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The use of a kinematic contraint map to prepare the structure for a dimensional variation analysis model
journal contributionposted on 2013-09-06, 13:09 authored by Leslie C. Sleath, Paul Leaney
Dimensional variation analysis (DVA) models are widely used in the automotive industry to predict how minor variations in the size, shape and location of the component parts are likely to propagate throughout a body structure, suspension, engine or transmission system and how this will affect the overall assembly, operation and performance. This paper is one of in series of four papers that describe how different techniques can be utilised to aid the creation and application of DVA models. This paper explains the development and use of the kinematic constraint map (KCM) method to prepare, in advance, the most appropriate structure for a DVA model. The KCM method provides a concise and comprehensive graphical method that, in one document, can identify all the physical constraints that govern the location and (where applicable) the motion of each component within a complete mechanical system. Once complete, the KCM for a mechanical system contains sufficient information to fully define the structure of the subsequent DVA model. The other three papers cover the use of virtual fixtures, jigs and gauges to achieve the necessary component location and the required variation measurements; the use of two stage DVA models to simulate interdependence between different model configurations and the use of 3D plots to display large numbers of DVA results as a single 3D shape.
- Mechanical, Electrical and Manufacturing Engineering
CitationSLEATH, L.C. and LEANEY, P.G., 2013. The use of a kinematic contraint map to prepare the structure for a dimensional variation analysis model. American Journal of Vehicle Design, 2013, 1 (1), pp. 1 - 8.
Publisher© Science and Education Publishing
- VoR (Version of Record)
NotesThis article was published in the American Journal of Vehicle Design [© Science and Education Publishing Co. Ltd]. The journal's website is at: http://www.sciepub.com/journal/ajvd