The dimensions of nanoobjects are important because the properties of nanoobjects are related to dimension and automatic manufacturing inspection of nanoobject requires dimensional information. The current approaches to estimate the projection lengths (one of the important dimensions of nanorods) are manual, error leading and time consuming. So, an automated approach is essential to determine the projection lengths . We propose an innovative approach to estimate projection lengths of nanorods from scanning electron micrographs. Extracting projection length is very crucial to dimensional quality engineering because once we have projection length, we can determine actual length of each nanorod and establish a proper measurement system. The large volume of production of nanorods depends on proper manufacturing process of nanorod which is challenging until there is any proper measurement process exists to understand the manufacturing process of nanorod. Our proposed automated algorithm can extract nanorods by following steps: consider an image of nanorod, determine top edges and side edges of each nanorod separately, associate edges to corresponding nanorods, extract correct nanorods. Then we estimate the projection lengths. Low signal to noise ratio and high degree of overlaps of nanorods in scanning electron micrographs are the major challenges in this research task. Currently there is no image processing algorithm that can measure the dimensions of nanorods from scanning electron micrograph. Hence our research work will contribute to this area. We calculate the total number of true and false detection of nanorods obtained by our automated algorithm. We compare the algorithm with other relevant algorithms (e.g. Normalized cut, Snake and simagis). This research work is promising to dimensional quality control as it can contribute to establish a proper measurement system of nanomanufacturing. / A Thesis submitted to the Department of Industrial and Manufacturing Engineering in partial fulfillment of the requirements for the degree of Master of Science. / Summer Semester 2015. / July 09, 2015. / Nanoinformatics, Nanorods, Particle detection algorithm, Projection Length, Quality / Includes bibliographical references. / Abhishek K. Shrivastava, Professor Directing Thesis; Chiwoo Park, Committee Member; Arda Vanli, Committee Member.

Carbon fiber reinforced composites (CFRPs) are light weight, have high strength and modulus, which make them qualified candidates for numerous automotive and aerospace industries. However one of the major technical challenges for these materials in airplanes bodies, in terms of aircraft safety and durability, is lightning strike protection (LSP). This is due to the inadequate electrical properties of normal CFRPs. Although, CFRPs have relatively good in-plane electrical conductivity compared to neat epoxy resin, they lack metal-like high conductivity for LSP applications. To solve this issue, metal materials such as copper or aluminum mesh are attached to carbon fiber composites in the aircraft's body. This solution is not satisfactory due to heavy weight of metals and the occurrence of galvanic corrosion between metals and CFRPs. On other hand carbon nanotubes (CNTs) possess great potential for enhancing high-performance and multifunctional nanocomposites and exhibit incredible mechanical, electrical and thermal properties, which can replace the metal mesh materials. This study investigates the use of carbon nanotubes to enhance composite conductivity for potential metal material replacement and their basic current- carrying capacity. During testing, the samples were exposed to high temperatures with electrical current-induced thermal heating in atmosphere condition. High electrical currents generated Joule heating causing thermal degradation at over 600 °C (main failure mechanism). Micro-structural changes of the samples after electrical current heating were observed using SEM and EDS analyses. The result show resin evaporation at the notch and nucleation of Fe and Ti particles. CNTs enhance the current carrying capability of the CFRP and Buckypaper (preformed thin CNT film) composites. However, the performance improvement differs upon on the epoxy used as well as the manufacturing method and nantubes types. In addition higher conductivity of the samples contributed to higher current-carrying density at the breakdown point. These results provide a preliminary understanding of the effect of the CNTs on the current carrying capability and the electrical properties of CNT enhanced CFRPs and BP composites. / A Thesis submitted to the Department of Industrial and Manufacturing Engineering in partial fulfillment of the requirements for the degree of Master of Science. / Fall Semester, 2010. / November 2, 2010. / Composites, Buckypapers, CFRPs, Lightning strike protection / Includes bibliographical references. / Richard Zhiyong Liang, Professor Directing Thesis; Chuck Zhang, Committee Member; Chad Zheng, Committee Member.

This thesis highlights the development of an immerse and high fidelity virtual environment for lifetime and reliability analysis of circuit cards for nuclear power electronics. The developed virtual environment allows prediction of total life time, overall reliability and maintainability for circuit cards (system level) and their components (component level) through a simulation methodology. Component repair or replace approaches are used within this simulation which gives the user the ability to choose between them based on experience and component history. As excessive temperature is the primary cause of poor reliability in electronics, quantitative accelerated life tests are designed to quantify the life of circuit cards under different thermal stresses to produce the data required for accelerated life data analysis. This research provides a better understanding and helps in predicting overall system failure characteristics for any given configuration. It allows the user to identify components which contribute the most to downtime and to determine the effect of design alternatives on system performance in a cost-effective manner.

