Open AccessProbabilistic Framework for Product Design Optimization
Author(s) -
Joni Keski-Rahkonen
Publication year - 2017
Publication title -
rakenteiden mekaniikka
Language(s) - English
Resource type - Journals
eISSN - 1797-5301
pISSN - 0783-6104
DOI - 10.23998/rm.64959
Subject(s) - probabilistic logic , reliability engineering , reliability (semiconductor) , computer science , process (computing) , failure mode and effects analysis , product (mathematics) , product lifecycle , new product development , probabilistic design , component (thermodynamics) , margin (machine learning) , engineering design process , risk analysis (engineering) , engineering , machine learning , power (physics) , artificial intelligence , mechanical engineering , medicine , physics , geometry , mathematics , business , quantum mechanics , marketing , thermodynamics , operating system
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. Our 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.