Hostname: page-component-586b7cd67f-vdxz6 Total loading time: 0 Render date: 2024-11-26T12:27:19.539Z Has data issue: false hasContentIssue false

Visual tolerance analysis for engineeringoptimization

Published online by Cambridge University Press:  06 March 2014

W. Zhou Wei
Affiliation:
SAS Institute Co., Ltd. No.1 East Chang An Ave, Beijing, P.R. China
M. Moore
Affiliation:
SAS Institute Co., Ltd. Wittington House, Henley Road, Medmenham, Marlow, Buckinghamshire SL7 2EB, UK
F. Kussener*
Affiliation:
SAS Institute, Domaine de Grégy, Grégy-sur-Yerres, 77257 Brie Comte Robert Cedex, France
*
Correspondence:[email protected]
Get access

Abstract

Classic methodologies of DOE are widely applied in design, manufacture, qualitymanagement and related fields. The resulting data can be analysed with linear modelingmethods such as multiple regression which generates a set of equations, Y = F(X), thatenable us to understand how varying the mean of one or more inputs changes the mean of oneof more responses. To develop, scale-up and transfer robust processes to manufacturing wealso need to set the control tolerances of each critical X and understand the extentto which variation in the critical X’s propagate through to variation in theY’s and howthis may impact performance relative to requirements (or specifications). Visual toleranceanalysis provides a simple way to understand and reduce propagation of variation fromX’s toY’s usingmodels developed from DOE’s or historical data. This paper briefly introduces the conceptof tolerance analysis and extents this to visual tolerance analysis through defectprofiles and defect parametric profiles. With the help of visual tolerance analysis,engineering and statistical analysts can work together to find the key factors responsiblefor propagating undesired variation into responses and how to reduce these effects todeliver a robust and cost effective process. A case study approach is used to aidexplanation and understanding.

Type
Research Article
Copyright
© EDP Sciences 2014

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Derringer, G., Suich, R., Simultaneous Optimization of Several Response Variables, J. Qual. Technol. 12, 214219 (1980) Google Scholar
Y. Beers, Introduction to the theory of error (Addison-Wesley Publishing Co., 1957)
A. Lehman, L. Creighton, J. Sall, B. Jones, E. Vang, M. Drake, M. Blackwelder, JMP statistics and graphics guide, Release 7 (SAS Institute Inc., 2007)