Time-varying dynamics updating method for chatter prediction in thin-walled part milling process

Abstract

The dynamics of thin-walled parts vary in time with the feed of tool and the removal of material in the milling process, which is a non-negligible phenomenon related to chatter. A new thin-walled parts time-varying dynamics updating method based on a degree of freedom (DOF) reduction model is proposed in this paper for milling chatter prediction. First, to establish the DOF-reduced model, the in-process thin-walled part is divided into two substructures, namely, the substructure with material to be removed and the substructure with finished part, according to the entire removed material. The finite element (FE) model of the substructure with finished part is directly reduced by using the free-interface method since it remains unchanged in the milling process. The FE model of the substructure with material to be removed is reduced by using a combination of the free-interface method and the structural dynamic modification technique. In this way, the changed dynamics of the thin-walled part are updated only in the DOF-reduced model of the substructure with material to be removed. DOF-reduced FE models of the two substructures are coupled by using interface compatibility conditions to establish the DOF-reduced FE model of the entire thin-walled part. Then, the time-varying dynamics of the thin-walled part are obtained by solving the eigenvalue problem of the reduced thin-walled part. Compared to the existing methods, the proposed method updates the time-varying dynamics with high efficiency. Finally, the proposed time-varying dynamics updating method for thin-walled parts is merged into a milling dynamic model to predict chatter. A series of milling experiments are carried out to verify the correctness of the proposed method. The experimental results agree with the simulation results, indicating that the proposed method can be used to predict the time-varying dynamics of the thin-walled parts for chatter analysis.