Process planning may be informally described as the preparation of process instructions for manufacturing parts. A fully automated process planning system should include strategies for the representation and acquisition of product data. Features are defined to be the fundamental entities which carry form and functional information for a certain engineering application. Presently, components may be designed by various modelling systems. IGES serves as the neutral representation of the boundary information of the CAD models for feature extraction and recognition. The goal of this feature reasoning scheme is to obtain a representation which will uniquely describe a feature in an explicit and/or implicit manner. By categorizing the geometric and topological characteristics of features, one may classify form features in a generic manner. This feature classification is application independent. The classification of functional features in a certain application domain can be also generalized. The domain of interest of the present work is assembly and sheet metal operations. A graph-based feature representation is developed and divided into two levels. The mono-feature representation level is used to represent the information of a single feature. Conversely, poly-feature representation level expresses the relationships among the features of an object. The representation scheme is designed as a communication protocol for transferring product information of objects in a CIM system. Additionally, standard features are defined and represented in the database.

Integrating Computer-Aided Design(CAD) and Computer-Aided Manufacturing(CAM) systems has been one of the major aims of research in automation for many years. However, a fully automated system has not been developed yet. Previous research indicate that an automated process planning system which possesses the capacity to reason about geometric properties of objects is an invaluable tool for overcoming many of the obstacles in integrating current CAD/CAM systems. During the past few years, people have proposed alternative methodologies for overcoming these obstacles. These alternative methodologies such as geometric coding systems or feature-based design systems have been widely utilized both in industry and academia. However, these methodologies merely offer an interface process and a storage mechanism. Human beings still perform the essential activity of geometric interpretation. In this research, a robust object recognition process is presented. Preparation of geometric data has been studied. Subsequently, a unique boundary representation scheme is developed to facilitate the recognition process. By using graph theory and search techniques from Artificial Intelligence, the recognition process can realize that two objects are identical or similar. Applications of the recognition process are also introduced. The recognition process can be employed to determine whether two features can be assembled. It can also be included in a geometric coding system to perform the task of searching for geometric data. Symmetry, an important geometric property of objects, can be determined automatically using the recognition process. In conclusion, methods of utilizing and expanding the object recognition algorithm are discussed.

The goal of this research is to develop a designer system which will be capable of evaluating the product for life-issues, capturing designer's intentions for a product, propagating the intentions to planning modules, and generating the process information which normally come from interpreting the geometry and designer intentions information for the product. This product modeler is developed for the domain of sheet metal products, and is restricted to non-multi-curved surface object. In order to accomplish this, principles, architecture and algorithms are studied and formulated to capture and propagate designer's intentions as product information and life-cycle issues as constraints in the design of sheet metal components. Evaluation tools based on these principles and algorithms will be provided as part of product modeling framework for sheet metal. The tools can include methods to capture design intentions which involve geometry; design evaluation and analysis where the design is measured against some variables which can be established by a design rule checker; design advisory where the suggestions are provided to the designer based on design analysis; and then synthesis of the product based on initial design and advice. This work will result in a scheme for mapping from features onto operations and tools required for the operation. The underlying purpose is to provide a stand-alone designer system for sheet metal domain.

Increasing uncertainty, such as Covid-19 and weather changes, threatens BCT (Basic Cadet Training) at ROKAFA (Republic of Korea Air Force Academy). In preparation for the changing and adverse environment, this study carried out the following steps to propose a system improvement utilizing LVC (Live, Virtual, Constructive) in BCT system at ROKAFA; 1) Researching the examples of BCT and LVC by advanced countries, 2) Analyzing BCT (ROKAFA) through systems approach (document analysis, survey of 140 cadets and officers, written interviews with military experts), 3) Applying LVC to BCT. BCT is a vital step in the transition from civilian to military status (ROKAFA, 2022), which has significant components such as the environment, its plan, the motivation of trainees, and training instructors. A review of BCT systems of the United States, Japan, the United Kingdom, and Sweden revealed that South Korea and the United States are the most similar. After analyzing BCT through the systems approach, it was identified that BCT has no alternative training despite time constraints (4 weeks). Besides, 87.9% of trainees and instructors experienced changes in BCT plan, and 4.86 ( > 4: Neutral) agreed with improvement in BCT. There was a significant correlation between negative emotions when changing training plans and the desire to improve BCT; the correlation coefficient (r) is 0.317 (p-value < 0.001). Respondents provided positive feedback ( > 4: Neutral) on nine of the 14 LVC training cases that would be applied to BCT, with Q25 (formerly Eagle training, not conducted since 2002) being ranked as the most needed training with a score of 4.74. Military experts advised considering the training development needed for Korea Air Force Academy cadets, system development through a long-term plan, and cost-effectiveness when applying LVC to BCT. The application of LVC in military training is inevitable because it is a proactive alternative training to prepare for the future and guide the way forward, and many advanced countries have already recognized the importance of LVC and are using it for the future military. In addition, LVC enables the accumulation of data to personalize the training data of Korea Air Force cadets even after enrollment. There are limitations of this study that need to be overcome, such as increasing the awareness of LVC among participants, performing Heuristic decision analysis when choosing alternative plans (Blanchard, 2014), and confirming the actual effect of LVC before and after its application.

This dissertation explores the application and examination of the Food Energy Water (FEW) nexus within the context of urban farms, with the goal of contributing fresh food supply to the local community. Every urban farm is connected to a community-scale microgrid (MG) that utilizes renewable energy sources to generate electricity for the local community. The microgrid (MG) aims to achieve environmental sustainability objectives by significantly reducing carbon emissions in comparison to conventional energy sources such as coal, natural gas, and crude oil. An Agent Based Model (ABM) is formulated to investigate the impacts of the different constituents through an analysis of societal, economic, and environmental sustainability measures. The model is employed to analyze the interplay among various agents, enabling a comprehensive comprehension of the synergistic effects and tradeoffs inherent in a Food Energy Water (FEW) nexus. Machine learning models that can provide input data such as crop prediction and electricity data are prepared and their potential integration within the ABM is discussed. The application of the framework on different case studies provided a qualitative connection between water, food, and energy in the community, quantified these connections, and identified critical links in the system. These findings enhanced our understanding of how the incorporation of renewable energy can influence food and water in communities. A mathematical programming model to optimize the FEW Nexus incorporating renewable energy has also been developed. The model addressed the supply-demand balance with existing and future FEW infrastructures. Depending on water-energy-food demand trend obtained from the ABM, the optimal way of matching FEW supply and demand over time is identified using indicators such as cost and carbon footprint.

No description available.

No description available.

No description available.

No description available.

No description available.