Digital Commons Network^™

Neural Network Fatigue Life Prediction In 7075-T6 Aluminum From Acoustic Emission Data, Emeka Chigozie Ibekwe

Master's Theses - Daytona Beach

The objective of this research was to classify acoustic emission (AE) -data associated with fatigue cracks in aluminum fatigue specimens and to use early cycle life AE data to predict failure in such members. An AE data acquisition system coupled with a Kohonen self organizing map and a back propagation neural network were used to perform the analysis. AE waveforms were recorded during fatigue cycling of twenty-four notched 7075-T6 aluminum specimens using broad-band piezoelectric transducers. A Kohonen self organizing map was used to classify the AE flaw growth signals. The signals were classified into three categories based on their AE …

Classification Of In-Flight Fatigue Cracks In Aircraft Structures Using Acoustic Emission And Neural Networks, Christopher Lee Rovik

Master's Theses - Daytona Beach

The research encompassed within this paper deals with the analysis and classification of fatigue cracks in aircraft structures. The particular structure that was examined was the vertical tail section of a Cessna T-303 Crusader aircraft. The analysis was performed using the nondestructive evaluation technique known as acoustic emission (AE), as well as the artificial intelligence of neural networks. Data were taken in a controlled laboratory environment as well as in a flying testbed aboard the aircraft.

The first part of the research involved the analysis of a typical aircraft structure in a controlled laboratory environment. This support structure was fabricated …

In-Flight Fatigue Crack Monitoring Of An Aircraft Engine Cowling, Samuel Gordon Vaughn Iii

Master's Theses - Daytona Beach

This research investigates the feasibility of implementing an in-flight fatigue crack monitoring system in an airplane to identify fatigue crack growth. An acoustic emission data acquisition system coupled with a Kohonen self organizing map neural network were used to perform the analysis.

Fatigue cracking was responsible for ripping the top of a fuselage off an Aloha Airlines Boeing 737-200 as it carried passengers over the Pacific Ocean, killing some aboard. This tragedy is perhaps a precursor of problems to come, as our nation’s aircraft age. These planes experience fatigue as they perform their daily routine of ferrying passengers from location …

Detection Of Fatigue Crack Growth In A Simulated Aircraft Fuselage, Michael Lee Marsden

Master's Theses - Daytona Beach

Acoustic emission (AE) nondestructive testing can detect fatigue cracks as they occur in complex structures. One use for AE has been in-flight detection of fatigue cracks in aircraft. The KC-135 aircraft were successfully monitored as early as 1979. The main problem with this and subsequent applications was an unfavorable signal to noise ratio, the key being to separate the small amplitude crack signals from the large amplitude ambient noise. This was accomplished here through the use of a Kohonen self-organizing map (SOM) neural network.

In order to simulate a fuselage undergoing fatigue, a pressure vessel was constructed from a 0.040 …