LAUSR

Abstract Mechanistic models are commonly used to compute the machining forces in milling operations. The constant coefficients of mechanistic models, also known as Specific Force Coefficients (SFC), are directly linked to the mechanics of shearing, friction, and ploughing actions during chip formation. Because of this direct linkage, online monitoring of them can provide informative metrics for process monitoring. However, SFC are usually determined offline by using the machining forces measured under various cutting conditions (e.g. feedrate and cutting speed). In this paper, the performance of Recursive Least Squares (RLS) and Kalman Filter (KF) algorithms in online monitoring of SFC is studied. In the RLS method, instantaneous milling forces are expressed as linear functions of unknown SFC. The RLS algorithm is then used to recursively identify the SFC from the milling forces measured at discrete time steps during the process. In the Kalman Filter method, the SFC are modelled as constant stochastic processes, and the dynamics of their variation is described by a state-space model in which the SFC are the state variables and the milling forces are outputs. A Kalman Filter state observer is then designed to recursively estimate the SFC from the forces measured at discrete time steps. Numerical simulations and experimental studies are presented to verify the effectiveness of the proposed methods in identifying SFC in various cutting conditions.