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.
Highlights
As a kind of machine tool used for finish machining, grinder plays a significant role in the field of precision manufacturing
Ren et al.[39] proposed a prediction framework for bearing remaining useful life (RUL) by using deep autoencoder and deep neural network (DNN), after comparison, the results showed that it was of higher prediction accuracy than other models
In the process of dynamic parameters identification, firstly, the optimization model is established, that is, the optimization variables and their ranges are determined, and the optimization objective function and constrain conditions are constructed; secondly, the predicted values are obtained by the DNN model, and the experimental values are obtained by modal test; the optimization issue is calculated by the selected appropriate convergence conditions and the appropriate optimization algorithm
Summary
As a kind of machine tool used for finish machining, grinder plays a significant role in the field of precision manufacturing. Aiming at the dynamic modeling and parameters identification of joints for machine tools, many researchers have focused on three kinds of methods: theoretical calculation method, experimental method and the method combining theory with experiment.[6,13] due to the complexity and lower accuracy of. DNN has played a very important role in the above-mentioned high dimensional nonlinear problems It is not widely used in dynamic modeling and dynamic parameters identification for joints of the machine tool, it can be inferred that DNN has a good potential to solve such issues. Taking the self-developed M7120D/H surface grinder as the research object, in this paper, a new approach of dynamic parameters identification for sliding joints based on DNN modeling is presented, the number of hidden layers in the network topology is optimally selected so as to make the training results optimum.
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