Federated Learning for Predictive Maintenance and Quality Inspection in Industrial Applications

Data-driven machine learning is playing a crucial role in the advancements of Industry 4.0, specifically in enhancing predictive maintenance and quality inspection. Federated learning (FL) enables multiple participants to develop a machine learning model without compromising the privacy and confidentiality of their data. In this paper, we evaluate the performance of different FL aggregation methods and compare them to central and local training approaches. Our study is based on four datasets with varying data distributions. The results indicate that the performance of FL is highly dependent on the data and its distribution among clients. In some scenarios, FL can be an effective alternative to traditional central or local training methods. Additionally, we introduce a new federated learning dataset from a real-world quality inspection setting

BOLD Vision 2020:Designing a vision for the future of Big Open Legal Data

The vision of openlaws.eu is to make access to justice easier for citizens, business- es and legal experts. For this purpose, an innovative legal information platform has been designed by the openlaws.eu project, considering the needs of various stakeholder groups as well as the latest developments in technology and our information society. Access to justice is a fundamental problem in the European Union. There are over 500 million citizens and over 21 million businesses who live, work and operate in 28 jurisdictions, written in 24 official languages. A common market cannot work without a legal system as a basis. Legal information is a public good and it is the duty of governments and the EU to inform citizens and business about the law. In a democracy and under the rule of law everybody should know legislation and case law – or at least have access to it. Legal tech is a new terms for new technology that can be applied to legal information in order to create better access and better understanding of the law. However, just because things can be done, does not mean automatically that they are done. Financial and organisational restrictions and the lack of competency can be a deal-breaker for innovation. Open data, open innovation and open source software can be a potential solution to this problem, especially when combined to one coherent ecosystem. openlaws.eu has developed a prototype platform upon these new open concepts. The application and implementation of some of the features of this innovative legal cloud service indicate where the road of “Big Open Legal Data” can lead us in the upcoming years. The project team envisages an environment, where a “social layer” is put on top of the existing “institutional layer”. Citizens, businesses and legal experts can actively collaborate on the basis of primary legislation and case law. Linked and aggregated legal data provide a solid basis. Such information can then be represented in traditional and more innovative ways. Text and data mining as well as legal intelligence help to process large amounts of legal information automatically, so that experts can focus on the more complicated questions. In the next five years more and more legal data will be opened up, not only because of the PSI Directive, but also because it is in the best interest of governments. As a result, we anticipate that more legal tech start-ups will emerge, as already happened during the past two years. They will apply innovative concepts and new technology on existing legal information and create better access to justice in the EU, in Member States and in the world

Methoden des maschinellen Lernens zur Analyse und Prognose von Ausfällen bei reaktiven Ionenätzprozessen in der Halbleiterindustrie

