314 research outputs found

    Digital Twins of production systems - Automated validation and update of material flow simulation models with real data

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    Um eine gute Wirtschaftlichkeit und Nachhaltigkeit zu erzielen, müssen Produktionssysteme über lange Zeiträume mit einer hohen Produktivität betrieben werden. Dies stellt produzierende Unternehmen insbesondere in Zeiten gesteigerter Volatilität, die z.B. durch technologische Umbrüche in der Mobilität, sowie politischen und gesellschaftlichen Wandel ausgelöst wird, vor große Herausforderungen, da sich die Anforderungen an das Produktionssystem ständig verändern. Die Frequenz von notwendigen Anpassungsentscheidungen und folgenden Optimierungsmaßnahmen steigt, sodass der Bedarf nach Bewertungsmöglichkeiten von Szenarien und möglichen Systemkonfigurationen zunimmt. Ein mächtiges Werkzeug hierzu ist die Materialflusssimulation, deren Einsatz aktuell jedoch durch ihre aufwändige manuelle Erstellung und ihre zeitlich begrenzte, projektbasierte Nutzung eingeschränkt wird. Einer längerfristigen, lebenszyklusbegleitenden Nutzung steht momentan die arbeitsintensive Pflege des Simulationsmodells, d.h. die manuelle Anpassung des Modells bei Veränderungen am Realsystem, im Wege. Das Ziel der vorliegenden Arbeit ist die Entwicklung und Umsetzung eines Konzeptes inkl. der benötigten Methoden, die Pflege und Anpassung des Simulationsmodells an die Realität zu automatisieren. Hierzu werden die zur Verfügung stehenden Realdaten genutzt, die aufgrund von Trends wie Industrie 4.0 und allgemeiner Digitalisierung verstärkt vorliegen. Die verfolgte Vision der Arbeit ist ein Digitaler Zwilling des Produktionssystems, der durch den Dateninput zu jedem Zeitpunkt ein realitätsnahes Abbild des Systems darstellt und zur realistischen Bewertung von Szenarien verwendet werden kann. Hierfür wurde das benötigte Gesamtkonzept entworfen und die Mechanismen zur automatischen Validierung und Aktualisierung des Modells entwickelt. Im Fokus standen dabei unter anderem die Entwicklung von Algorithmen zur Erkennung von Veränderungen in der Struktur und den Abläufen im Produktionssystem, sowie die Untersuchung des Einflusses der zur Verfügung stehenden Daten. Die entwickelten Komponenten konnten an einem realen Anwendungsfall der Robert Bosch GmbH erfolgreich eingesetzt werden und führten zu einer Steigerung der Realitätsnähe des Digitalen Zwillings, der erfolgreich zur Produktionsplanung und -optimierung eingesetzt werden konnte. Das Potential von Lokalisierungsdaten für die Erstellung von Digitalen Zwillingen von Produktionssystem konnte anhand der Versuchsumgebung der Lernfabrik des wbk Instituts für Produktionstechnik demonstriert werden

    Fuzzing software with deep learning

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    Generation based fuzz testing can uncover various bug classes and security vulnerabilities. However, compared to mutation based fuzz testing it takes a great amount of time to develop a well balanced generator that generates good test cases and decides were to break the underlying structure to exercise new code paths. This thesis provides an evaluation of generative deep learning algorithms to generate HTML test cases to fuzz test a browser’s HTML rendering engine. The experiments highlight that various deep learning algorithm are performing well in this setting. However, there are large differences in the stability of the training and code coverage performance. The best performing in terms of code coverage as well as training stability is a Temporal Convolutional Network (TCN). The TCN model is then also used to learn from real world HTML data to generate novel test cases withouth the need of a generative fuzzer in the first place. The results show that the approach is able to discover new code areas that were neither discovered by the underlying fuzzer nor the prior models. Furthermore, this highlights how an existing fuzzer can be augmented with the help of a deep learning model and publicly available training data. Finally, reinforcement learning is used to further improve the existing fuzzer by utilizing the code coverage data from the browser under test. The designed DDQN agent is able to guide the test case creation of a TCN to even outperform the underlying baseline test case generator

    Operational Research: methods and applications

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    This is the final version. Available on open access from Taylor & Francis via the DOI in this recordThroughout its history, Operational Research has evolved to include methods, models and algorithms that have been applied to a wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first summarises the up-to-date knowledge and provides an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion and used as a point of reference by a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes

