Neural parameter calibration for reliable hysteresis prediction in bolted joint assemblies

Abstract

Bolted joints are a common way for connecting multiple structures, and ensuring their safe operation is crucial. Changes in operational conditions, such as variations in tightening torque, can introduce hysteresis mechanisms and complex nonlinearities, making it challenging to analyze and diagnose any issues. Vibration measurements can be used to calibrate a reduced-order model that captures the effects of energy dissipation in bolted joints, such as a Bouc-Wen oscillator. However, the nonlinearities inherent in these systems make calibration problematic, typically requiring ad-hoc knowledge and considerations. In this work, we propose to use a new neural parameter calibration paradigm for this computational model, utilizing a physics-informed neural network to estimate the Bouc-Wen model parameters from time series data. The approach involves using a neural differential equation to represent the hysteresis effect and extracting coefficients from the vibration time series to inform the model. The method generates accurate predictions for the hysteresis loop in a matter of minutes, demonstrating the potential of this approach for real-time monitoring and diagnosis of bolted joint assemblies.