Arbeit an der Bibliothek noch nicht eingelangt - Daten nicht geprüftAbweichender Titel nach Übersetzung der Verfasserin/des VerfassersInstandhaltungsarbeiten sind ein wesentlicher Kostenfaktor in der Halbleiterproduktion. Insbesondere Plasmaätzgeräte erfordern aufgrund des reaktiven Prozesses und den hohen Anforderungen an die Prozessstabilität regelmäßige Wartungsarbeiten. Daten-getriebene Prognosemodell können helfen die Wartungskosten zu senken, indem der aktuelle Anlagenzustand und daraus der optimale Zeitpunkt für Reparaturen bestimmt wird. Daher wurde eine Pipeline für maschinelles Lernen zur Vorhersage von Ausfällen in Plasmaätzgeräten implementiert. Die verwendeten Daten stammen aus einer realen Produktionsumgebung und wurden über einen Zeitraum von 6 Monaten aufgezeichnet und enthalten Sensordaten wie Temperatur und Druck, Prozessdaten wie das verwendete Rezept sowie Wartungsdaten. Zunächst wurde eine umfassende explorative Datenanalyse durchgeführt, um informative Kennzahlen zu bestimmen. Zusammen mit Domain-Experten wurden die aggregierten Daten geclustert, visualisiert und interpretiert und Schlüsselmetriken definiert, die das historische Verhalten der Maschinen darstellen. Außerdem wurden mehrere TTF-Varianten (Time-to-Failure) eingeführt, die wir in den Experimenten verwenden. Wir vergleichen unsere Ergebnisse mit drei Benchmarks, die verschiedenen Wissensstufen entsprechen. Der realistische Benchmark ähnelt dem menschlichen Urteilsvermögen, der idealistische Benchmark erfordert Informationen über zukünftige Ausfälle und der naive Benchmark wird ohne Annahmen erstellt. Unsere Ergebnisse zeigen, dass datengesteuerte Modelle einen naiven und realistischen Benchmark übertreffen. Prozessdaten, die speziell von Domain-Experten überwacht werden, und die Kombinationen der verwendeten Rezepte sind die effektivsten Features. Die besten Gesamtergebnisse wurden mit einer Support Vector Machine erreicht. Unsere Experimente sind der erste Schritt zu einer umfassenden Strategie für die vorausschauende Wartung der Halbleiterindustrie. Sie bilden die Grundlage für ein Entscheidungsunterstützungssystem, das Ausfallvorhersagen mit der Verfügbarkeit von Wartungspersonal und Ersatzteilen, aktuellen Produktionszielen und den damit verbundenen Kosten kombiniert, um eine kosteneffiziente Handlungsempfehung zu treffen.Maintenance is a significant cost factor in semiconductor production. Especially plasma etching equipment requires regular maintenance due to the reactive process and high demand for process stability. Additionally, it is a bottleneck in semiconductors production and treated with special care. Data-driven predictive maintenance models could reduce costs as repairs are performed when indicated by the machine's data instead of a scheduled approach.We implemented a machine learning pipeline to predict outages in plasma etching equipment. We used data obtained in a real-world production environment for a period of 6-months. During this period, sensor data such as temperature and pressure, process data such as the used recipe, and maintenance data were recorded. First, we performed an extensive exploratory data analysis to create informative features. Together with domain experts, we clustered, visualized, and interpreted the aggregated data and defined key metrics representing the machines' historical behavior. Further, we introduced multiple TTF (Time-to-Failure) variants, which we use in the experiments. We compare our results to three benchmarks that represent three levels of knowledge. The realistic benchmark resembles human judgment, the idealistic benchmark requires information on future breakdowns, and the naive benchmark is created without any assumptions. Our results show that data-driven models outperform a naive and a realistic benchmark, which was derived to resemble human judgment. Process data that are specially monitored by domain experts and the combinations of used recipes are the most effective features, and a Support Vector Machine showed the best overall results. Our experiments are the first step towards a comprehensive predictive maintenance strategy for the semiconductor industry. They show the potential of predictive maintenance for plasma etching equipment and also outline data requirements for predictive maintenance to process engineers, maintenance personal, and persons who are responsible for implementing a long-term data science strategy. Our results provide a basis for a decision support system that combines predictions of failures with the availability of maintenance personnel and spare parts, current production goals, and the related costs to provide a cost-efficient recommendation.6

Atomistische Spindynamik

Abweichender Titel nach Übersetzung der Verfasserin/des VerfassersIm Rahmen dieser Arbeit wird eine GPU-beschleunigte Software zur Simulation von Spindynamik vorgestellt. Das Programm enthält einen Solver für die Landau-Lifshitz-Gilbert-Gleichung für ein atomistisches Spinmodell, das Dipol-Dipol-, Austausch-, Anisotropie- und Dzyaloshinskii-Moriya-Wechselwirkungen berücksichtigt und damit die Untersuchung magnetischer Skyrmionen ermöglicht. Weiters wird mittels einer Kontinuumsannahme des Magnetfeldes das mikromagnetische Modell eingeführt und in der Software implementiert. Die numerischen Methoden, die zur Lösung dieser Modelle benötigt werden, werden mit einem Schwerpunkt auf Finite-Differenzen-Methoden, Faltungen mittels Fourier-Transformationen und adaptive Zeitintegrationstechniken präsentiert. Darüber hinaus wird eine Implementierung der Stringmethode vorgestellt und zur Untersuchung der Annihilationsenergie magnetischer Skyrmionen verwendet.Within the scope of this thesis a GPU-accelerated software for the simulation of spin dynamics is presented. The program features a solver for the Landau{Lifshitz{Gilbert equation for an atomistic spin model considering dipole-dipole, exchange, anisotropy and Dzyaloshinskii-Moriya interactions thereby allowing for the investigation of magnetic skyrmions. By applying a continuum assumption, a micromagnetic description is derived and also implemented in the software. The discrete numerical methods needed for the solution of these models are discussed with a focus on nite difference methods, fast convolutions and adaptive time integration techniques. Furthermore, an implementation of the string method, a numerical method for the calculation of minimum energy paths in barrier-crossing events, is presented and used to investigate the annihilation energy of magnetic skyrmions.7