Abstract: Cutting force is highly sensitive and capable of rapid response to changes in cutting state, which is considered as the most valuable physical quantity for machining state monitoring and adaptive machining. Since there is no need to introduce additional sensing components, an online prediction cutting force solution based on inherent servo monitoring signals in CNC systems has the potential to achieve long-term, low-cost, and accurate monitoring of cutting force. However, the relationship between servo monitoring signals and cutting force is very complex. Therefore, a cutting force online prediction method based on recurrent neural networks is proposed. Firstly, the problem of feed-axis cutting force prediction based on machine tool servo signals is defined as a nonlinear dynamic system modeling problem with adaptive time delay. Then, two types of recurrent neural networks, long short-term memory neural network (LSTM NN) and gated recurrent unit neural network (GRU NN), are introduced to directly learn the dynamic prediction model from end-to-end observation data. A set of variable speed hole milling experiments are carried out to construct a cutting excitation dataset under time-varying working conditions for comparative verifications. For the X-axis with more complex dynamic characteristics, LSTM NN has better prediction performance, with a relative root mean square error of 17.62%. For the Y-axis with relatively more simple dynamic characteristics, GRU NN has better prediction performance, with a relative root mean square error of 11.74%.

Key words: cutting force, machining state monitoring, adaptive machining, recurrent neural network, servo monitoring signal