Stochastic modelling and updating of a joint contact interface. (15th August 2019)

• Record Type: Journal Article
• Authors: Jalali, H.; Khodaparast, H. Haddad; Madinei, H.; Friswell, M.I.
• Is Part Of: Mechanical systems and signal processing. Volume 129(2019)
• Page Start: 645
• Page End: 658
• Publication Date: 2019-08-15
• Subjects: Structural joints -- Stochastic modelling -- Preload -- Surface roughness -- Uncertainty; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2019.04.003
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: The effects of structural joint variability on modal parameters are experimentally studied. A stochastic generic joint element is introduced to represent variability in joint. Bayesian model updating is used to identify joint model parameters from measured data. Bayesian model updating is used to identify joint parameters from measured data. Abstract: Dynamic properties of the contact interfaces in joints and mechanical connections have a great influence on the overall dynamic properties of assembled structures. Uncertainty and nonlinearity are two major effects of contact interfaces which introduce challenges in accurate modeling. Randomness in surface roughness quality, surface finish and contact preload are the main sources of variability in the contact interfaces. On the other side, slip and slap are two mechanisms responsible for nonlinear behavior of joints. Stochastic linear/nonlinear models need to be developed for such uncertain structures to be used in dynamic response analysis or system parameter identification. In this paper, variability in linear behavior of an assembled structure containing a bolted lap-joint is investigated by using experimental results. A stochastic model is then constructed for the structure by employing a stochastic generic joint model and the uncertainty in the joint model parameters is identified by using a Bayesian identification approach.