An Environmental and Durability Study of Bonded Structural Patch Repairs with Finite Element and Machine Learning Enabled Predictions

<p>Aircraft and vehicles experience substantial structural and aerodynamic stresses over their operational lifetimes. These stresses can cause damage or structural weakening, especially in aging military and civilian aircraft and vehicles. Depending on the severity of the damage, three options are available to alleviate the damage in these structures or parts: complete replacement of the aircraft or vehicle, replacement of the damaged part, or temporary repair of the damage.</p> <p>The goal of this study is to evaluate damaged specimens repaired with an adhesively bonded patch subjected to quasi-static and fatigue loads under various environmental conditions. The patch repair technique is widely applied to thin, damaged specimens, particularly in the field of aircraft structures, where maintaining structural integrity while minimizing additional weight is crucial. This study focuses on the application of patches to different materials (aluminum and steel), thicker specimens and also evaluates their performance under different environmental conditions, namely room temperature (70F), high temperature (145F), and low temperature (-60F). Additionally, the effect of patch thickness on the performance of thicker specimens is investigated.</p> <p>The damage in aluminum and steel specimens is introduced by generating a central fatigue crack. First the damaged specimens are tested under static, and fatigue loads to determine the failure load and fatigue life of the specimens respectively. The damaged specimens are then adhesively patched using a glass-fiber/epoxy or graphite-fiber/epoxy composite patch. The adhesive is an epoxy. The cured patched specimens are then subjected to static, and fatigue loads to determine the improved failure load and fatigue life respectively. The static tests are repeated at high (145F) and low (-60F) temperatures. Furthermore, a finite element (FE) model is developed to analyze the results and predict the failure loads. It is found that the load carrying capacity and fatigue life of the patched specimens improved significantly. Furthermore, high and low temperatures may also have an effect on the failure load. A major finding of this study is that the failure load of patched specimens can be predicted using the developed FE model.</p> <p>In addition, this study incorporates machine learning tools, such as artificial neural networks, to predict the failure load and crack length at failure of unpatched specimens, and to predict the failure load and fatigue life of patched aluminum and steel specimens containing a central fatigue crack.</p>

Name thesis:degree_name

Doctor of Philosophy (Ph.D.)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Mechanical Engineering

Year dc:date.available

2025

Author dc:creator

• Kokner, Yesim

Contributors dc:contributor

• Feridun Delale

• Bonded Structural Patch Repairs
• Fatigue Testing
• Adhesive Bonding
• Composite Materials
• Static Testing
• Environmental Effects
• Finite Element Analysis
• Stress Intensity Factor (SIF)
• Machine Learning / Artificial Neural Networks (ANN)
• Leave-One-Out Cross Validation (LOOCV)
• Mechanical Engineering

Repository record dc:identifier

https://academicworks.cuny.edu/cc_etds_theses/1253

OAI identifier oai:identifier

oai:academicworks.cuny.edu:cc_etds_theses-2371