Probabilistic framework for product design optimization and risk management

Content Provider	Scilit
Author	Keski-Rahkonen, J. K.
Copyright Year	2018
Description	Journal: Iop Conference Series: Materials Science and Engineering Probabilistic methods have gradually gained ground within engineering practices but currently it is still the industry standard to use deterministic safety margin approaches to dimensioning components and qualitative methods to manage product risks. These methods are suitable for baseline design work but quantitative risk management and product reliability optimization require more advanced predictive approaches. Ample research has been published on how to predict failure probabilities for mechanical components and furthermore to optimize reliability through life cycle cost analysis. This paper reviews the literature for existing methods and tries to harness their best features and simplify the process to be applicable in practical engineering work. Recommended process applies Monte Carlo method on top of load-resistance models to estimate failure probabilities. Furthermore, it adds on existing literature by introducing a practical framework to use probabilistic models in quantitative risk management and product life cycle costs optimization. The main focus is on mechanical failure modes due to the well-developed methods used to predict these types of failures. However, the same framework can be applied on any type of failure mode as long as predictive models can be developed.
Related Links	http://iopscience.iop.org/article/10.1088/1757-899X/351/1/012007/pdf
ISSN	17578981
e-ISSN	1757899X
DOI	10.1088/1757-899x/351/1/012007
Journal	Iop Conference Series: Materials Science and Engineering
Issue Number	1
Volume Number	351
Language	English
Publisher	IOP Publishing
Publisher Date	2018-05-14
Access Restriction	Open
Subject Keyword	Journal: Iop Conference Series: Materials Science and Engineering
Content Type	Text
Resource Type	Article