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  1. AU="Gerald Berger"
  2. AU="Lenning, Ole Bernt"
  3. AU="Voetsch, Barbara"
  4. AU="Jakielska, Ewelina"
  5. AU="Sholl, Lynette"
  6. AU="Izquierdo, Inmaculada"
  7. AU="Miller, Andrew S"
  8. AU="Vincent-Levy-Frebault, V"
  9. AU="Willis, Zachary I"
  10. AU="Kruger, Eric S"
  11. AU="Ge, Shiyu"
  12. AU="Srivastava, Rajat"
  13. AU="Nemanja Vuksanovic"

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  1. Artikel ; Online: Industry 4.0 In-Line AI Quality Control of Plastic Injection Molded Parts

    Saeid Saeidi Aminabadi / Paul Tabatabai / Alexander Steiner / Dieter Paul Gruber / Walter Friesenbichler / Christoph Habersohn / Gerald Berger-Weber

    Polymers, Vol 14, Iss 3551, p

    2022  Band 3551

    Abstract: Automatic in-line process quality control plays a crucial role to enhance production efficiency in the injection molding industry. Industry 4.0 is leading the productivity and efficiency of companies to minimize scrap rates and strive for zero-defect ... ...

    Abstract Automatic in-line process quality control plays a crucial role to enhance production efficiency in the injection molding industry. Industry 4.0 is leading the productivity and efficiency of companies to minimize scrap rates and strive for zero-defect production, especially in the injection molding industry. In this study, a fully automated closed-loop injection molding (IM) setup with a communication platform via OPC UA was built in compliance with Industry 4.0. The setup included fully automated inline measurements, in-line data analysis, and an AI control system to set the new machine parameters via the OPC UA communication protocol. The surface quality of the injection molded parts was rated using the ResNet-18 convolutional neural network, which was trained on data gathered by a heuristic approach. Further, eight different machine learning models for predicting the part quality (weight, surface quality, and dimensional properties) and for predicting sensor data were trained using data from a variety of production information sources, including in-mold sensors, injection molding machine (IMM) sensors, ambient sensors, and inline product quality measurements. These models are the backbone of the AI control system, which is a heuristic model predictive control (MPC) method. This method was applied to find new sets of machine parameters during production to control the specified part quality feature. The control system and predictive models were successfully tested for two groups of quality features: Geometry control and surface quality control. Control parameters were limited to injection speed and holding pressure. Moreover, the geometry control was repeated with mold temperature as an additional control parameter.
    Schlagwörter injection molding of plastics ; closed-loop quality control ; in-line quality control ; AI quality control ; predictive control ; deep neural network ; Organic chemistry ; QD241-441
    Thema/Rubrik (Code) 670 ; 600
    Sprache Englisch
    Erscheinungsdatum 2022-08-01T00:00:00Z
    Verlag MDPI AG
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  2. Artikel ; Online: Bridging the science-policy gap

    Michal Sedlacko / Umberto Pisano / Gerald Berger / Katrin Lepuschitz

    Sustainability: Science, Practice, & Policy, Vol 9, Iss 2, Pp 105-

    development and reception of a joint research agenda on sustainable food consumption

    2013  Band 123

    Abstract: To increase the uptake of research findings by policy makers and to encourage European researchers to better reflect policy needs, we facilitated the development of a joint research agenda (JRA) on sustainable food consumption (SFC) involving scientists, ...

    Abstract To increase the uptake of research findings by policy makers and to encourage European researchers to better reflect policy needs, we facilitated the development of a joint research agenda (JRA) on sustainable food consumption (SFC) involving scientists, policy makers, and other stakeholders. Pursuing interpretive action research and using a number of data sources, we tried to understand how the “fit” between the characteristics of policy makers’ organizational contexts and the attributes of the JRA development process affects the reception of the JRA and its outcomes. Our framework was based on three distinct formations of discursive and material practices related to the use of knowledge in public policy making: bureaucratic, managerial, and communicative. Two dominant patterns seem to be represented in SFC consumption in the European Union: a transition between the bureaucratic and the managerial formation and a highly developed managerial formation with occasional communicative practices. We found that reflecting national policy priorities would help overcome some of the structural barriers between science and policy, whereas other barriers could be addressed by designing the process to better fit with the logics of the three formations, such as the fragmentation of knowledge (bureaucratic formation) or breadth of participation (communicative formation).
    Schlagwörter policy research ; workshops ; food consumption ; participatory planning ; stakeholders ; Social sciences (General) ; H1-99
    Thema/Rubrik (Code) 360 ; 320
    Sprache Englisch
    Erscheinungsdatum 2013-10-01T00:00:00Z
    Verlag Taylor & Francis Group
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

    Volltext online

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    Fernleihe an ZB MED

    Sie können sich den gewünschten Titel als lokale Nutzerin oder lokaler Nutzer von ZB MED direkt an den Standort Köln schicken lassen.

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