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

Abstract

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.

Manufacturing variation models in multi-station machining systems

“…Previous works were limited to 3-2-1 orthogonal fixture layouts based on locators and generic cutting-tool path deviations without explicitly including machining-induced errors. Loose et al [7] extended the state space model formulation by including general non-orthogonal fixture layouts based on locators.More recently, Abellan-Nebot et al [8] expanded the formulation of matrix B k in order to include common machining sources of variation such as those due to tool wear, thermal expansions, cutting-tool deflections and geometric-kinematic machine-tool errors.…”

An Improved Equivalent Fixture Error Model for Machining Process

“…Jin et al [6] introduced state space model to describe the dimensional variation accumulation and propagation for multistage body assembly processes. To obtain the SoV models for MMPs, lots of efforts have been done [1,[7][8][9][10][11][12]. Huang et al [7] proposed an implicit non-linear SoV model to predict the variation accumulation and its propagation for MMPs.…”

“…Abellan-Nebot et al [9] demonstrated that the absence of machining-induced variations in state space modeling of variation propagation for MMPs could be an important factor that influences the accurate in variation prediction. Using DMVs, Abellan-Nebot et al [10] introduced such machining-induced variations including geometricthermal effects, cutting force-induced variations, and/or cutting-tool wear, etc into the SoV model. Several review papers have introduced SoV modeling methods briefly [11][12][13][14].…”

Variation source identification of multistage manufacturing processes through discriminant analysis and stream of variation methodology: a case study in automotive industry

“…Jin & Shi (1999) proposed a state space model, based on fundamental physical laws that can be used as a physical model for modeling variation propagation. This state space model that has been used to model variation transmission between stages in various multiple manufacturing processes is proposed by Xie et al (2012) and Liu et al (2009) for assembly processes and by Loose et al (2007) ;Djurdjanovic & Ni (2003) and Abellan-Nebot et al (2012) for machining process. In monitoring and diagnosis areas of quality improvement study, that constitutes the research area of this paper, the main technique that can be used for process monitoring and fault diagnosis of MMPs is statistical process control.…”