Working groups

Virtual & augmented reality

  • Description
    • A lot of learning factories are using VR & AR to show new applications in production and product development and to extend their didactical concept. In this working group different concepts and ideas to use VR & AR can be discussed.
  • Working Group Leader
    • Antonio Kreß (TU Darmstadt)
    • Thomas Riemann (TU Darmstadt)
  • Working Group Members
    • Christopher Prinz (RUB, Bochum)
    • Panagiotis Stavropoulos (LMS)
    • Patrick Herstätter (TU Graz)
    • Sri Kolla (University of Luxembourg)
    • Dimitris Mourtzis (LMS)

Energy and Resource Efficiency & Materials and bio-economy

  • Description
    • In order to decrease carbon emissions and address the problem of resource scarcity, producing goods energy and resource efficiently becomes more and more important in industry. Thus, the working group discusses how to properly address the topics of energy and resource efficiency and how to integrate it in learning and teaching environments.
    • In this working group 1) energy efficiency in learning factories; 2) KPIs and  tools to measure energy efficiency and 3) energy efficiency in production and logistics are discussed.
  • Working Group Leader
    • Astrid Weyand (TU Darmstadt)
    • Thomas Gries (RWTH Aachen)
  • Working Group Members
    • Atacan Ketenci (TU Graz)
    • Panagiotis Stavropoulos (LMS)
    • Jeff Mangers (University of Luxembourg)
    • Peter Plapper (University of Luxembourg)

Work-based learning

  • Description
    • Industry 4.0’s enabling technologies, especially AI and collaborative robotics, increasingly create skill mismatches on the labour market. Hence, efficient training and learning opportunities for manufacturing workforce is essential to close skill gaps and enable workers to factor into manufacturing transformation effectively. Emerging technologies also introduce new ways of work-based learning such as reciprocal learning. The working group of Work-based Learning (WbL) aims to foster synergies towards bringing new ideas into reality in learning- and smart factories.
  • Working Group Leader
    • Fazel Ansari (TU Wien)
  • Working Group Members
    • Sebastian Schlund (TU Wien)
    • Steffen Nixdorf (TU Wien)
    • Maximilian Papa (TU Wien)
    • Antonio Kreß (TU Darmstadt)
    • Sebastian Bardy (TU Darmstadt)
    • Maria Hulla (TU Graz)
    • Matthias Wolf (TU Graz)

Human Robot Collaboration

  • Description
    • Application of HRC, quick and easy integration of HRC to workstations, universal intergration of one Cobot in multiple workstations.
  • Working Group Leader
    • Bernd Kuhlenkötter (RUB, Bochum)
    • Michael Miro (RUB, Bochum)
  • Working Group Members
    • Panagiotis Stavropoulos (LMS)
    • Atal Kumar (University of Luxembourg)
    • Peter Plapper (University of Luxembourg)

AI for manufacturing systems / Artificial Intelligence in production processes

  • Description
    • Development and adaptation of AI algorithms for manufacturing systems
    • Process monitoring, -control and anomaly detection
    • Predictive quality and Zero-Defect-Manufacturing
    • Digital twins, simulation and process optimization
    • Predictive maintenance and condition monitoring
    • Application of different AI methods for the manufacturing environment: predictive maintenance, assistance systems, productions planing an controll, quality controll, shopfloor management, etc. Devopment of new KI-algorithms
  • Working Group Leader
    • Tobias Biegel (TU Darmstadt)
    • Nicolas Jourdan (TU Darmstadt)
  • Working Group Members
    • Bernd Kuhlenkötter (RUB, Bochum)
    • Christopher Prinz (RUB, Bochum)
    • Weimin Zhang (Tongji University)
    • Fazel Ansari (TU Wien)
    • Panagiotis Stavropoulos (LMS)
    • Dimitris Mourtzis (LMS)
    • Mohammad Abuabiah (University of Luxembourg)
    • Muneer Al Zubi (University of Luxembourg)

