This investigation was concerned with developing a procedure for using nonexpendable pattern materials in ceramic shell piece molds. Literature relating to this study indicated that nonexpendable materials, used in whole ceramic shell molds, had been limited to frozen mercury.

In the power generation sector, more specifically, the gas turbine industry, competition has forced the lead time-to-market for product advancements to be more important than ever. For design engineers, this means that product design iterations and final product development must be completed within both critical time windows and budgetary constraints. Therefore, two areas that have received significant attention in the research and in practice are: (1) rapid prototyping technology development, and (2) rapid prototyping technology selection. Rapid prototyping technology selection is the focus of this research. In practice, selecting the rapid prototyping method that is acceptable for a specific design application is a daunting task. With technological advancements in both rapid prototyping and conventional machining methods, it is difficult for both a novice design engineer as well as an experienced design engineer to decide not only what rapid prototyping method could be applicable, but also if a rapid prototyping method would even be advantageous over a more conventional machining method and where in the manufacturing process any of these processes would be utilized. This research proposes an expert system that assists a design engineer through the decision process relating to the investment casting of a superalloy gas turbine engine component. Investment casting is a well-known technique for the production of many superalloy gas turbine parts such as gas turbine blades and vanes. In fact, investment-cast turbine blades remain the state of the art in gas turbine blade design. The proposed automated expert system allows the engineer to effectively assess rapid prototyping iii opportunities for desired gas turbine blade application. The system serves as a starting point in presenting an engineer with commercially-available state-of-the-art rapid prototyping options, brief explanations of each option and the advantages and disadvantages of each option. It is not intended to suggest an optimal solution as there is not only one unique answer. For instance, cost and time factors vary depending upon the individual needs of a company at any particular time as well as existing strategic partnerships with particular foundries and vendors. The performance of the proposed expert system is assessed using two real-world case studies. The first case study shows how the expert system can advise the design engineer when suggesting rapid manufacturing in place of investment casting. The second case study shows how rapid prototyping can be used for creating part patterns for use within the investment casting process. The results from these case studies are telling in that their implementations potentially result in an 82 to 94% reduction in design decision lead time and a 92 to 97% cost savings.

This thesis presents an investigation on innovative approaches to agricultural management, addressing challenges in both viticulture and turfgrass management. The first topic of this thesis introduces the Adaptive Crop Load Estimation (ACLE) method, a deep learning-based grape counting approach designed to alleviate the need for extensive annotated datasets. By training the model on a limited set of images, this method demonstrates promising results in accurately estimating grape cluster counts across different zones in the vineyards, with an average Mean Absolute Error (MAE)/Root Mean Square Error (RMSE) of 0.86/0.66. The ACLE method aims to reduce the cost of deploying automated grape counting systems by minimizing manual image annotation efforts and enabling model reusability across different vineyards. The second topic of this thesis delves into the realm of Turfgrass management, recognizing its pivotal roles in environmental health and aesthetics. Focusing on the challenges posed by spot- based diseases, the study introduces the Spot Treatment Pathfinding and Scheduling (STPAS) method. This framework employs Unmanned Ground Vehicles (UGV) for targeted spot spraying, optimizing robot stops and trajectories based on varying scenarios such as different spot sizes and robot capabilities. The trajectory planner developed within STPAS utilizes GPS coordinates and the radius of affected areas to determine efficient stops and paths for autonomous vehicles. Comparative analysis on the developed simulators reveals that STPAS reduces the distance traveled and time taken for spot spraying by over 50% compared to conventional boom-based sprayers, thereby enhancing both economic and environmental sustainability in Turfgrass management practices. / Master of Science / This thesis explores solutions for improving agricultural practices, specifically focusing on grapevine cultivation and turfgrass management. The first part introduces a novel method called Adaptive Crop Load Estimation (ACLE), which employs deep learning to accurately count grape clusters in vineyards. Unlike traditional methods requiring extensive annotated data, ACLE demonstrates significant results with minimal training images, aiming to reduce the cost of automated grape counting systems and enhance their adaptability across various vineyards. In the second part, the thesis delves into development of planning algorithm for precision spot spraying. Addressing challenges posed by spot-based diseases, the study introduces the Spot Treatment Pathfinding and Scheduling (STPAS) method. This algorithm provides robot stops and optimizes routes based on different scenarios such as spot sizes and robot capabilities. Comparative analysis of the simulation results reveals that STPAS improves efficiency, reducing both the distance traveled and time taken for spot spraying compared to boom-based sprayers. This not only benefits economic considerations but also contributes to environmental sustainability in turfgrass management practices.

