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mediaTUM (Technische Univ. München)
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    173102 research outputs found

    Parallel-in-Time Integration with preCICE

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    Machine Learning / AI in Insurance: The Regulators' View.

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    A Knowledge-Augmented Socio-Technical Assistance System for Product Engineering

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    Manufacturing companies are exposed to increasingly complex products and shorter product engineering cycles. Unstructured data hinders the integration of knowledge over the different product engineering stages and complicates structured product development. However, combining an integrated view on relevant data sources following the Advanced Product Quality Planning (APQP) approach provides guidance for product engineers. In this paper, a semantic Knowledge Base (KB), a Process Execution System (PES), and a Computer Vision System (CVS) are introduced, which, in their interaction, compose a Socio-Technical Assistance System (STAS). We combine semantic models of production knowledge, APQP-guided product development, and ontology-based geometric representations of products and manufacturing resources. The PES coordinates the interaction with the user and other system components. The CVS tracks used tools and parts during the assembly and, therefore, enables traceability features and creates confidence in the quality of the assembly. As a result, the developed STAS prototype offers support from customer inquiry through product design and development to manufacturing and assembly, as well as after-sales support. The assistance system enables handling of complex products efficiently in order to reduce required times and costs

    Intuitive Instruction of Robot Systems: Semantic Integration of Standardized Skill Interfaces

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    This work aims at facilitating the integration of industrial robots and other devices such as their gripper tools at small and medium-sized enterprises (SMEs). For this purpose, an intuitive user interface for the skill-based instruction of robot systems is combined with standardized OPC UA-based skill interfaces that support various hardware and software resources from different manufacturers. Special emphasis is laid on supporting different user groups with varying levels of expertise. Production system engineers are provided with a detailed graphical user interface (GUI) for hierarchically defining new skills by combining preexisting ones. System operators receive a simplified view with limited complexity for process instruction and changing high-level task parameterizations. The skills and relevant semantic context knowledge about products, processes, and resources (PPR) are formally represented in OWL ontologies to enable hardware-agnostic process descriptions that can be deployed to different production environments, while automatically deriving parameterizations for skill invocations. The proposed concept has been qualitatively evaluated in two real-world robot workcells based on a smartphone accessory packaging use case

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    mediaTUM (Technische Univ. München)
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