Fraunhofer-Publica

The Fraunhofer-Publica has been successfully documenting the research results of the Fraunhofer-Gesellschaft for over 30 years. The platform enables the collaborative linking of research-relevant objects and disseminates within the international scientific community.

The Fraunhofer-Publica thus fulfils its responsibility to promote the transfer of knowledge and know-how to industry and society.

Categories

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Research outputs

As an application-oriented research organisation, Fraunhofer aims to conduct highly innovative and solution-oriented research - for the benefit of society and to strengthen the German and European economy.

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Projects

Fraunhofer is tackling the current challenges facing industry head on. By pooling their expertise and involving industrial partners at an early stage, the Fraunhofer Institutes involved in the projects aim to turn original scientific ideas into marketable products as quickly as possible.

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Researchers

Scientific achievement and practical relevance are not opposites - at Fraunhofer they are mutually dependent. Thanks to the close organisational links between Fraunhofer Institutes and universities, science at Fraunhofer is conducted at an internationally first-class level.

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Institutes

The Fraunhofer-Gesellschaft is the leading organisation for applied research in Europe. Institutes and research facilities work under its umbrella at various locations throughout Germany.

Recent Additions

  • Publication
    Transfer of Logistics Optimizations to Material Flow Resource Optimizations using Quantum Computing
    ( 2024)
    Pfister, Raphael
    ;
    Schubert, Gunnar
    ;
    Kröll, Markus
    The complexity of industrial logistics and manufacturing processes increases constantly. As a key enabling technology of the upcoming decades, quantum computing is expected to play a crucial role in solving arising combinatorial optimization problems superior to traditional approaches. This study analyzes the current progress of quantum optimization applications in the logistics sector and aims to transfer an existing vehicle routing use case to a newly conceptualized matrix production use case regarding resource-efficient material flows. The simulation of the originating simple model is executed on a local circuit-based quantum simulator that emulates the behavior of real quantum hardware. Using a QAOA algorithm for problem-solving, optimal results have been achieved for all simulated scenarios. The theoretical material flow model is based on multiple assumptions and was created for testing reasons exclusively. For a realistic practical application, the model must therefore first be adapted and extended to include additional features.
  • Publication
    Method for the Derivation of Flexible Process Modules
    ( 2024)
    Berkhan, Patricia
    ;
    Ranke, Daniel
    ;
    Bauernhansl, Thomas
    Matrix production systems are modular, cycle time-independent, and flow-oriented production systems. They combine flexibility plus productivity and consist of flexibly linked and freely accessible process modules. The derivation and design of these process modules in flexible structures is still very time-consuming due to many degrees of freedom and limiting constraints. This paper presents a method for deriving flexible process modules, taking into account the creation of increased automation potentials, flexible order flows, and specialization in processes. The method consists of seven steps to derive harmonized process modules for multiple products. It is suitable for all manufacturing industries to reduce the planning effort. The defined process modules can be further used for layout planning.
  • Publication
    Investigating the Suitability of Time Series Classification Algorithms for Embedded Systems: A Case Study on Bicycle Pedaling Detection
    ( 2024)
    Oberle, Michael
    ;
    Gärtner, Sascha
    ;
    Maier, Florian
    In this paper, we investigate the performance of state-of-The-Art time series classification algorithms for pedaling detection in bicycles, focusing on embedded device implementation. Using accelerometer data from a crank-mounted sensor, we benchmark various algorithms, including Rocket, MiniRocket, CNN, LSTM, and HIVECOTEV2. The Rocket algorithm achieves the highest accuracy, followed by LSTM and CNN. However, considering the memory and complexity constraints of embedded devices, the CNN model emerges as the most suitable option. Surprisingly, MiniRocket underperforms in classifying backward pedaling as a non-pedaling state, warranting further investigation. Our findings contribute valuable insights into the applicability of time series classification algorithms for pedaling detection, paving the way for advancements in user assistance systems for e-bikes and mountain bikes.

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