Document Type
Article
Source Publication Title
American Institute of Aeronautics and Astronautics (AIAA) SCITECH 2023 Forum
DOI
10.2514/6.2023-0773
Abstract
With the increased use of composite materials, researchers have developed many approaches for structural and prognostic health monitoring. Broadband Dielectric Spectroscopy (BbDS)/Impedance Spectroscopy (IS) is a state-of-the-art technology that can be used to identify and monitor the minute changes in damage initiation, accumulation, interactions, and the degree of damage in a composite under static and dynamic loading. This work presents a novel artificial neural network (ANN) framework for fiber-reinforced polymer (FRP) composites under fatigue loading, which incorporates dielectric state variables to predict the life (durability) and residual strength (damage tolerance) from real-time acquired dielectric permittivity of the material. The findings of this study indicate that this robust ANN-based prognostic framework can be implemented in FRP composite structures, thereby assisting in preventing unforeseeable failure.
Disciplines
Engineering | Materials Science and Engineering
Publication Date
1-19-2023
Language
English
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Das, Partha Pratim; Elenchezhian, Muthu Ram Prabhu; Vadlamudi, Vamsee; and Raihan, Rassel, "Artificial Intelligence Assisted Residual Strength and Life Prediction of Fiber Reinforced Polymer Composites" (2023). Institute of Predictive Performance Methodologies (IPPM-UTARI). 1.
https://mavmatrix.uta.edu/utari_ippm/1
Comments
Refer to https://youtu.be/Z6z1Jm8WBk8 as well.