Identification of the Mechanical Joint Parameters with Model Uncertainty

Joint parameter identification is a key problem in the modeling of assembled complex structural. Dynamic behavior of mechanical joints may be with random feature due to the stochastic properties of preload, joint geometry, contact surface and its finishes, etc. A procedure for stochastic mechanical joint identification based on a novel model updating technique combined with probabilistic approach is proposed in this paper. Uncertainty of the mechanical joint of assembled structures is modeled via springs and dampers with normal distributed stochastic variables. Stochastic properties of the modal parameters of the assembled structure are obtained via EMA. Mean values of the joint parameters are then identified by a novel model updating technique, which is based on a meta-model via Response Surface Method (RSM). Variances of the parameters are finally estimated by using the probabilistic approach solving an inverse uncertainty propagation problem. A case study with a real assembled frame structure is conducted to demonstrate the feasibility and effectiveness of the proposed procedure.