Parameter identification of bolted joint models by trust-region constrained sensitivity approach

Abstract

Bolted joints are commonly used in mechanical connections and they pose the complex hysteretic behavior due to the existence of contact and friction. Various bolted joint models including the bilinear model, Iwan models etc have been established, and calibration of such models is a prerequisite to design, analysis and health monitoring with bolted joints. To this end, a novel trust-region constrained sensitivity approach is developed in this paper to identify bolted joint parameters from noisy time-domain response data. At first, parameter identification is formulated as a nonlinear least-squares problem, that finds the parameters to minimize the residual between the measured and predicted data. Then, the sensitivity analysis is conducted to solve the least-squares problem. As is noteworthy, bolted joints are often modelled as parallel union of Jenkins elements which are non-differentiable and the smoothing strategy is essentially called for sensitivity analysis of non-differentiable models. Moreover, to enhance the convergence of the sensitivity approach, the trust-region constraint is additionally introduced, notwithstanding, it is simply tackled by the Tikhonov regularization. Numerical examples and finite element simulations are studied to demonstrate the effectiveness and accuracy of the proposed approach.