Qualitative uncertainties are a key challenge for the further industrialization of additive manufacturing. To solve this challenge, methods for measuring the process states and properties of parts during additive manufacturing are essential. The subject of this review is in-situ process monitoring for material extrusion additive manufacturing. The objectives are, first, to quantify the research activity on this topic, second, to analyze the utilized technologies, and finally, to identify research gaps. Various databases were systematically searched for relevant publications and a total of 221 publications were analyzed in detail. The study demonstrated that the research activity in this field has been gaining importance. Numerous sensor technologies and analysis algorithms have been identified. Nonetheless, research gaps exist in topics such as optimized monitoring systems for industrial material extrusion facilities, inspection capabilities for additional quality characteristics, and standardization aspects. This literature review is the first to address process monitoring for material extrusion using a systematic and comprehensive approach.

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Kurzfassung Methoden zur Qualitätssicherung sind ein zentraler Erfolgsfaktor für die weitere Industrialisierung der additiven Fertigung. In diesem Beitrag wird ein Ansatz für ein optisches Prüfsystem vorgestellt, welches die Prozessgüte bei der additiven Materialextrusion schichtweise während der Herstellung überwacht. Die Prüfaufgabe wird analysiert, Hardwarekomponenten für die Datenerfassung werden konzeptioniert und ein erster Schritt zur texturanalytischen Fehlerdetektion wird vorgestellt.

Limited visibility during the operation of forklifts is one of the most significant sources of danger in in-plant material handling. Existing systems record concealed areas via cameras and display them directly on monitors in the driver's cab. Augmented reality (AR) allows to display information directly in the driver's field of view (FoV). Concealed areas could thus be overlaid with the camera images, while nothing is displayed in areas with a clear view, allowing the real environment to be perceived. This research aims to demonstrate the suitability of AR as an assistance system for forklifts. The assistance system combines various sensors for pose determination, RGB-D cameras for environment recording and a head-mounted display (HMD) for visualization. We define requirements from a safety perspective and classify our system with respect to these requirements. The results show the suitability of AR technology as a driver assistance system. At the same time, they show challenges in accuracy and latency.

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