13 Zentrale Universitätseinrichtungen

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Start date: 2024Subject: search.filters.subject.670

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    Investigation of tool degradation during friction stir welding of hybrid aluminum-steel sheets in a combined butt and overlap joint
    (2024) Göbel, Robin; Schwertel, Stefanie; Weihe, Stefan; Werz, Martin
    Friction stir welding, as a solid-state welding technique, is especially suitable for effectively joining high-strength aluminum alloys, as well as for multi-material welds. This research investigates the friction stir welding of thin aluminum and steel sheets, an essential process in the production of hybrid tailor-welded blanks employed in deep drawing applications. Despite its proven advantages, the welding process exhibits variable outcomes concerning formability and joint strength when utilizing an H13 welding tool. To better understand these inconsistencies, multiple welds were performed in this study, joining 1 mm thick steel to 2 mm thick aluminum sheets, with a cumulative length of 7.65 m. The accumulation of material on the welding tool was documented through 3D scanning and weighing. The integrity of the resulting weld seam was analyzed through metallographic sections and X-ray imaging. It was found that the adhering material built up continuously around the tool pin over several welds totaling between 1.5 m and 2.5 m before ultimately detaching. This accretion of material notably affected the welding process, resulting in increased intermixing of steel particles within the aluminum matrix. This research provides detailed insights into the dynamics of friction stir welding in multi-material welds, particularly in the context of tool material interaction and its impact on weld quality.
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    A universal framework for skill-based cyber-physical production systems
    (2024) Hossfeld, Max; Wortmann, Andreas
    In the vision of smart manufacturing and Industry 4.0, it is vital to automate production processes. There is a significant gap in current practices, where the derivation of production processes from product data still heavily relies on human expertise, leading to inefficiencies and a shortage of skilled labor. This paper proposes a universal framework for skill-based cyber-physical production systems (CPPS) that formalizes production knowledge into machine-processable formats. Key contributions include a novel conceptual model for skill-based production processes and an automated method to derive production plans from high-level CPPS skills for production planning and execution. This framework aims to enhance smart manufacturing by enabling more efficient, transparent, and automated production planning, thereby addressing the critical gap in current manufacturing practices. The framework’s benefits include making production processes explainable, optimizing multi-criteria systems, and eliminating human biases in process selection. A case study illustrates the framework’s application, demonstrating its current capabilities and potential for modern manufacturing.