Date of Award
Doctor of Philosophy (PhD)
Dr. Oliver J. Myers
Dr. Suyi Li
Dr. Gang Li
Dr. Garrett J. Pataky
Asymmetric bistable carbon fibre reinforced plastic (CFRP) composites enable a broad range of applications as they can sustain multiple stable configurations and have small snap-through load requirements. These unique features, coupled with their light strength-to-weight and stiffness-to-weight ratios, have made them preferred options for multifunctional systems. This study investigates the fatigue and hygroscopic response of 2-ply, [0/90] bistable CFRP laminates and proposes predictive modeling approaches for improved performance.
While previous studies widely researched and documented the fatigue of general composites in axial loading, fatigue analysis of asymmetric bistable composites in the out-of-plane snap-through direction is inadequate. This study performs fatigue tests in this direction to snap the laminate between the two stable states during the cyclic loading and capture the stiffness and damage evolution for different combinations of test parameters incorporating frequencies from 1 to 10 Hz, two boundary conditions, and temperatures from 22°C to 150°C. The fatigue tests reveal that stiffness degradation and damage progression occur in two stages, while the specimens never experience global failure for any test combination. This analysis proposes a damage definition in terms of load, adopts the damage index with two analytical models: (1) Shiri Model and (2) Wu Model, and presents a range of model parameter values to predict damage during the first two stages for the specified conditions. The curvature and snap-through load evaluation demonstrates that fatigue loading does not affect these parameters. This finding enables application protocols to maintain bistable performance for a broad range of loading and environmental conditions.
This study extends the predictive analysis by proposing machine learning (ML) modeling approaches to predict the non-linear load response of bistable composites in fatigue loading and utilizing the load response to predict stiffness and damage with six selected ML models: decision tree (DT), random forest (RF), adaptive boosting (ADA), gradient boosting machine (gb), artificial neural network (ANN) and k-nearest neighbors (KNN). The ML models are trained on the fatigue data acquired during the first part of the analysis. This approach evaluates the model performances with five validation tests incorporating tests with frequency and temperatures inside and outside the domain of the training data. The model assessment reveals that the ML models can capture the non-linear load-displacement response and provides a reasonable prediction of load, stiffness, and damage while offering expandability of prediction outside the training feature domain.
Another aspect of this study is investigating the hygroscopic response of bistable composites in a variable relative humidity environment. The moisture absorption of asymmetric 2-ply, 4-ply, and 6-ply laminates is inspected in the laboratory environment to determine their curvature and snap-through load at different moisture content levels. The analysis reports a combined thermal and moisture expansion coefficient to predict these parameters with an improved analytical model and a finite element model. The models predict a linear drop in both parameters with increasing moisture content due to the plasticization of the epoxy polymer network and the subsequent relaxation of residual thermal strain, and these predictions demonstrate good agreement with test results.
Chowdhury, Shoab Ahmed, "Investigation of Fatigue Response with Analytical and Machine Learning Models and Hygroscopic Analysis of Asymmetric Bistable CFRP Composites" (2023). All Dissertations. 3401.
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