Fraunhofer-Institut für Produktionsanlagen und Konstruktionstechnik IPK

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  • Publication
    Conception and Requirements Identification of Gaia-X-Based Service Offerings
    ( 2023-08-10)
    Tanrikulu, Cansu
    ;
    Gogineni, Sonika
    ;
    Kondak, Konstantin
    ;
    Lindow, Kai
    Gaia-X is an initiative to develop the next generation of a secure and federated European data infrastructure to promote digital sovereignty for data exchange to fuel innovations. This paper introduces the basics of Gaia-X, in particular the mobility domain, followed by the federated system and its standards. In addition, a research methodology is presented to help conceptualize and derive requirements for service offerings on a Gaia-X-based data space. This is elaborated with a use case from the project "GAIA-X4 AMS" (Gaia-X4-Advanced Mobility Services), which reflects implementation in Gaia-X ecosystems and its added value.
  • Publication
    Digital Twin for Circular Economy
    ( 2023-05)
    Mügge, Janine
    ;
    Riedelsheimer, Theresa
    ;
    Lindow, Kai
    Digital twins offer a promising approach to sustainable value creation by providing a specific data base for the monitoring and execution of circular economy strategies. By analyzing product, component and material as well as process data, it is possible to create transparency throughout a products lifecycle and address current challenges such as climate change and resource scarcity. The concept of a digital twin for circular economy enables to build a data-driven ecosystem and supports new business and value creation models from SMEs to large enterprises. This paper identifies application scenarios, their technological readiness level and challenges of digital twins for circular economy in the manufacturing industry based on a systematic literature review. As a second result, a generic concept of a digital twin for circular economy is presented.
  • Publication
    Digital Twins for Sustainability in the Context of Biological Transformation
    ( 2023)
    Seegrün, Anne
    ;
    Mügge, Janine
    ;
    Riedelsheimer, Theresa
    ;
    Lindow, Kai
    Applying biological principles that are similar to those found in nature to product engineering and manufacturing offers new approaches to product and production systems and might lead to a significant contribution towards sustainability. By transferring materials, structures, and processes of natural to digital ecosystems industrial value creation can be optimized. A promising approach to establish a networked, self-regulating digital ecosystem represents a digital twin. The potential of digital twins within the context of biological transformation has not been researched yet. This paper attempts to provide a first entry into the research topic by identifying biological principles within the concept of a digital twin and analyzing its potential for biological transformation in the industry. As a main result, the paper presents a list of relevant principles of biological transformation based on a structured taxonomy. These are specified within the concept of a digital twin.
  • Publication
    Taxonomy for Biological Transformation Principles in the Manufacturing Industry
    ( 2023)
    Berkhahn, Magda
    ;
    Kremer, Gerald
    ;
    Riedelsheimer, Theresa
    ;
    Lindow, Kai
    ;
    Stark, Rainer
    Industry and research are seeking answers to current demands in industrial value creation, like resilience of production, sufficient product quality and sustainability of products and processes. A novel line of thought, seeking the accomplishment of those is the Biological Transformation (BT). BT describes the interweaving of biological modes of action, materials and organisms with engineering and information sciences. The conflation of disciplines from natural, technical and social sciences yields in a heterogeneous field of activities with ambiguous technical terms. An ascertainment of principles of BT is required to classify yet undifferentiated patterns in nature-based production, facilitating their systematic implementation in aiming for sustained solutions on current challenges. With increasing research in biomimetic, attempts arise to capture nature‑based activities in manufacturing through schematic classifications. Yet, basic semantics representing the effective principles of BT in the manufacturing industry is lacking. The goal of this publication is to introduce a taxonomy of Biological Transformation in manufacturing based on its core principles Bio Inspiration, Bio Integration and Bio Interaction. Within the research project BioFusion 4.0, the taxonomy was developed and applied to classify technology innovations. The paper presents the taxonomy, its development and application in use cases.
  • Publication
    Considering LCA in System Architectures of Smart-Circular PSS
    ( 2023)
    Kruschke, Thomas
    ;
    Riedelsheimer, Theresa
    ;
    Lindow, Kai
    The realization of smart-circular Product-Service Systems has theoretically promising advantages compared to traditional products. Nevertheless, the sustainability improvement, especially for the ecological dimension is not yet satisfactorily proved. In this paper, the authors examined the current state of research within a systematic literature review with a specific focus on the overlap of the topics: Life Cycle Assessment, Model-Based Systems Engineering, Product-Service Systems, and Circular Economy. The aim is to analyze the potential of a proactive quantification of the ecological impact in an early stage during the development of smart-circular PSS - the system architecture definition. As a result of the systematic review, 27 relevant papers were identified and analyzed and the findings are presented in a structured way. The main finding is that the current state of the art in this research field still is in the conceptualization stage. In addition, a proactive approach is rare and circularity is not considered to its fullest. Quantified use cases do not draw the system boundaries Cradle-to-Cradle and not every of the 9R-strategies is considered. Furthermore, the potentials and challenges of the revealed research gap are summarized.
