A comprehensive study of linear variation propagation modeling methods for multistage machining processes

Abstract

Stream of variation (SoV) model is an effective tool, which can describe the dimensional variation and propagation for multistage machining processes. Compared with traditional single-stage error models considering errors from a single machining stage only, SoV model can depict the complicated interactions between different errors at different stages. This paper reviews three major linearized SoV modeling methods for multistage machining processes based on the literature published over the last two decades. These three linearized SoV modeling methods are based on differential motion vectors, equivalent fixture error, and kinematic analysis, respectively. Each method has its corresponding advantages and disadvantages. The model using differential motion vector (DMV) concept from robotics incorporates fixture-, datum-, and machining-induced variations in the multistage variation propagation for orthogonal 3-2-1 fixturing layout while the primary datum deviation is currently overlooked. The kinematic analysis method can address general fixture layouts rather than being limited to orthogonal 3-2-1 fixture layouts. The variation propagation model using the equivalent fixture error concept can directly model the process physics regarding how fixture, datum, and machine tool errors generate the same pattern on the features of the workpiece. The results of these three models with respect to an example are also given to make a comparison. Finally, a perspective overview of the future research about SoV methods is presented.