Site-specific irrigation decisions require information about variations in soil moisture within the rooting depth actively being used by the crop. Producers have been using soil moisture sensors to make irrigation decisions, and it has been shown that soil moisture sensors can reduce water usage without reducing yields. There are still unanswered questions on improving efficiency with soil moisture sensors based on density and location of sensors within a field. This three-year study uses sensors to evaluate the spatio-temporal variability of soil moisture across an 18-ha production field in a corn/soybean rotation. The IDW results show that when uniform irrigation applications are made to the field, fewer sensors that are placed in better locations throughout the field can be as useful as a densely gridded array of sensors. Although, if variable rate irrigation (VRI) is being used, a dense array could be used the first season to fine tune management zones.

Ruminants play an essential role in supplying nutrients to the global population. Despite notable advancements in the livestock industry, there is a rising demand for animal protein products and a pressing need for sustainable practices. Consequently, it is imperative to focus on improving efficiency and sustainability across the environmental, economic, and social dimensions of the livestock system. Precision livestock farming (PLF) technologies have emerged as a potential solution to enhance sustainability by integrating individual animal monitoring and automated control over animal productivity, environmental impacts, health, and welfare parameters. Although PLF holds promise for improving livestock management practices, its widespread adoption is hindered by challenges including the high costs associated with implementation, data ownership, and implementation across different environments. he overarching aim of this research was to investigate and propose solutions to the challenges that limit the extensive implementation of wearable technologies in livestock systems. The primary objective of the first study was to develop and assess the utility of an open-source, low-cost research wearable technology equipped with Bluetooth for monitoring ruminants in a confined setting. The study successfully demonstrated the functionality and cost-effectiveness of this technology and its potential for monitoring ruminants' behavior in research and practical applications. Building upon the success of the technology in intensive systems, the subsequent study focused on updating the wearable sensor for deployment in extensive systems. This was achieved by incorporating LoRa data transmission and enabling real-time monitoring of livestock location. The study effectively demonstrated the feasibility of the updated technology for real-time monitoring of livestock in extensive grazing systems. In continuation of testing the feasibility of sensors, the subsequent experiment aimed to assess the accuracy and precision of a low-cost wearable sensor photoplethysmography (PPG) sensor in monitoring heart-rate (HR) of sheep housed under high-temperature conditions. The results revealed poor accuracy and precision in detecting HR changes using the PPG sensor. Future studies should explore alternative sensor deployment methods and data analytics techniques to improve the accuracy of a PPG sensor in detecting HR in livestock animals. The follow-up study focuses on evaluating the suitability of a continuous glucose monitor (CGM) designed for humans in measuring interstitial glucose concentrations in sheep, as a potential replacement for traditional blood glucose measurements. The findings demonstrated great potential of CGM in detecting changes in glucose concentrations in sheep. However, the study`s limitations such as the small sample size, warranting further investigation with a larger sample size and potential standardization with laboratory analysis bore implementing CGMs as a replacement for traditional glucose measurement methods in research. The limited expansion of technology application in extensive livestock systems, in contrast to confined operations, can be attributed to challenges such as limited battery life and data transmission. To overcome these limitations, edge processing techniques which involve performing data processing, analytics, and decision-making closer to the data source, have been proposed as cost-effective strategy for enhancing the usability of inertial measurement unit systems (IMU) in monitoring grazing animal behavior. Therefore, the objective of the fifth study was to explore different classification techniques suitable for edge processing using an open-source IMU. Analysis of variances, logistic regression, support vector machine, and random forest were evaluated for classifying grazing, walking, standing, and lying behaviors. The random forest model achieved the highest accuracy (93%) in classifying grazing using 1-minute interval. Moreover, the