Kalman filter with applications to assembly accuracy state estimation for precision machine tool

In terms of variation control strategy for sheet components assembly, a state space model (SSM) of variation propagation for precision machine tools in assembly process is established and a new method for optimally estimating assembling error by Kalman filter is proposed. Datum flow chain (DFC) of the machine is set up according to the machine topology, and the position and orientation error of key character of the part in DFC is defined as state variable. The SSM is introduced to describe the variation propagation and accumulation of assembly process to acquire the mathematical expression. The optimal estimation and corresponding covariance matrix of assembly error can be calculated by Kalman filter method, which synthesizes the measuring results of current assembly step. The suggested approach is applied to the assembly process in a precision machining center. The results show that the variances of estimation errors at final assembly step are reduced significantly by 63% using Kalman filter method compared with ones from the traditional tolerance analysis.