Stochastic modelling and updating of a joint contact interface

• Published in: Mechanical systems and signal processing Vol. 129; pp. 645 - 658
• Main Authors: Jalali, H., Khodaparast, H. Haddad, Madinei, H., Friswell, M.I.
• Format: Journal Article
• Language: English
• Published: Berlin Elsevier Ltd 15-08-2019; Elsevier BV
• Subjects: Bayesian analysis; Bolted joints; Dynamic response; Joints; Nonlinearity; Parameter identification; Parameter uncertainty; Preload; Randomness; Stochastic modelling; Stochastic models; Structural joints; Surface finish; Surface roughness; Uncertainty; Uncertainty; Structural joints; Surface roughness; Stochastic modelling; Preload
• ISSN: 0888-3270; 1096-1216
• DOI: 10.1016/j.ymssp.2019.04.003

•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.
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.