algorithms were compared considering a periodic snapshot of data with intervals of 3 or 5 seconds, and interesting revealed no significant impact on algorithm accuracy on differentiating behavior of grazing cows using IMU systems. Heat stress has negative impacts on animal behavior, welfare, and productivity. While IMU systems have been used to detect behavioral changes in thermoneutral conditions, their effectiveness on heat-stressed animals remains unclear. The objective of the last study was to investigate changes in sheep behavior using a low-cost IMU and the influence of ambient temperature in the algorithms ability to classify behaviors. Eating, lying, standing and ruminating while standing and lying were classified during exposure to different ambient temperature patterns. The algorithm demonstrated acceptable accuracies in differentiating behaviors under thermoneutral conditions, but its performance was impaired when tested outside the thermal range. Future research should focus on developing algorithms that account for different environmental conditions to improve the accuracy of IMU in classifying animal behavior. Collectively, these investigations contribute to enhancing the applicability of technologies in livestock systems. / Doctor of Philosophy / The global population relies on ruminant animals, such as cattle and sheep, for essential nutrient. However, with the increasing demand for animal protein products, there is a growing need for sustainable practices in the livestock industry. Precision livestock farming (PLF) technologies have emerged as a potential solution to enhance sustainability by enabling individual animal monitoring. However, challenges such as data ownership and accessibility and high costs, impair its adoption. To overcome these challenges and enhance the applicability of wearable sensors in livestock systems, this research aimed to explore potential solutions. The objective of the first study was to develop and evaluate an open-source, low-cost wearable technology equipped with Bluetooth for monitoring ruminants in confined settings. The study successfully demonstrated the functionality and cost-effectiveness of this technology for monitoring ruminant behavior. Building up the success of the technology in intensive systems, the subsequent study focused on updating the wearable sensor for deployment in extensive systems. This was achieved by incorporating LoRa data transmission, enabling real-time monitoring of livestock. The study effectively demonstrated the feasibility of and potential of the updated technology for real-time monitoring in extensive livestock systems. Continuing with the feasibility testing of technologies, the next experiment aimed to assess the accuracy and precision of a low-cost photoplethysmography (PPG) sensor in monitoring heart rate (HR) in sheep housed under high-temperature conditions. Unfortunately, the results indicated poor accuracy and precision in detecting HR changes using the PPG sensor. Future studies should explore alternative sensor deployment methods and data analysis techniques to improve the accuracy of PPG sensors for HR monitoring in livestock animals. The followed study focused on evaluating the suitability of a continuous glucose monitors (CGM) designed for humans to measure interstitial glucose concentrations in sheep and potentially replacing traditional blood glucose measurements. The findings demonstrated the potential of CGMs to detect changes in glucose but limitations such as the small sample size suggest the need for further investigations with a larger sample size and potential standardization with laboratory analysis before implementing CGM as a replacement for traditional glucose measurement methods in research. In extensive systems, where technology adoption has been slower compared to confined operations, edge processing techniques are proposed as a cost-effective strategy to monitor grazing animal behavior using inertial measurement unit systems (IMU). In the fifth study, different classification techniques were explored using an open-source IMU, including analysis of variances, logistic regression, support vector machine, and random forest. The random forest model achieved high accuracy (93%) in classifying grazing behavior with a 1-minute interval. Surprisingly, algorithm accuracy was not affected when snapshot in time was performed. The final study focused on using a low-cost IMU to investigate sheep behavior under varying ambient temperature conditions. While algorithm performed well under thermoneutral conditions, its accuracy decreased outside the thermal range. Future research should focus on algorithms that account for different environmental conditions to improve IMU accuracy in classifying behavior. These investigations contribute to enhancing technology's applicability to in livestock systems by addressing challenges and developing practical solutions to improve livestock management and animal well-being.

