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Currently, process control in automation technology is mostly regulated by fixed process parameters as a compromise between several identically constructed systems or by plant operators, who are often guided by intuition based on decades of experience. Some operators are not able to pass on their knowledge to the next generation due to societal developments, e.g. academization or increased desire for self-actualization. In contrast, the vision of Smart Factories includes intelligent machining processes that should ultimately lead to self-optimization and adaptation to uncontrollable variables. To consistently implement this vision of self-optimizing machines, a defined quality criterion must be automatically monitored and act as a feedback for continual, autonomous and safe optimization. The term safe refers to the compliance with process quality standards, which must always be maintained. In a very conservative branch such as automation technology, no risks whatsoever are allowed through random experiments for data generation in production operations, since, for example, an unscheduled downtime leads to serious financial losses. Furthermore, machine-driven decisions may at no time pose a threat. Thus, decisions under uncertainty may only be taken where the amount of uncertainty can be considered uncritical. Additionally, industrial applications require a guaranteed real-time capability in terms of reaction to ensure that the actions can be taken in time whenever needed. Since economic aspects are often crucial for decisions in industry, necessary experiments under laboratory conditions, for example, should also be as avoidable as possible, while the effort required for integration into a field application should be as simple as possible.
The aim of this work is the scientific investigation of the integration of learning feedback
for intelligent decision making in the control of industrial processes. The successful integration enables data-driven process optimization. To get closer to the vision of self-optimizing machines, safe optimization methods for industrial applications on the process level are investigated and developed. Here, considering the given restrictions of the automation industry is critical. This work addresses several fields including technical, algorithmic and conceptual aspects. The algorithmic refinements are essential for enabling a wider use of safe optimization for industrial applications. They allow, e.g., the automatic handling of the majority of hyper-parameters and the solution of complex problems by increased computational efficiency. Furthermore, the trade-off between exploration and exploitation of safe optimization in high-dimensional spaces is improved. To account for changeable states perceived via sensor data, contextual Bayesian optimization is modified so that safety requirements are met and real-time capability is satisfied. A software application for industrial safe optimization is implemented within a real-time capable control to be able to interact with other software modules to reach an intelligent decision. Further contributions cover recommendations regarding technical requirements with focus on edge control devices and the conceptual inclusion of machine learning to industrial process control.
To emphasize the application relevance and feasibility of the presented concepts, real world lighthouse projects are realized in the course of this work, indented to reduce skepticism and thus initiate the breakthrough of self-optimizing machines.
Diese Bachelorarbeit behandelt die Fragestellung "Wie muss ein Accessibility Analysewerkzeug aufgebaut sein, dass es eine*n Webentwickler*innen darin unterstützt, Webseiten für Menschen mit Legasthenie zu entwickeln?". Dabei wird geklärt was Legasthenie ausmacht und welche Barrieren für Menschen mit Legasthenie auf Webseiten entstehen. Barrierefreiheit (Accessibility) kann durch den Einsatz von assistiver Technologie für einzeln Nutzer*innen gefördert werden. Der Fokus dieser Arbeit liegt bei den Analysewerkzeugen, welche die Barrierefreiheit einer Webseite für alle Nutzer*innen erhöhen könne, wenn Webentwickler*innen sie korrekt einsetzen. Die Hauptzielgruppe für dieses Analysewerkzeug sind Webentwickler*innen. Durch das Barrierefreiheitsstärkungsgesetz (BFSG) soll die Barrierefreiheit auf deutschen Webseiten in Zukunft gestärkt werden. Das BFSG basiert auf den Web Content Accessibility Guidelines (WCAG), welche in dieser Arbeit ebenfalls vorgestellt wurden.
Anhand von bestehenden Arbeiten, Leitfäden für die Gestaltung von Webseiten für Menschen mit Legasthenie sowie von vorhandenen Analysewerkzeugen wurde die bestehende Forschungslücke herausgearbeitet. Daraus ergaben sich die Anforderungen für einen Prototypen. Dieser Prototyp erhielt ein Konzept, welches den grafischen wie technologischen Aufbau aus der Fragestellung beantworten soll. In mehreren Spikes wurde herausgefunden welche Technologien für die Implementierung in Frage kommen. Anschließend fand die Implementierung einer Google Chrome Erweiterung statt, welche das Konzept umsetzte und die Anforderungen größtenteils befriedigte.