State Space Modeling of Variation Propagation in Multistation Machining Processes Considering Machining-Induced Variations

摘要

In spite of the success of the stream of variation (SoV) approach to modeling variation propagation in multistation machining processes (MMPs), the absence of machininginduced variations could be an important factor that limits its application in accurate variation prediction. Such machining-induced variations are caused by geometricthermal effects, cutting-tool wear, etc. In this paper, a generic framework for machininginduced variation representation based on differential motion vectors is presented. Based on this representation framework, machining-induced variations can be explicitly incorporated in the SoV model. An experimentation is designed and implemented to estimate the model coefficients related to spindle thermal-induced variations and cutting-tool wear-induced variations. The proposed model is compared with the conventional SoV model resulting in an average improvement on quality prediction of 67%. This result verifies the advantage of the proposed extended SoV model. The application of the new model can significantly extend the capability of SoV-model-based methodologies in solving more complex quality improvement problems for MMPs, such as process diagnosis and process tolerance allocation, etc.