Digital assistance systems for manual and semi-automatic assembly

  • Description
    • Development and combination of methods for the identification of potentials,
    • Digital assistance and its combination with artificial intelligence: Identifying Use Cases
    • Potential quantification (depending on Use Cases)
    • Analysis and improvement of the (automated) creation of information material
    • New methods for analysing workstation, in order to finde a potential for assistance systems, development of new contextsensitive and adaptive assistance systems
    • The assistance of workers in production with cognitive and physical systems is currently studied in various learning factories. In this working group the concepts and ideas of worker assistance systems can be discussed.
  • Working Group Leader
    • Sophie Sandner (TU Darmstadt)
  • Working Group Members
    • Matthias Eder (TU Graz)
    • Bernd Kuhlenkötter (RUB, Bochum)
    • Christopher Prinz (RUB, Bochum)
    • Matthias Wolf (TU Graz)

5G in Learning Factories

  • Description
    • 5G is changing industrial production and offers great potential for the manufacturing industry. A lot of learning factories are carrying out research and application of 5G. In this working group, different 5G application scenarios focusing on training in learning factory are discussed and implemented including but not limited to intelligent manufacturing, plug-and-play application (IIoT) based on the fusion of 5G and AI, Business model, etc. Each unit of the working group contributes a specific 5G use case and gives the solution. For example, Tongji University has already formed a LF for manufacturing demo line with 5G, which focuses on mass data transmission and real-time monitoring of machines.
  • Working Group Leader
    • Weimin Zhang (Tongji University)
  • Working Group Members
    • Lukas Weiser (wbk)
    • Bernd Kuhlenkötter (RUB, Bochum)
    • Christopher Prinz (RUB, Bochum)
    • Muneer Al Zubi (University of Luxembourg)
    • Dimitris Mourtzis (LMS)

Cross Learning Factory Product Production System (CLFPPS)

  • Description
    • The aim of this working group is to develop a concept for a “Cross Learning Factory Product Production System (CLFPPS)” including Learning Factory teaching and training modules to foster the cross Learning Factory collaboration with a holistic consideration of the product design and creation processes in production networks. Therefore Learning Factories with complementary competency fields and infrastructures should collaborate to design and manufacture a common product in the sense of a distributed, industry-oriented production system. The IALF can benefit amongst others from an intensified collaboration in terms of joint learning modules for holistic, practice-oriented training going beyond the limits of existing joint courses between IALF members as the joint course of TU Darmstadt, Ruhr-University Bochum and Reutlingen University addressing competencies in the field of digitalization and Industry 4.0 from different perspectives. The developed concept will be transferred into a project proposal by the members of the Working Group to apply for funding also opening up the possibility to integrate industrial partners into the concept. Several years ago (2015) we already submitted a project proposal within the NIL-network in this field with the title “ExcEEd - Excellence in international product creation processes through networking European Learning Factories”  within the EU Erasmus+ / Knowledge Alliances program, unfortunately the proposal was not successful.
  • Working Group Leader
    • Jan Schuhmacher (ESB Reutlingen)
  • Working Group Members
    • Christopher Prinz (RUB, Bochum)
    • NN (RUB, Bochum)
    • Panagiotis Stavropoulos (LMS)
    • Antonio Kreß (TU Darmstadt)
    • Dimitris Mourtzis (LMS)

Learning in the Digital Transformation

  • Description
    • Digitalization is constantly increasing in the manufacturing industry. Besides technological knowledge also transformability and flexibility of workers are required. This working group deals with needed competencies in the digital transformation and how they can be taught and measured.
  • Working Group Leader
    • Maria Hulla (TU Graz)
  • Working Group Members
    • Christopher Prinz (RUB, Bochum)
    • Esra Öztürk (RUB, Bochum)

Learning Factory Design

  • Description
    • More and more companies and universities are using learning factories for training, teaching and in research. For this purpose, various design approaches have been developed in the past, which take into account the different aspects of learning factories. In this working group, we exchange our experiences in the design of learning factories. Based on this, we would like to work on developing our own design approach for future learning factory projects.
  • Working Group Leader
    • Antonio Kreß (TU Darmstadt)
  • Working Group Members
    • Thomas Riemann (TU Darmstadt)
    • Astrid Weyand (TU Darmstadt)
    • Maria Hulla (TU Graz)
    • Vera Hummel (ESB Reutlingen)
    • Rafiq Ahmad (University of Alberta)