modal test, the stiffness and damping are identified via cuckoo search algorithm. Finally, the relative error between the predicted and experimental results is less than 2.2%, which achieves extremely high prediction precision; and thereby indicates the feasibility and effectiveness of the proposed method.
- Subjects
ARTIFICIAL neural networks; PARAMETER identification; NUMERICAL control of machine tools; MACHINE tool industry; SEARCH algorithms; MACHINE performance; MACHINE tools
- Publication
Advances in Mechanical Engineering (Sage Publications Inc.), 2021, Vol 13, Issue 2, p1
- ISSN
1687-8132
- Publication type
Article
- DOI
10.1177/1687814021992181