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APTA: advanced probability-based tolerance analysis ofproducts

Published online by Cambridge University Press:  12 April 2011

Nicolas Gayton ,
Paul Beaucaire ,
Jean-Marc Bourinet ,
Emmanuel Duc ,
Maurice Lemaire  and
Laurent Gauvrit
Paul Beaucaire
Affiliation:
Clermont Université, IFMA, EA 3867, Laboratoire de Mécanique et Ingénieries, BP 10448, 63000 Clermont-Ferrand, France
Jean-Marc Bourinet
Affiliation:
Clermont Université, IFMA, EA 3867, Laboratoire de Mécanique et Ingénieries, BP 10448, 63000 Clermont-Ferrand, France
Emmanuel Duc
Affiliation:
Clermont Université, IFMA, EA 3867, Laboratoire de Mécanique et Ingénieries, BP 10448, 63000 Clermont-Ferrand, France
Maurice Lemaire
Affiliation:
Clermont Université, IFMA, EA 3867, Laboratoire de Mécanique et Ingénieries, BP 10448, 63000 Clermont-Ferrand, France
Laurent Gauvrit
Affiliation:
RADIALL S.A., rue Velpeau, 37110 Château-Renault, France
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Abstract

In mass production, the customer defines the constraints of assembled products byfunctional and quality requirements. The functional requirements are expressed by thedesigner through the chosen dimensions, which are linked by linear equations in the caseof a simple stack-up or non-linear equations in a more complex case. The customer qualityrequirements are defined by the maximum allowable number of out-of-tolerance assemblies.The aim of this paper is to prove that quality requirements can be accurately predicted inthe design stage thanks to a better knowledge of the statistical characteristics of theprocess. The authors propose an approach named Advanced Probability based ToleranceAnalysis (APTA), assessing the defect probability (called PD)that the assembled product has of not conforming to the functional requirements. Thisprobability depends on the requirements (nominal value, tolerance, capability levels) setby the designer for each part of the product and on the knowledge of production devicesthat will produce batches with variable statistical characteristics (mean value, standarddeviation). The interest of the proposed methodology is shown for linear and non-linearequations related to industrial products manufactured by the RADIALL SA Company.

Keywords

APTAtolerance analysisdefect probabilitycapability levelsFORM approximations
Type
Research Article
Information
Mechanics & Industry , Volume 12 , Issue 2 , 2011 , pp. 71 - 85
Copyright
© AFM, EDP Sciences 2011

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References

Jeang, A., Robust design by variability optimization, Qual. Reliab. Eng. Int. 17 (2001) 131–139 CrossRefGoogle Scholar
Parkinson, D.B., Robust design by variability optimization, Qual. Reliab. Eng. Int. 13 (1997) 97–102 3.0.CO;2-7>CrossRefGoogle Scholar
Savage, G.J., Tong, D., Carr, S.M., Optimal mean and tolerance allocation using conformance-based design, Qual. Reliab. Eng. Int. 22 (2006) 445–472 CrossRefGoogle Scholar
Greenwood, W.H., Chase, K.W., A new tolerance analysis method for designers and manufacturers, Trans. ASME 109 (1987) 112–116 Google Scholar
R.A. Boyles, The Taguchi capability index, J. Qual. Techn. 23 (1991)
Pillet, M., Inertial tolerancing, The Total Quality Management Magazine 16 (2004) 202–209 Google Scholar
F. Scholtz, Tolerance stack analysis methods, Boeing Techn. Rep., 1995
Gladman, C.A., Applying probability in tolerance technology, Trans. Inst. Eng. Australia Mech. Eng. 5 (1975) 82–88 Google Scholar
Mansoor, E.M., The application of probability to tolerances used in engineering designs, Proc. Inst. Mech. Eng. 178 (1963) 29–51 CrossRefGoogle Scholar
Evans, D.H., Statistical tolerancing: The state of the Art. Part II, Methods for estimating moments, J. Qual. Techn. 17 (1975) 1–12 Google Scholar
Graves, S., Tolerance analysis tailored to your organization, J. Qual. Techn. 33 (2001) 293–303 Google Scholar
Ballu, A., Plantec, J.Y., Mathieu, L., Geometrical reliability of overconstrained mechanisms with gaps, CIRP Annals – Manufacturing Technology 57 (2008) 159–162 CrossRefGoogle Scholar
S.D. Nigam, J.U. Turner, Rev. Stat. Appr. Tolerance Analysis
Bender, A., Statistical tolerancing as it relates to quality control and the designer, SAE transactions 77 (1968) 1965–1971 Google Scholar
Gilbert, J.M., Bell, I.M., Johnson, D.R., Circuit design optimization based on quality cost estimation, Qual. Reliab. Eng. Int. 21 (2005) 365–386 CrossRefGoogle Scholar
Parkinson, D.B., The application of reliability methods to tolerancing, Trans. ASME 104 (1982) 612–618 CrossRefGoogle Scholar
Bennis, F., Castagliola, P., Pino, L., Statistical analysis of geometrical tolerances: A case study, Qual. Eng. 17 (2005) 410–427 CrossRefGoogle Scholar
Evans, D.H., Statistical tolerancing: The state of the art. Part III. Shifts and Drifts, J. Qual. Techn. 7 (1975) 72–76 Google Scholar
O. Ditlevsen, H.O. Madsen, Structural reliability methods, John Wiley and Sons, 1996
M. Lemaire, Structural reliability, ISTE/Wiley, 2009
C.G. Glancy, K.W. Chase, A second-order method for assembly tolerance analysis, Proc. ASME Design Engineering Technical Conferences, 1999. DETC99/ DAC-8707