  • Publication
    Kann Systems Engineering eine nachhaltige Produktentwicklung unterstützen
    ( 2022)
    Kruschke, Thomas
    ;
    Kaufmann, Uwe
    ;
    Lindow, Kai
    Systems Engineering (SE) wird erfolgreich für die Entwicklung komplexer Sys-teme eingesetzt. Komplexität äußert sich vor Allem in der Berücksichtigung der Vielzahl von Entwicklungszielen und der Wechselwirkung von Einflussparame-tern in einem multikriteriellen Entwicklungsprozess für Produkt-Service-Umgebungen. Es ist daher naheliegend, die vielversprechenden Vorteile des SE für die Entwicklung nachhaltiger Produkte zu nutzen. In diesem Konferenzbeitrag werden die Herausforderungen zur Umsetzung der Nachhaltigkeitsstrategien beschrieben, es wird auf die Vorteile von Modell-Basierten Systems Engineering (MBSE) eingegangen sowie der theoretische Hintergrund und die Aufgaben der neuen Arbeitsgruppe Sustainability enabled by Systems Engineering der GfSE vorgestellt. Deren Zielstellung ist es, den Beitrag zu identifizieren, den SE und insbesondere das MBSE für eine nachhaltige Produktentwicklung leisten kann. Dafür wurden insgesamt 13 Fragen formuliert und in einer Roadmap eingeordnet, mit denen sich die Arbeitsgruppe in den nächsten Jahren beschäftigen wird.
  • Publication
    Applying Contextualization for Data-Driven Transformation in Manufacturing
    ( 2020)
    Gogineni, Sonika
    ;
    Lindow, Kai
    ;
    Nickel, Jonas
    ;
    Stark, Rainer
    Manufacturing is highly distributed and involves a multitude of heterogeneous information sources. In addition, Production systems are increasingly interconnected, hence leading to an increase in heterogeneous data sources. At present, data available from these new type of systems are growing faster than the ability to productively integrate them into engineering and production value chains of companies. Known applications such as predictive maintenance and manufacturing equipment management are currently being continuously optimized. While these applications are designed to help companies manage their manufacturing and engineering data, they only use a fraction of the total potential that can be realized by linking manufacturing and engineering data with other enterprise data. In the future, the context in which the data can be set will play an essential role. A meaningful added value in manufacturing can be achieved only with context specific data. Against this background, this paper presents three main areas of application for contextualizing data (semantics, sensitivity and visualization) and explains these applications with the help of a contextualization architecture. The concept is also evaluated using an industrial example. Furthermore, the paper describes the theoretical background of contextualization and its application in industry. The major challenges of the ability of engineers to adapt their activities and the integration of process knowledge for semantic linking are addressed as well.
  • Publication
    Development of an Industrial Internet of Things Ontologies System
    ( 2020)
    Fedotova, Alena V.
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    Lindow, Kai
    ;
    Norbach, Alexander
    The work is aimed at researching and developing an innovative subject field Industrial Internet of Things. The article deals with the system of the structure of the Industrial Internet of Things, the relationship between the objects of structure and the method of knowledge visualization with the help of ontological modeling of systems. Using the ontological approach allows us to provide support for design management, as well as further improvement of the complex technical system under consideration. The paper presents the ontology of the Industrial Internet of Things.
  • Publication
    Use of Digital Twins in Additive Manufacturing Development and Production
    ( 2019)
    Bergmann, André
    ;
    Lindow, Kai
    The megatrend of the digitization of the industry is picking up speed. Today, the digital twin is an important component in the strategic positioning of a manufacturing company. The Gartner Report predicts that more than 50% of large industrial companies will be using the digital twin and that the effectiveness of the companies can be increased by up to 10% by 2021. For this, it is necessary on the one hand that the products are equipped with sensors, in order to be able to provide the data for the digital twin. On the other hand, it is also necessary to be capable to evaluate the data unambiguously with regard to the products and to be able to initiate appropriate measures to control them. In addition, insights can be gained into the improvement of subsequent product generations and their production. The virtual representation of the product over its lifecycle requires a coupling with the real environment, in which lifecycle data are recorded via sensory systems and continuously imported into the virtual environment. Thus, the information and actual properties in the digital twin are mapped to the real conditions and the product condition in a dynamic data model. For this, it is necessary to integrate the information into the data systems of the product development and manufacturing processes. Based on this data, the behavior can be virtually tested, analyzed and predicted before actual production and use. This enables the engineer and manufacturer to further develop the product at reduced costs as early as the design phase. The virtual validation is significantly extended by the collected database in the digital twin. For companies, this means a reduction of costs by reducing material and time expenditures as well as process times - for example, with increased utilization time. On the basis of this study, a product example will be used to show which framework conditions are necessary for the use of the digital twin and which effects can be achieved in product development. It is also estimated to what extent the quality of the product and the process can be improved. In the area of additive manufacturing, for example, the question arises how quality data can be used either to control the machine parameters of the printing process in a targeted manner (feedback-to-planning) so that the desired product quality is achieved, or to adapt the product models before manufacturing (feedback-to-engineering) so that the desired product quality can be produced with existing parameters. The data alone is of little use to the companies. In addition to methodological and organizational issues, it is also necessary at the technological level to prepare the data for the various lifecycle phases of the product development process. This is where automated data evaluation in the form of AI comes in. Algorithms allow data evaluation by identifying patterns and deviations and consequently interpreting them for feedback-to-planning and feedback-to-engineering.
  • Publication
    Feedback to Design with Digital Lifecycle-Twins - literature review and concept presentation
    ( 2018)
    Riedelsheimer, Theresa
    ;
    Lindow, Kai
    ;
    Stark, Rainer
    In this paper, the authors propose a concept for optimizing the design process as well as product-related features and services through learning from Digital Twin data and establishing a continuous feedback loop from downstream phases of the product lifecycle to the design phase. As a first step, a systematic review of existing concepts in literature as well as a gap analysis is conducted. The presented concept details existing Digital Twin concepts and implementations by focusing on the specific objective of realizing Feedback to Design and integrating the lifecycle aspect.