Browsing by Author "Wells, Lee Jay"

Now showing 1 - 5 of 5
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    Advancing Manufacturing Quality Control Capabilities Through The Use Of In-Line High-Density Dimensional Data
    Wells, Lee Jay (Virginia Tech, 2014-01-15)
    Through recent advancements in high-density dimensional (HDD) measurement technologies, such as 3D laser scanners, data-sets consisting of an almost complete representation of a manufactured part's geometry can now be obtained. While HDD data measurement devices have traditionally been used in reverse engineering application, they are beginning to be applied as in-line measurement devices. Unfortunately, appropriate quality control (QC) techniques have yet to be developed to take full advantage of this new data-rich environment and for the most part rely on extracting discrete key product characteristics (KPCs) for analysis. In order to maximize the potential of HDD measurement technologies requires a new quality paradigm. Specifically, when presented with HDD data, quality should not only be assessed by discrete KPCs but should consider the entire part being produced, anything less results in valuable data being wasted. This dissertation addresses the need for adapting current techniques and developing new approaches for the use of HDD data in manufacturing systems to increase overall quality control (QC) capabilities. Specifically, this research effort focuses on the use of HDD data for 1) Developing a framework for self-correcting compliant assembly systems, 2) Using statistical process control to detect process shifts through part surfaces, and 3) Performing automated part inspection for non-feature based faults. The overarching goal of this research is to identify how HDD data can be used within these three research focus areas to increase QC capabilities while following the principles of the aforementioned new quality paradigm.
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    Advancing the Utility of Manufacturing Data for Modeling, Monitoring, and Securing Machining Processes
    Shafae, Mohammed Saeed Abuelmakarm (Virginia Tech, 2018-08-23)
    The growing adoption of smart manufacturing systems and its related technologies (e.g., embedded sensing, internet-of-things, cyber-physical systems, big data analytics, and cloud computing) is promising a paradigm shift in the manufacturing industry. Such systems enable extracting and exchanging actionable knowledge across the different entities of the manufacturing cyber-physical system and beyond. From a quality control perspective, this allows for more opportunities to realize proactive product design; real-time process monitoring, diagnosis, prognosis, and control; and better product quality characterization. However, a multitude of challenges are arising, with the growing adoption of smart manufacturing, including industrial data characterized by increasing volume, velocity, variety, and veracity, as well as the security of the manufacturing system in the presence of growing connectivity. Taking advantage of these emerging opportunities and tackling the upcoming challenges require creating novel quality control and data analytics methods, which not only push the boundaries of the current state-of-the-art research, but discover new ways to analyze the data and utilize it. One of the key pillars of smart manufacturing systems is real-time automated process monitoring, diagnosis, and control methods for process/product anomalies. For machining applications, traditionally, deterioration in quality measures may occur due to a variety of assignable causes of variation such as poor cutting tool replacement decisions and inappropriate choice cutting parameters. Additionally, due to increased connectivity in modern manufacturing systems, process/product anomalies intentionally induced through malicious cyber-attacks -- aiming at degrading the process performance and/or the part quality -- is becoming a growing concern in the manufacturing industry. Current methods for detecting and diagnosing traditional causes of anomalies are primarily lab-based and require experts to perform initial set-ups and continual fine-tuning, reducing the applicability in industrial shop-floor applications. As for efforts accounting for process/product anomalies due cyber-attacks, these efforts are in early stages. Therefore, more foundational research is needed to develop a clear understanding of this new type of cyber-attacks and their effects on machining processes, to ensure smart manufacturing security both on the cyber and the physical levels. With primary focus on machining processes, the overarching goal of this dissertation work is to explore new ways to expand the use and value of manufacturing data-driven methods for better applicability in industrial shop-floors and increased security of smart manufacturing systems. As a first step toward achieving this goal, the work in this dissertation focuses on adopting this goal in three distinct areas of interest: (1) Statistical Process Monitoring of Time-Between-Events Data (e.g., failure-time data); (2) Defending against Product-Oriented Cyber-Physical Attacks on Intelligent Machining Systems; and (3) Modeling Machining Process Data: Time Series vs. Spatial Point Cloud Data Structures.
