Fraunhofer-Institut für Produktionsanlagen und Konstruktionstechnik IPK

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  • Publication
    Stufenmodell zur Strukturierung digitaler Assistenzfunktionen: Digital assistierte Montageplanung
    ( 2020)
    Bußwinkel, L.
    ;
    Hauk, J.-C.
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    Schneider, H.
    ;
    Stark, R.
    Die Montageplanung ist durch große manuelle Aufwände sowie eine auf Erfahrungen und Expertenwissen basierende Entscheidungsfindung geprägt. Trotz zahlreicher Forschungsaktivitäten wird nur eine geringe Automatisierung der Montageplanung erreicht. Daher wird für die Montageplanung ein Assistenzstufenmodell erarbeitet, das zukünftig eine systematische Entwicklung digitaler Assistenzsysteme erlaubt, die in der industriellen Praxis einen Mehrwert erzeugen.
  • Publication
    An Ensemble Learning based Hierarchical Multi-label Classification Approach to Identify Impacts of Engineering Changes
    ( 2020)
    Pan, Y.
    ;
    Stark, R.
    In the process of complex products development, design decisions of products are constantly changed to improve quality or functionality, reduce costs, respond to statutory constraints or implement wishes from customers, with respect to new functionality. The changes on already released design decisions are known as Engineering Changes (EC). Due to interdependency, changes on one component may cause changes on another, and this will spread along the product structure. Therefore, to completely identify the affected components of engineering changes is a major challenge. This paper presents a novel approach to use properties of components as target variables and applying the predicted properties to locate the EC affected components in product structure. We create a hierarchy of the properties and divide the label space into separate communities. A stacked multi-label classifier is trained in each community, the result is obtained by union of assigned labels from different communities. Finally, the predicted labels are adjusted by incorporating the hierarchical relation. Experiments conducted on real-world industrial EC dataset with mixed data types. Results demonstrated that, the ensemble framework in our approach is more efficient and effective than our baseline models and has achieved superior performance on real industrial engineering change data.
  • Publication
    An optimal algorithm for the robotic assembly system design problem: An industrial case study
    ( 2020)
    Hagemann, S.
    ;
    Stark, R.
    The design process of flow-oriented assembly systems is characterized by being both highly complex and time consuming. Especially those design processes categorized into robotic and multi variant encountered in the automotive body-in-white production stages. Unlike established manual and template-based assembly system design models, which are currently applied in industry, the here presented novel approach uses a knowledge-based search algorithm and automatically generates optimal assembly system configurations. The algorithm has been implemented in a software prototype and the results have been validated against different large-size industrial scenarios in the automotive field of body-in-white production.
  • Publication
    Neural Network Hyperparameter Optimization for the Assisted Selection of Assembly Equipment
    ( 2019)
    Hagemann, S.
    ;
    Sünnetcioglu, A.
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    Fahse, T.
    ;
    Stark, R.
    The design of assembly systems has been mainly a manual task including activities such as gathering and analyzing product data, deriving the production process and assigning suitable manufacturing resources. Especially in the early phases of assembly system design in automotive industry, the complexity reaches a substantial level, caused by the increasing number of product variants and the decreased time to market. In order to mitigate the arising challenges, researchers are continuously developing novel methods to support the design of assembly systems. This paper presents an artificial intelligence system for assisting production engineers in the selection of suitable equipment for highly automated assembly systems.
  • Publication
    Leveraging circular economy through a methodology for smart service systems engineering
    ( 2019)
    Halstenberg, F.A.
    ;
    Lindow, K.
    ;
    Stark, R.
    Product Service Systems (PSS) and Smart Services are powerful means for deploying Circular Economy (CE) goals in industrial practices, through dematerialization, extension of product lifetime and efficiency increase by digitization. Within this article, approaches from PSS design, Smart Service design and Model-based Systems Engineering (MBSE) are combined to form a Methodology for Smart Service Architecture Definition (MESSIAH). First, analyses of present system modelling procedures and systems modelling notations in terms of their suitability for Smart Service development are presented. The results indicate that current notations and tools do not entirely fit the requirements of Smart Service development, but that they can be adapted in order to do so. The developed methodology includes a modelling language system, the MESSIAH Blueprinting framework, a systematic procedure and MESSIAH CE, which is specifically designed for addressing CE strategies and practices. The methodology was validated on the example of a Smart Sustainable Street Light System for Cycling Security (SHEILA). MESSIAH proved useful to help Smart Service design teams develop service-driven and robust Smart Services. By applying MESSIAH CE, a sustainable Smart Service, which addresses CE goals, has been developed.
