Pär Johannesson
Fraunhofer-Chalmers Research Centre for Industrial Mathematics, Sweden
Thomas Svensson
SP Technical Research Institute of Sweden
Leif Samuelsson
Volvo Aero Corporation, Sweden
Ladda ner artikelIngår i: 10th QMOD Conference. Quality Management and Organiqatinal Development. Our Dreams of Excellence; 18-20 June; 2007 in Helsingborg; Sweden
Linköping Electronic Conference Proceedings 26:6, s.
Publicerad: 2008-02-15
ISBN:
ISSN: 1650-3686 (tryckt), 1650-3740 (online)
An important goal of engineering design is to get a reliable system; structure or component. One such well-established method is FMEA (Failure Mode and Effect Analysis); where the aim is to identify possible failure modes and evaluate their effect. A general design philosophy; within robust design; is to make designs that avoid failure modes as much as possible; see e.g. (Davis; 2006). Further; it is important that the design is robust against different sources of unavoidable variation. A general methodology called VMEA (Variation Mode and Effect Analysis) has been developed in order to deal with this problem; see (Johansson; et al.; 2006) and (Chakhunashvili; et al.; 2006). The VMEA is split into three different levels; 1) basic VMEA; in the early design stage; when we only have vague knowledge about the variation; and the goal is to compare different design concepts; 2) advanced VMEA; further in the design process when we can better judge the sources of variation; and 3) probabilistic VMEA; in the later design stages where we have more detailed information about the structure and the sources of variation; and the goal is to be able to asses the reliability.
This paper treats the third level; the probabilistic VMEA; and we suggest a simple model; also used in (Svensson; 1997); for assessing the total uncertainty in a fatigue life prediction; where we consider different sources of variation; as well as statistical uncertainties and model uncertainties.
1. Casella; G.; Berger; R.; (2001); Statistical Inference. (2nd Edition); Duxbury; California.
2. Chakhunashvili; A.; Barone; S.; Johansson; P.; Bergman; B.; (2006); Robust product development using variation mode and effect analysis; Submitted for publication.
3. Davis; T.P.; (2006); Science; engineering; and statistics; Applied Stochastic Models in Business and Industry; Vol. 22; pp. 401–430.
4. Davison; A.C.; Hinkley; D.V.; (1997); Bootstrap Methods and their Applications; Cambridge UP; New York.
5. Ditlevsen; O.; Madsen; H.; (1996); Structural Reliability Methods (1st edition); John Wiley & Sons; Chichester; UK. Internet edition 2.2.5; July 2005; http://www.web.mek.dtu.dk/staff/od/books.htm [25 April 2007].
6. Efron; B.; Tibshirani; R.J.; (1993); An Introduction to the Bootstrap. Chapman & Hall; New York.
7. Hjorth; U.; (1994); Computer Intensive Statistical Methods: Validation Model Selection and Bootstrap; Chapman & Hall; London.
8. Johansson; P.; Chakhunashvili; A.; Barone; S.; Bergman; B.; (2006); Variation Mode and Effect Analysis: a Practical Tool for Quality Improvement; Quality and Reliability Engineering International; Vol. 22; pp. 865-876.
9. Lodeby; K.; (2000); Variability Analysis in Engineering Computational Process; Licentiate thesis in Engineering; Mathematical Statistics; Chalmers; Göteborg.
10. Melchers; R.; (1999); Structural Reliability Analysis and Prediction (2nd edition); John Wiley & Sons; Chichester; UK.
11. Pawitan; Y.; (2001); In All Likelihood: Statistical Modelling and Inference Using Likelihood; Cambridge UP; New York.
12. Svensson T.; (1997); Prediction uncertainties at variable amplitude fatigue; International Journal of Fatigue; Vol. 19; pp. S295-S302.