Stochastic modeling of strain and fatigue sensing elements

摘要

The research presented in this paper focuses on predicting the electromechanical properties of conductive polymercomposite (CPC) materials using stochastic modeling methods and data-driven model adaptation. CPC sensors haverecently garnered attention in the field of structural health monitoring (SHM) due to their atomic similarities withcomposite materials which are an increasingly popular commodity among numerous industries. In this study, a CPCcomposed of carbon black nanoparticles and phenolic-based resin epoxy is manufactured and characterized bothexperimentally and via computational methods. The accuracy of the model is investigated, and the physical parametersdefined in the model are adjusted based on empirical data. A potential manufacturing method for piezoresistive CPCsensors is presented, and preliminary results of sample builds are discussed. The potential applications for such a sensorare introduced, and the implementation of such sensors in industrial SHM applications is considered.