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    Cyber-Physical Security for Advanced Manufacturing
    Desmit, Zachary James (Virginia Tech, 2018-01-16)
    The increased growth of cyber-physical systems, controlling multiple production processes within the manufacturing industry, has led to an industry susceptible to cyber-physical attacks. Differing from traditional cyber-attacks in their ability to alter the physical world, cyber-physical attacks have been increasing in number since the early 2000's. To combat and ultimately prevent the malicious intent of such attacks, the field of cyber-physical security was launched. Cyber-physical security efforts can be seen across many industries that employ cyber-physical systems but little work has been done to secure manufacturing systems. Through the completion of four research objectives, this work provides the foundation necessary to begin securing manufacturing systems from cyber-physical attacks. First, this work is motivated through the systematic review of literature surrounding the topic. This objective not only identifies and highlights the need for research efforts within the manufacturing industry, but also defines the research field. Second, a framework is developed to identify cyber-physical vulnerabilities within manufacturing systems. The framework is further developed into a tool allowing manufacturers to more easily identify the vulnerabilities that exist within their manufacturing systems. This tool will allow a manufacturer to utilize the developed framework and begin the steps necessary to secure the manufacturing industry. Finally, game theoretic models is applied to cyber-physical security in manufacturing to model the interactions between adversaries and defenders. The results of this work provide the manufacturing industry with the tools and motivation necessary to begin securing manufacturing facilities from malicious cyber-physical attacks and create a more resilient industry.
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    Monitoring and Prognostics for Broaching Processes by Integrating Process Knowledge
    Tian, Wenmeng (Virginia Tech, 2017-08-07)
    With the advancement of sensor technology and data processing capacities, various types of high volume data are available for process monitoring and prognostics in manufacturing systems. In a broaching process, a multi-toothed broaching tool removes material from the workpiece by sequential engagement and disengagement of multiple cutting edges. The quality of the final part, including the geometric integrity and surface finish, is highly dependent upon the broaching tool condition. Though there has been a considerable amount of research on tool condition monitoring and prognostics for various machining processes, the broaching process is unique in the following aspects: 1) a broaching process involves multiple cutting edges, which jointly contribute to the final part quality; 2) the resharpening and any other process adjustments to the tool can only be performed with the whole broaching tool or at least a whole segment of the tool replaced. The overarching goal of this research is to explore how engineering knowledge can be used to improve process monitoring and prognostics for a complex manufacturing process like broaching. This dissertation addresses the needs for developing new monitoring and prognostics approaches based on various types of data. Specifically, the research effort focuses on 1) the use of in-situ force profile data for real-time process monitoring and fault diagnosis, 2) degradation characterization for broaching processes on an individual component level based on image processing; and 3) system-level degradation modeling and remaining useful life prediction for broaching processes based on multiple images.
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    Quality Control Tools for Cyber-Physical Security of Production Systems
    Elhabashy, Ahmed Essam (Virginia Tech, 2019-01-15)
    With recent advancements in computer and network technologies, cyber-physical systems have become more susceptible to cyber-attacks; and production systems are no exception. Unlike traditional Information Technology (IT) systems, cyber-physical systems are not limited to attacks aimed at Intellectual Property (IP) theft, but also include attacks that maliciously affect the physical world. In manufacturing, such cyber-physical attacks can destroy equipment, force dimensional product changes, alter a product's mechanical characteristics, or endanger human lives. The manufacturing industry often relies on modern Quality Control (QC) tools to protect against quality losses, such as those that can occur from an attack. However, cyber-physical attacks can still be designed to avoid detection by traditional QC methods, which suggests a strong need for new and more robust QC tools. Such new tools should be able to prevent, or at least minimize, the effects of cyber-physical attacks on production systems. Unfortunately, little to no research has been done on using QC tools for cyber-physical security of production systems. Hence, the overarching goal of this work is to allow QC systems to be designed and used effectively as a second line of defense, when traditional cyber-security techniques fail and the production system is already breached. To this end, this work focuses on: 1) understanding the role of QC systems in cyber-physical attacks within manufacturing through developing a taxonomy encompassing the different layers involved; 2) identifying existing weaknesses in QC tools and exploring the effects of exploiting them by cyber-physical attacks; and 3) proposing more effective QC tools that can overcome existing weaknesses by introducing randomness to the tools, for better security against cyber-physical attacks in manufacturing.