The automobile industry has set the demand regarding Powder Metallurgy (PM) parts for decades, since this near-net shape technology is a cost-effective manufacturing process allying good mechanical properties with dimensional and geometrical precision. Aiming at the future of the electric automobiles high production and demand, many changes are on the way to guarantee the competitiveness of PM against other manufacturing process. The high costs of alloying elements such as Ni and Cu, the changes in health and safety regulations as well as light weighting of components are the topics of major importance in the field of PM and focus of main R&D around the globe. The use of high temperature sintering and different alloying elements are possible solutions to overcome properties obtained by using Ni as an alloying element sintered at conventional temperatures. Materials with Cr, Mo and Si were investigated using high temperature sintering (1180°C and 1250°) in comparison to traditionally high Ni materials sintered at conventional temperature (1120°C). The dimensional stability, geometrical precision, density, and microstructure of ring-shaped specimens were studied by using a coordinate measuring machine (CMM) and the effect of HTS on the mechanical properties were estimated through the fraction of the load bearing section. The effect of HTS on the dimensional precision and geometrical stability was later investigated in real parts manufactured by industrial partners through an EPMA Club Project. The 4%Ni material sintered at 1120°C was also compared to Ni-less/Ni-free materials sintered at 1250°C using tensile testing, impact testing, and hardness. The use of HTS to improve the mechanical properties without impairing the dimensional and geometrical stability was confirmed in parts with both low and high complexity designs. This project sets the blueprint for future material developments using HTS as manufacturing process.

Thesis (M.Sc.D.)--Boston University School of Graduate Dentistry, 1973. Prosthodontics. / Bibliography included. / Precision and semi-precision attachments have been utilized in fixed partial prostheses for many years. A review of the dental literature reveals a lack of clinical and laboratory information and their use in specific situations therefore must have been empiricly established . The purpose of this research was to contribute to the establishment of either a sound rationale for the use of precision attachments or, conversely, to an indictment of them based on factual testing under simulated clinical conditions. [TRUNCATED]

Maintaining cotton fiber quality is crucial for the survival of the U.S. cotton industry. Previous studies have indicated that spatial variability of fiber quality parameters exists in cotton fields. If site-specific measurement and prediction of quality is possible, then fiber could be segregated during the harvesting process, thus increasing the overall price a producer would receive for his crop. Because of the importance of fiber micronaire to the textile industry, the fact that micronaire exhibits moderate variation in the field, and the fact that it has been shown to be related to optical properties of cotton fibers, micronaire measurement was considered for quality segregation. Two years? cotton and soil data from two fields in Brooksville, Mississippi were used to investigate how much spatial variation in cotton quality factors could be explained by soil parameters. It was found that spatial variability exists in soil and cotton quality parameters, and micronaire (maturity and fineness) was found to have relatively large variability compared to other quality parameters. About 22 to 35% of the variation in micronaire could be explained by soil parameter variability. Site-specific prediction of micronaire based on only soil seems to be not practical according to the results of this study. Another objective was to develop a methodology for measuring important parameters of cotton crop quality in the field. USDA Micronaire Standard Calibration samples were used in infrared spectral measurements in order to relate their micronaire values to near-infrared and mid-infrared wavelength spectra. Near-infrared reflectance measurements in certain wavelengths ranging from 800 to 2500 nm were found to be closely correlated to micronaire values. Mid-infrared transmittance measurements (ratios) in certain wavelengths ranging from 2.5 to 25 µm were also related to micronaire values. The R2 value of the optimal prediction model was 0.92. This model was validated with HVI measurement of cotton samples from Mississippi and Arizona. Optical sensors based on spectral reflectance and transmittance measurements seem to be a reasonable choice for site-specific harvesting. A practical sensor mounted on cotton picker for measuring cotton micronaire appears to be feasible and a draft design was proposed.

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Current trends in agricultural equipment have led to an increasing degree of autonomy. As the state of the art progresses towards fully autonomous vehicles, it is important to consider assumptions implicit in the design of these vehicles. Current automation in harvesters have led to increased sensing and automation on current combines, but no published research examines the effect of machine size on the viability of the autonomous system. The question this thesis examines is: if a human is no longer required to operate an individual harvester, is it possible to build smaller equipment that is still economically viable? This thesis examines the appropriateness of automating these machines by developing a conceptual model for smaller, fully autonomous harvesters. This model includes the basic mechanical subsystems, a conceptual software design, and an economic model of the total cost of ownership. The result of this conceptual design and analysis is a greater understanding of the role of autonomy in harvest. By comparing machine size, machine function, and the costs to own and operate this equipment, design guidelines for future autonomous systems are better understood. It is possible to build an autonomous harvesting system that can compete with current technologies in both harvest speed and overall cost of ownership. / Master of Science