  • Publication
    Approaching Knowledge Dynamics Across the Product Development Process with Methods of Social Research
    ( 2019)
    Wang, W.M.
    ;
    Mörike, F.
    ;
    Hergesell, J.
    ;
    Baur, N.
    ;
    Feufel, M.
    ;
    Stark, R.
    Knowledge is a crucial factor in state-of-the-art product development. It is often provided by stakeholders from divers disciplinary and individual backgrounds and has to be integrated to create competitive products. Still, it is not fully understood, how knowledge is generated, transformed, transferred and integrated in complex product development processes. To investigate the dynamic interrelations between involved stakeholders, applied knowledge types and related artefacts, researchers at the TU Berlin conducted and evaluated a student experiment to study basic phenomena of development projects. In relation to research methods and instruments applied in this experiment, various improvement opportunities were identified. In this paper, the experimental setting and its results are critically analysed from a social science perspective in order to generate improved research design. Based on the results of this analysis, a first set of methods and instruments from social sciences are identified that can be applied in further experiments. The goal is to develop a methodological toolbox that can be used to approach research on knowledge dynamics in product development.
  • Publication
    Introducing Product Service System Architectures for realizing Circular Economy
    ( 2019)
    Halstenberg, F.A.
    ;
    Stark, R.
    Product-Service Systems (PSS) as well as modular products can act as an enabler for Circular Economy (CE). Products and services have to be developed concurrently in order to be attuned properly. In product design, developers have to fulfil various requirements such as functional and cost targets. Integrating requirements regarding CE and developing products and services simultaneously makes their task even more complex and challenging. In concept design, the outline or rough concept of the product is defined. In order to develop functional PSS and to integrate CE goals in the stage of concept design, the authors propose Integrated Product Service Systems Architectures (IPSSAs), which depict physical product architectures and services architectures in one integrated model. This paper presents first findings on how IPSSAs can be realized. An analysis of different modelling notations was conducted and an exemplary application on a use case was performed. The findings lead to further research steps on the path to a method for modularizing PSS for CE.
  • Publication
    Development capabilities for smart products
    ( 2019)
    Tomiyama, T.
    ;
    Lutters, E.
    ;
    Stark, R.
    ;
    Abramovici, M.
    Smart products supported by new step-changing technologies, such as Internet of Things and artificial intelligence, are now emerging in the market. Smart products are cyber physical systems with services through Internet connection. For example, smart vehicles equipped with advanced embedded intelligence are connected to other vehicles, people, and environment, and offer innovative data-driven services. Since smart products are software-intensive, data-driven, and service-conscious, their development clearly needs new capabilities underpinned by advanced tools, methods, and models. This paper reviews the status and trends of these emerging development technologies such as model-based systems engineering and digital twin.
  • Publication
    Hybrid artificial intelligence system for the design of highly-automated production systems
    ( 2019)
    Hagemann, S.
    ;
    Sünnetcioglu, A.
    ;
    Stark, R.
    The automated design of production systems is a young field of research which has not been widely explored by industry nor research in recent decades. Currently, the effort spent in production system design is increasing significantly in automotive industry due to the number of product variants and product complexity. Intelligent methods can support engineers in repetitive tasks and give them more opportunity to focus on work which requires their core competencies. This paper presents a novel artificial intelligence methodology that automatically generates initial production system configurations based on real industrial scenarios in the automotive field of body-in-white production. The hybrid methodology reacts flexibly against data sets of different content and has been implemented in a software prototype.
  • Publication
    Lücken und Herausforderungen bei der praktischen Umsetzung des Model-Based Systems Engineering
    ( 2019)
    Fresemann, C.
    ;
    Stark, R.
    ;
    Sauer, C.
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    Schleich, B.
    ;
    Wartzack, S.