Young novice drivers are at a significantly higher risk of having a fatal vehicle crash than experienced drivers. One of the main causes for this statistic is that these drivers lack risk perception skills. They have not developed the ability to efficiently perceive or predict risks while driving. This dissertation will detail the research undertaken to study and remedy this problem. Driving behaviors of drivers of different age groups were first evaluated to establish metrics that could discriminate between novice and experienced drivers. The comparison was carried out in a driving simulator while collecting vehicle parameters and eye movement information from the drivers. It was found that eye glance locations serve as effective indicators of the driver’s level of risk awareness and perception: experienced drivers were better at scanning the appropriate locations for risk relevant elements while driving. This finding led to the development of a PC-Based Risk Awareness and Perception Training Program (RAPT) that was used to test the feasibility of training risk perception skills among novice drivers. Evaluation of the trained drivers’ behaviors showed improved risk perception, both in a driving simulator and in the field. The training also generalized to driving situations that were conceptually different from the ones used in training. The training program was primarily designed to target the tactical risk perception skills of drivers, i.e., those skills necessary to detect a potentially hazardous scenario materializing at a particular time and location, which can be detected by scanning for and recognizing various configurations and dynamics of elements in the driving situation. However the training also resulted in the improvement of strategic risk anticipation behavior (i.e., scanning patterns when there is no obvious threat but where a driver is required to be always aware of the possibilities of unexpected hazards). The trained drivers had eye movements that would facilitate efficient and early detection of hazards, while at the same time they were able appropriately to regulate the distribution of their glances towards and away from the forward roadway. This risk perception training can thus be used as an effective intervention for the vulnerable novice driver population.

abstract: In conventional supervised learning tasks, information retrieval from extensive collections of data happens automatically at low cost, whereas in many real-world problems obtaining labeled data can be hard, time-consuming, and expensive. Consider healthcare systems, for example, where unlabeled medical images are abundant while labeling requires a considerable amount of knowledge from experienced physicians. Active learning addresses this challenge with an iterative process to select instances from the unlabeled data to annotate and improve the supervised learner. At each step, the query of examples to be labeled can be considered as a dilemma between exploitation of the supervised learner's current knowledge and exploration of the unlabeled input features. Motivated by the need for efficient active learning strategies, this dissertation proposes new algorithms for batch-mode, pool-based active learning. The research considers the following questions: how can unsupervised knowledge of the input features (exploration) improve learning when incorporated with supervised learning (exploitation)? How to characterize exploration in active learning when data is high-dimensional? Finally, how to adaptively make a balance between exploration and exploitation? The first contribution proposes a new active learning algorithm, Cluster-based Stochastic Query-by-Forest (CSQBF), which provides a batch-mode strategy that accelerates learning with added value from exploration and improved exploitation scores. CSQBF balances exploration and exploitation using a probabilistic scoring criterion based on classification probabilities from a tree-based ensemble model within each data cluster. The second contribution introduces two more query strategies, Double Margin Active Learning (DMAL) and Cluster Agnostic Active Learning (CAAL), that combine consistent exploration and exploitation modules into a coherent and unified measure for label query. Instead of assuming a fixed clustering structure, CAAL and DMAL adopt a soft-clustering strategy which provides a new approach to formalize exploration in active learning. The third contribution addresses the challenge of dynamically making a balance between exploration and exploitation criteria throughout the active learning process. Two adaptive algorithms are proposed based on feedback-driven bandit optimization frameworks that elegantly handle this issue by learning the relationship between exploration-exploitation trade-off and an active learner's performance. / Dissertation/Thesis / Doctoral Dissertation Industrial Engineering 2020

The process of thought applied to establish a thesis topic has been one of non-acceptance of the situations on face value. As such the future profitability of the Nissan Stamping Plant has been considered. A mental model has been established in the authors mind that a definite problem exists within this area. This has led to the formulation of a framework that can be used to handle the problem of low profits that will eventually lead to the closure of the operation. Status-Quo has remained within the operations for almost 25 years, but now changes in Government Legislation pose threats. This leads to the question of how can the profits be improved. The framework was built around a philosophy of continual search for the truth. The scientific method has been applied to understand the theories of a single or double loop response whilst proceeding through the Plan, Do, Check, and Action cycle.

