Prediction of the cutting forces in milling operation based on multi-objective optimization of the time series analysis parameters

Accurate simulation of the cutting forces in the milling process is extremely complicated because of variations in cutting process parameters, including the change in modal characteristics of the workpiece along the machining path and the high-temperature gradient around the cutting zone, especially for the modeling of milling forces in flexible thin-walled workpieces. Therefore, proposing effective methods for prediction and control of cutting forces is highly important to ensure the high performance and stability of the cutting process as well as the quality of the machined surface. In this article, a novel method is presented for rapid prediction of cutting force signals based on the combination of the multi-variable time series analysis, the Imperialist Competitive Algorithm (ICA) and Multi-View Embedding (MVE) algorithm. A state-space model of the dynamic system is presented in order to quickly predict the instantaneous dynamic characteristics of the cutting process. The maximum error between the amplitude of experimental and predicted force signals (peak-to-peak) in stable and unstable cutting conditions is less than 8% and 17%, respectively. Therefore, it is illustrated that the presented method has remarkable computational accuracy and convergence speed for predicting the future of the cutting forces under both stable and unstable conditions.