As an important part of structural dynamic analysis

modal parameters are the key to chatter prediction during milling of thin-walled components

and machine learning provides a new paradigm for traditional identification of structural modal parameters. However

for complex curved thin-walled parts

it is difficult to obtain the data in a specific environment and the amount of data collected would be large; uncertain factors such as high-dimensional nonlinear mapping relationships would affect the complex curved thin-walled parts as well. Therefore

a new method based on machine learning is proposed to identify the dynamic characteristics of curved thin-walled parts during milling process. Firstly

the state space model of curved thin-walled milling system is established

the continuous system is discretized

and the stochastic state space equation of generalized milling system discretized is derived. Secondly

based on the random subspace theory

modal parameters of the milling process of curved thin-walled parts are obtained. Then

the sliding window technology is used to reduce dimensionality of the data

extract the signal features

and establish the functional relationship between the input features and modal parameters through the neural network for modal parameter recognition

therefore

to realize the modal parameter recognition of curved thin-walled parts. Finally

milling dynamic parameters of the S-shaped standard part are obtained by using the method proposed in this study and analytical method

verifying accuracy of the proposed method.