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Characterization and application of assistance systems in digital engineering

2021 , Stark, Rainer , Brandenburg, Elisabeth , Lindow, Kai

A broad range of assistance systems can be found in manufacturing practice as well as in the corresponding literature. Similarly, it can be observed that there is a growing need for and an increasing supply of assistance systems of all kinds. However, for digital manufacturing, the assistance systems are not clearly characterized. The diversity in application areas and possible uses varies and there are no possibilities for comparison. This paper addresses the topic of assistance systems and examines the various basic elements of engineering activities in terms of possible types of assistance systems based on research in manufacturing industry. Crucial aspects of assistance capabilities for engineering are elaborated and possible digital approaches are validated based on investigations in the field of aircraft engine design and assembly.

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Hybrid Prototype-in-the-Loop

2018 , Buchholz, Christian , Kind, Simon , Blume, Claas , Enin, Alexey , Stark, Rainer

In der Phase der früheren Anlagen- und Prozessabsicherung zeichnet sich ein Bedarf nach neuen Absicherungswerkzeugen für eine integrierte Betrachtung von Anlagenverhalten und Mensch-Maschinen-Kollaboration ab. Durch die Kopplung von virtuellen 3D-Modellen des Layouts, deren Verhaltensmodellen sowie mechatronischen Elementen werden mit dem Hybrid Prototype-in-the-Loop (HPiL)-Ansatzes das frühzeitige Absichern und Evaluieren von Montageprozessen ermöglicht. Im Rahmen einer Studie werden die Vor- sowie Nachteile des Ansatzes kritisch beleuchtet.

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Increasing resource efficiency with an engineering decision support system for comparison of product design variants

2019 , Buchert, Tom , Ko, Nathanael , Graf, Roberta , Vollmer, Thomas , Alkhayat, Moritz , Brandenburg, Elisabeth , Stark, Rainer , Klocke, Fritz , Leistner, Philip , Schleifenbaum, Johannes Henrich

The development of sustainable and resource-efficient products requires consideration of multiple design targets concerning the whole product life cycle. Taking these factors into account leads to complex decision situations with conflicting targets and trade-offs. To support design engineers in these situations an Engineering Decision Support System (EDSS) has been developed. In this article, the overall concept of the EDSS is presented. Furthermore, one function of the EDSS to assist a systematic comparison of product variants is introduced in detail. It is based on combining an existing PLM solutions (in particular Siemens Teamcenter 11/Siemens NX9) and software for Life Cycle Assessment (GaBi 7). Beyond a proof of concept for information exchange between both systems a methodology is presented which enables design engineers to systematically assess and select multiple product variants based on their resource utilization. The approach is complemented with a comprehensive case study for different design options of a core slide. In the scope of this study, variations of geometry (solid vs. hollow design), materials (hot-working steel vs. nickel-based superalloy) and manufacturing processes (laser metal deposition vs. milling) were considered. Furthermore, a usability study of the decision support tool is shown.

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Development and operation of Digital Twins for technical systems and services

2019 , Stark, Rainer , Fresemann, Carina , Lindow, Kai

Digital Twins are new solution elements to enable ongoing digital monitoring and active functional improvement of interconnected products, devices and machines. In addition, benefits of horizontal and vertical integration in manufacturing are targeted by the introduction of Digital Twins. Using the test environment of smart factory cells, this paper investigates methodological, technological, operative, and business aspects of developing and operating Digital Twins. The following Digital Twin dimensions are considered in scientific and application oriented analysis: (1) integration breadth, (2) connectivity modes, (3) update frequency, (4) CPS intelligence, (5) simulation capabilities, (6) digital model richness, (7) human interaction, and (8) product lifecycle. From this, design elements for the development of Digital Twins are derived and presented.