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Carbon nanotubes have attracted tremendous attention in both industrial and academic world due to their incomparable attributes in various areas since their first discovery. In recent years, as one of the most distinctive applications, conductive carbon nanotube composites have been investigated. Comparing with traditional materials, conductive nanocomposites have several advantages including extra-low threshold of particle content, no degradation of mechanical properties, ability to absorb magnetic interference, and tunable conductivity. The main objectives of this thesis were to develop a comprehensive electrical resistivity measurement system which can cover resistivity range from 10E-6 to 10E18 §Ùcm and understand the relationship between their nanostructure and electrical resistivity. The resistivity of various nano-materials produced at FACCT including random and aligned buckypaper (BP), direct mixing and casting samples, BP composites, and multi-scale composites were investigated and characterized. It was found that less than 1% tube loading can make resistivity of neat resin drops more than five orders. Electrical resistivity of MWNT/Epon862 composites decreases with the increase of tube loading. Also, good tube dispersion can help the formation of electrical conductive network. Electrical resistivity of random BP composite is as low as 3.92E-2 §Ùcm. The low resistivity of BP composites, BP/Carbon fiber(CF)/Epon862 composite and MWNT/CF/SC79 multi-scale composite make them very good candidates for EMI shielding materials. / A Thesis Submitted to the Department of Industrial Engineering in Partial Fulfillment of the Requirements for the Degree of Master of Science. / Summer Semester, 2005. / July 8, 2005. / EMI, Conductivity, Buckypaper, Nanotube composites, Resistivity / Includes bibliographical references. / Zhiyong Liang, Professor Directing Thesis; James Simpson, Committee Member; Ben Wang, Committee Member; Chuck Zhang, Committee Member.

Many industrial experiments are conducted under various conditions which do not facilitate complete randomization of all the experimental factors. In response surface methodology whenever there are restrictions on randomization the experimental procedure usually follows the split plot design approach. Split plot designs are used when there are factors which are difficult or costly to change or adjust during an experiment. Split plot designs are currently generating renewed interest because of their usefulness and practical application in industrial settings. Despite the work accomplished through various research efforts, there is still a need to understand the optimality properties of these designs for second-order response surface models. This dissertation provides the development of an analytical approach for the computation of various optimality properties for the assessment of second-order split plot designs. The approach involves a thorough investigation of the impact of restricted randomization on the information matrix, which characterizes much of the relationship between the design points and the proposed response surface model for split plot designs. Several important insights are presented for the construction of second-order split plot designs. In addition, the analytical equations reported compute exact design optimality values and are more efficient than currently available methods. A particular feature of these analytical equations is that they are functions of the design parameters, radius and variance ratio. Further, a significant result is the ability to efficiently compute the exact value of the integrated prediction variance for both split plot designs and completely randomized designs. The functionality of the computational procedures presented provides easy evaluation of the impact of changes in the design structure and variance ratio on the optimality properties of second-order split plot designs. / A Dissertation Submitted to the Department of Industrial Engineering in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy. / Summer Semester, 2006. / July 6, 2006. / Integrated Variance G-optimality, Information Matrix, Split Plots, Design Optimality, Response Surface Designs / Includes bibliographical references. / James R. Simpson, Professor Directing Dissertation; Anuj Srivastava, Outside Committee Member; Peter A. Parker, Outside Committee Member; Joseph J. Pignatiello, Jr., Committee Member.

Carbon nanotubes have exceptional mechanical and functional properties and are considered by many as the most promising reinforcement for the next generation high performance nanostructured materials. Tubes dispersion and interfacial bonding are the most critical issues for developing carbon nanotube-reinforced composites. Considerable improvements in the dispersion and interfacial bonding of single-walled carbon nanotube (SWNT)/ epoxy nanocomposites were obtained through developing new fabricating techniques and functionalized SWNTs in this thesis research. By systematically investigating dispersion formulations, processing parameters, mechanical property tests and SEM observations, better tubes dispersion were achieved while dispersing nanotubes in a special dispersion system to form a stable solution and then mixing it with epoxy resin. Further functionalized nanotubes also improve the dispersion. Functionalization methods such as chopping, chopping-oxidizing, molecular wraping and directly oxidizing were used to create more active sites in the nanotubes for enhancing tube-resin interaction during processing and composite interface. Interfacial bonding improvement was studied through mechanical tests and SEM observations. The results shows that the storage modulus of nanocomposites is improved over the neat epoxy resin through better tubes dispersion, functionalized nanotubes as well as higher tubes loading. However, the addition of nanotubes resulted in the glass transition degradation of the resulting nanocomposites. This thesis research developed new methods of dispersion and fabrication of nanotube composites, which considerably enhances tube dispersion and mechanical properties of the resultant nanocomposites. The results also reveal that modified tubes using the selected functionalization methods can be used to improve both tube dispersion and interface bonding in the nanocomposites. / A Thesis Submitted to the Department of Industrial Engineering in Partial Fulfillment of the Requirements for the Degree of Master of Science. / Fall Semester, 2003. / November 10, 2003. / Composites, Functionalization, Carbon Nanotube / Includes bibliographical references. / Zhiyong Liang, Professor Directing Thesis; Ben Wang, Committee Member; Chuck Zhang, Committee Member.