Variation propagation modelling for multi-station machining processes with fixtures based on locating surfaces

Modelling the dimensional variation propagation in multi-station machining processes (MMPs) has been studied intensively in the past decade to understand and reduce the variation of product quality characteristics. Among others, the Stream-of-Variation (SoV) model has been successfully applied in a variety of applications, such as fault diagnosis, process planning and process-oriented tolerancing. However, the current SoV model is limited to the MMPs where only fixtures with punctual locators are applied. Other types of fixtures, such as those based on locating surfaces, have not been investigated. In this paper, the derivation of the SoV model is extended to model the effect of fixture- and datum-induced variations when fixtures with locating surfaces are applied. Due to the hyperstatic nature of these fixtures, different workholding configurations can be adopted. This will increase the dimension of the SoV model exponentially and thus may make the model-based part quality prediction extremely complex. This paper presents a method of reducing the complexity of the SoV model when fixtures based on locating surfaces are applied and evaluates the worst-case approach of the resulting part quality