    Operational research:methods and applications

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    Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order

    Development of Novel Nano Platforms and Machine Learning Approaches for Raman Spectroscopy

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    In Raman spectroscopy, data analysis occupies a large amount of time and effort; thus, it is paramount to have the proper tools to extract the most meaning from the Raman analysis. This thesis explores improved ways to analyse Raman data mostly by using machine learning techniques available in Python. The substrate used throughout this thesis has been patterned through an electrohydrodynamic process that patterns micrometric pillars onto the substrate, which, after being gold coated, can generate surface-enhanced Raman scattering. An initial theoretical background was laid for the electrohydrodynamic process and additional observations regarding the fluid mechanics. Furthermore, when the structures are fabricated, and Raman measurements are taken, we show that it is possible to create an effective convolutional neural networks that systematically evaluate these patterns’ surface morphology and extracts features responsible for the surface-enhanced Raman scattering phenomenon. Being able to predict 90% of the time from optical microscope images and 99% of the time with atomic force microscopy images Additionally, a thorough machine learning analysis of the Raman literature was done. The best machine learning algorithms were put together into a script combined with a graphical user Interface that can run multiple commands such as principal component analysis and self-organizing maps, all in a centralised way. This way, we managed to consistently extract information from Raman and surface-enhanced Raman scattering spectra to open possibilities for precise peak analysis methods using a multi-Lorentzian fit algorithm

    20. ASIM Fachtagung Simulation in Produktion und Logistik 2023

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    Operational Research: Methods and Applications

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    Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes

    Operational Research: Methods and Applications

    Get PDF
    Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order

    Digitization of the work environment for sustainable production

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    Global pandemics, devastating wars and natural disasters with increasing frequency and impact are disrupting previously carefully balanced manufacturing networks. All industrial companies are required to examine their operations and adjust accordingly. The increasing cost of resources require enterprises to re-design their value creation processes to be more sustainable, to optimize the supplier network to become more resilient and to accelerate digitizing of operations to enhance operational effectiveness. This year's WGAB research seminar is themed around Digitization of the work environment for sustainable production and seeks to contribute solutions to the current challenges. The scientific discourse aims to advance the sustainable and data-based organization of value creation processes. Exemplary efforts for the sustainable production of 3D printed footwear and the circular supply chain of energy production will be discussed. With advances in sensory data collection in cyber-physical production systems (CPPS), there are new opportunities for sensing the status of manufacturing systems, which enable advanced data analytics to contribute to a sustainable production. Intelligent processes enable sustainable value creation and bi-directional knowledge exchange between humans and machines. With people at the centre of the CPPS, production systems shall be both adaptive and personalized for every worker. People need to be involved in the technological and organizational changes. Simulating the migration from a linear economy to a circular economy supports the trend of regionalized production networks. Digital assistance systems are tested to back up resilient manufacturing. We would like to thank all authors for their efforts in preparing the contributions, which are valuable inputs to the discourse to solve the current challenges

    IMPLEMENTING CONDITION-BASED MAINTENANCE PLUS AS A GROUND MAINTENANCE STRATEGY IN THE MARINE CORPS

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    In 2020, Marine Corps Order 4151.22 and Commandant White Letter 2–20 was published to implement Condition-Based Maintenance Plus (CBM+) as a ground maintenance strategy to improve operational availability and reduce life-cycle costs. The Fleet Marine Force is still operating under preventative and corrective maintenance strategies instead of CBM+ strategies. Organizational inertia, such as competing priorities, legacy processes, and inspections, has slowed the integration of CBM+ strategies. We reviewed key policy documents and interviewed fifteen subject-matter experts relevant to Marine Corps ground transport maintenance policies and practices. Based on this information, we conducted a thematic analysis using an organizational change approach to identify barriers and opportunities that impact CBM+ implementation. We found that immediate gains from CBM+ implementation in the Marine Corps can be achieved through a focus on people and process improvements while technology integration continues. The CBM+ strategy supports Force Design 2030 and Talent Management 2030 objectives and emphasizing this alignment can build momentum for CBM+. In this paper, we make six specific recommendations that apply organizational change concepts to enable effective CBM+ implementation as a ground maintenance strategy in the Marine Corps.NPS Naval Research ProgramThis project was funded in part by the NPS Naval Research Program.Major, United States Marine CorpsMajor, United States Marine CorpsApproved for public release. Distribution is unlimited
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