Process-oriented tolerancing using the extended stream of variation model

Abstract

Current works on process-oriented tolerancing for multi-station manufacturing processes (MMPs) have been mainly focused on allocating fixture tolerances to ensure part quality specifications at a minimum manufacturing cost. Some works have also included fixture maintenance policies into the tolerance allocation problem since they are related to both manufacturing cost and final part quality. However, there is a lack of incorporation of other factors that lead to increase of manufacturing cost and degrade of product quality, such as cutting-tool wear and machine-tool thermal state. The allocation of the admissible values of these process variables may be critical due to their impact on cutting-tool replacement and quality loss costs. In this paper, the process-oriented tolerancing is expanded based on the recently developed, extended stream of variation (SoV) model, which explicitly represents the influence of machining process variables in the variation propagation along MMPs. In addition, the probability distribution functions (pdf) for some machining process variables are analyzed, and a procedure to derive part quality constraints according to GD&T specifications is also shown. With this modeling capability extension, a complete process-oriented tolerancing can be conducted, reaching a real minimum manufacturing cost. In order to demonstrate the advantage of the proposed methodology over a conventional method, a case study is analyzed in detail.

A modification of DMVs based state space model of variation propagation for multistage machining processes

“…The SoV model mathematically represents the relationship between the feature deviation of the workpiece and the deviation sources (Abellan-Nebot et al , 2013a, 2013b). And the impact of the error sources on the deviations of newly generated features of the part in N-station MMPs can be described as a state space model (Zhou et al , 2003a, 2003b): …”

“…Compared with the conventional SoV model without considering the machining-induced variations, there is approximate 33 per cent improvement on an average on the quality prediction of the extended SoV model (Abellan-Nebot et al , 2012). The SoV models have been applied in many fields, such as process monitoring (Djurdjanovic and Ni, 2003; Du et al , 2015a, 2015b), fault diagnosis (Zhou et al , 2003a, 2003b), quality-assured setup planning (Liu et al , 2009) and process-oriented tolerancing (Abellan-Nebot et al , 2013a, 2013b). More detailed descriptions of existing research work on SoV model and/or the corresponding applications can be found in review (Ceglarek et al , 2004; Shi and Zhou, 2009; Abellan-Nebot et al , 2013a, 2013b; Yang et al , 2016).…”

Variation propagation modelling in multistage machining processes using dual quaternions

“…In the past two decades, variation propagation modelling in multistage manufacturing processes has been actively researched. A significant contribution has been made in the directions of part quality prediction [1][2][3][4], variation source identification [5,6], variation compensation [7,8], and process-oriented tolerancing [9] in multistage manufacturing processes. These contributions are often studied under the concept of stream of variation (SoV) and model of a manufactured part [10].…”

Modeling Product Manufacturing Reliability with Quality Variations Centered on the Multilayered Coupling Operational Characteristics of Intelligent Manufacturing Systems

“…SoV is widely used in multistage complex manufacturing systems to control and improve product quality [ 23 , 24 ]. Abella’n-Nebot et al [ 25 ] proposed an extended SoV model on the basis of process tolerances, which clarify the effects of variables in the machining process on the costs of the manufacturing system and product quality. Zhang et al [ 26 ] constructed a state space model to characterize the relationship between the source variation and the final assembly variation considering the part manufacturing error, fixture position error, and relocation-induced error.…”