A Neural Network Framework for Predicting Durability and Damage Tolerance of Polymer Composites under Combined Hygrothermal-mechanical Loading

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Published Oct 26, 2023
Partha Pratim Das

Abstract

Fiber-reinforced polymer (FRP) composites are used in crucial structures which are susceptible to a combination of mechanical (static/dynamic) and hygrothermal (moisture absorption and temperature) loads. This research presents a novel artificial neural network (ANN) framework that employs the dielectric permittivity response of FRP composites under combined mechanical-hygrothermal loading to predict the extent of moisture absorption, fatigue life, and remaining useful life. The proposed framework is based on the phenomenological and data-driven study of the effects of static and dynamic mechanical loads along with moisture absorption in the dielectric characteristics of these composites.

How to Cite

Das, P. P. (2023). A Neural Network Framework for Predicting Durability and Damage Tolerance of Polymer Composites under Combined Hygrothermal-mechanical Loading. Annual Conference of the PHM Society, 15(1). https://doi.org/10.36001/phmconf.2023.v15i1.3800
Abstract 31 | PDF Downloads 36

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Keywords

Composites, Prognostics, Neural Network

Section
Doctoral Symposium Summaries