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In this thesis, Inception V3, a convolutional neural network model from Google, was partially retrained to categorize pipeline images based on their damage modes. The images for different damage modes of the pipeline were simulated through MATLAB to represent image data collected from in-line pipe inspection. The final convolutional layer

In this thesis, Inception V3, a convolutional neural network model from Google, was partially retrained to categorize pipeline images based on their damage modes. The images for different damage modes of the pipeline were simulated through MATLAB to represent image data collected from in-line pipe inspection. The final convolutional layer of the model was retrained with the simulated pipeline images using TensorFlow as the base platform. First, a small-scale retraining was done with real images and simulated images to compare the differences in performance. Then, using simulated images, a 2^5 full factorial design of experiment and individual parametric studies were performed on five different chosen parameters, including training steps, learning rate, batch size, training data size and image noise. The effect of each parameter on the performance of the model was evaluated and analyzed. It is crucial to understand that due to the nature of the experiment, the learnings may or may not apply to neural network models trained for other tasks. After analyzing the results, the effects and trade-offs for each parameter are discussed in detail. In addition, a method of predicting the training time was proposed. Based on the findings, an optimized model was proposed for this training exercise, with 1180 training steps, a learning rate of 0.01, a batch size of 100 and a training data set of 200 images. The optimized model reached 87.2% accuracy with a training time of 2 minutes and 6 seconds. This study will enhance our understanding in applying machine learning techniques in damage and risk identification.
ContributorsShen, Guangqing (Author) / Liu, Yongming (Thesis director) / Ren, Yi (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
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Description
Widespread knowledge of fracture mechanics is mostly based on previous models that generalize crack growth in materials over several loading cycles. The objective of this project is to characterize crack growth that occurs in titanium alloys, specifically Grade 5 Ti-6Al-4V, at the sub-cycle scale, or within a single loading cycle.

Widespread knowledge of fracture mechanics is mostly based on previous models that generalize crack growth in materials over several loading cycles. The objective of this project is to characterize crack growth that occurs in titanium alloys, specifically Grade 5 Ti-6Al-4V, at the sub-cycle scale, or within a single loading cycle. Using scanning electron microscopy (SEM), imaging analysis is performed to observe crack behavior at ten loading steps throughout the loading and unloading paths. Analysis involves measuring the incremental crack growth and crack tip opening displacement (CTOD) of specimens at loading ratios of 0.1, 0.3, and 0.5. This report defines the relationship between crack growth and the stress intensity factor, K, of the specimens, as well as the relationship between the R-ratio and stress opening level. The crack closure phenomena and effect of microcracks are discussed as they influence the crack growth behavior. This method has previously been used to characterize crack growth in Al 7075-T6. The results for Ti-6Al-4V are compared to these previous findings in order to strengthen conclusions about crack growth behavior.
ContributorsNazareno, Alyssa Noelle (Author) / Liu, Yongming (Thesis director) / Jiao, Yang (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
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Description

We present a new method of chemical quantification utilizing thermal analysis for the detection of relative humidity. By measuring the temperature change of a hydrophilically-modified temperature sensing element vs. a hydrophobically-modified reference element, the total heat from chemical interactions in the sensing element can be measured and used to calculate

We present a new method of chemical quantification utilizing thermal analysis for the detection of relative humidity. By measuring the temperature change of a hydrophilically-modified temperature sensing element vs. a hydrophobically-modified reference element, the total heat from chemical interactions in the sensing element can be measured and used to calculate a change in relative humidity. We have probed the concept by assuming constant temperature streams, and having constant reference humidity (~0% in this case). The concept has been probed with the two methods presented here: (1) a thermistor-based method and (2) a thermographic method. For the first method, a hydrophilically-modified thermistor was used, and a detection range of 0–75% relative humidity was demonstrated. For the second method, a hydrophilically-modified disposable surface (sensing element) and thermal camera were used, and thermal signatures for different relative humidity were demonstrated. These new methods offer opportunities in either chemically harsh environments or in rapidly changing environments. For sensing humidity in a chemically harsh environment, a hydrophilically-modified thermistor can provide a sensing method, eliminating the exposure of metallic contacts, which can be easily corroded by the environment. On the other hand, the thermographic method can be applied with a disposable non-contact sensing element, which is a low-cost upkeep option in environments where damage or fouling is inevitable. In addition, for environments that are rapidly changing, the thermographic method could potentially provide a very rapid humidity measurement as the chemical interactions are rapid and their changes are easily quantified.

ContributorsBridgeman, Devon (Author) / Tsow, Francis (Author) / Xian, Xiaojun (Author) / Chang, Qinan (Author) / Liu, Yongming (Author) / Forzani, Erica (Author) / Biodesign Institute (Contributor)
Created2017-05-24
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Description

Structural health monitoring has been studied by a number of researchers as well as various industries to keep up with the increasing demand for preventive maintenance routines. This work presents a novel method for reconstruct prompt, informed strain/stress responses at the hot spots of the structures based on strain measurements

Structural health monitoring has been studied by a number of researchers as well as various industries to keep up with the increasing demand for preventive maintenance routines. This work presents a novel method for reconstruct prompt, informed strain/stress responses at the hot spots of the structures based on strain measurements at remote locations. The structural responses measured from usage monitoring system at available locations are decomposed into modal responses using empirical mode decomposition. Transformation equations based on finite element modeling are derived to extrapolate the modal responses from the measured locations to critical locations where direct sensor measurements are not available. Then, two numerical examples (a two-span beam and a 19956-degree of freedom simplified airfoil) are used to demonstrate the overall reconstruction method. Finally, the present work investigates the effectiveness and accuracy of the method through a set of experiments conducted on an aluminium alloy cantilever beam commonly used in air vehicle and spacecraft. The experiments collect the vibration strain signals of the beam via optical fiber sensors. Reconstruction results are compared with theoretical solutions and a detailed error analysis is also provided.

ContributorsHe, Jingjing (Author) / Zhou, Yibin (Author) / Guan, Xuefei (Author) / Zhang, Wei (Author) / Zhang, Weifang (Author) / Liu, Yongming (Author) / Ira A. Fulton Schools of Engineering (Contributor)
Created2016-08-16