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Stochastic modeling of composite strain and fatigue sensing elements
Author(s): Tyler B. Albright; Jared D. Hobeck
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Paper Abstract

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

Paper Details

Date Published: 27 March 2019
PDF: 11 pages
Proc. SPIE 10970, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2019, 1097014 (27 March 2019); doi: 10.1117/12.2514130
Show Author Affiliations
Tyler B. Albright, Kansas State Univ. (United States)
Jared D. Hobeck, Kansas State Univ. (United States)


Published in SPIE Proceedings Vol. 10970:
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2019
Jerome P. Lynch; Haiying Huang; Hoon Sohn; Kon-Well Wang, Editor(s)

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