Dimensional variation propagation modeling and analysis for single-station assembly based on multiple constraints graph. Issue 3 (1st August 2016)

• Record Type: Journal Article
• Authors: Yu, Jian-feng; Tang, Wen-Bin; Li, Yuan; Zhang, Jie
• Is Part Of: Assembly automation. Volume 36:Issue 3(2016)
• Page Start: 308
• Page End: 317
• Publication Date: 2016-08-01
• Subjects: Dimensional variation -- Geometrical constraints -- Multiple constraints graph -- Variation analysis -- Variation propagation modeling; Automation -- Periodicals; Automatic machinery -- Periodicals; Assembly-line methods -- Periodicals; Industrial engineering -- Periodicals; 670.42705
• DOI: 10.1108/AA-11-2015-090
• Languages: English
• ISSNs: 0144-5154

Abstract

Abstract : Purpose: Modeling and analysis of dimensional variation propagation is a crucial support technology for variation reduction, product/process design evaluation and recognition of variation source. However, owing to the multi-deviation (i.e. part deviations and fixture deviations) and multi-interaction (i.e. part-to-part interaction, part-to-fixture interaction and station-to-station interaction) in assembly processes, it is difficult for designers to describe or understand the variation propagation (or accumulation) mechanism clearly. The purpose of this paper is to propose a variation propagation modeling and analysis (VPMA) method based on multiple constraints aiming at a single station. Design/methodology/approach: Initially, part-to-part constraints (PPCs) and part-to-fixture constraints (PFCs) are applied for the multi-interaction of assembly, and multiple constraints graph (MCG) model is proposed for expressing PPCs, PFCs, parts, as well as the variation propagation relation among them. Then, locating points (LPs) are adopted for representing the deviations in constraints, and formulas for calculating the deviations of LPs are derived. On that basis, a linearized relation between LPs' deviations and part's locating deviations is derived. Finally, a wing box is presented to validate the proposed method, and the results indicate the methodology's feasibility. Findings: MCG is an effective tool for dimensional VPMA, which is shown as an example of this paper. Originality/value: Functions of geometric constraints in dimensional variation propagation are revealed, and MCG is proposed to formulize dimensional variation propagation.