Fraunhofer-Institut für Produktionsanlagen und Konstruktionstechnik IPK

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  • Publication
    Methodology to develop Digital Twins for energy efficient customizable IoT-Products
    ( 2021)
    Riedelsheimer, Theresa
    ;
    Gogineni, Sonika
    ;
    Stark, Rainer
    Products are increasingly individualized and enhanced to be able to communicate, e.g. via Industrial Internet of Things (IoT). However, the impact of products on sustainability (environmental and social) across their life is often not considered and analyzed. IoT-based or smart products, that are able to communicate, generate data, which can be used to monitor and optimize sustainability indicators. The Digital Twin (DT) is a new technological concept which focuses on product individual data collection and analysis. It provides the possibility to make use of the available data and optimize the systems individual sustainability as well as future product generations. However, the design and realization of such a DT requires new approaches and capabilities, which is an identified research gap. Therefore, this paper presents a methodology to develop DTs of physical IoT-based products, the so called DT V-Model with the aim to optimize the systems sustainability, specifically environmental aspects. It is based on the V-model for the development of smart products and is enhanced with additional roles and approaches for DT development. The methodology is described in detail. The result of a development cycle according to the DT-V-Model is a tested concept of a DT, which includes Digital Master (DM) data from the planning phase and Digital Shadow (DS) data from the production, operation and End of Life-phase. For a DT for energy efficiency, the Digital Master model consists of the information and models from the product development phase including the planned production and use phase energy consumption. The Digital Shadow consists of the actual production energy consumption and the use phase energy consumption. The methodology is applied to a use case of an IoT-based consumer product that can be customized to a certain degree by the consumer. A DT is developed to monitor and optimize the products energy efficiency in production and use. The necessary elements of the DT and the capabilities are depicted. The paper shows the feasibility of the methodology for the development of DTs, the necessary adaptions to common approaches for development and the specific characteristics of DT development for the aim of energy efficiency.
  • Publication
    Systematic literature review - Effects of PSS on sustainability based on use case assessments
    ( 2020)
    Blüher, Till
    ;
    Riedelsheimer, Theresa
    ;
    Gogineni, Sonika
    ;
    Klemichen, Antje
    ;
    Stark, Rainer
    Product-service systems (PSS) are often presented as an inherently sustainable business model. The argumentation is often based on theoretical considerations, which cite circular economy (CE) characteristics in PSS business models as an explanation. In this paper we examined to what extent positive and negative sustainability effects of PSS could actually be observed, based on use cases. For this purpose, we conducted a systematic literature review and analyzed the statements on sustainability effects based on the triple bottom line approach. We find that positive sustainability effects, especially on the environmental sustainability of PSS, are described disproportionately often, which may be indicating a possible publication bias. In addition, the methods used to derive statements on sustainability effects are very heterogeneous and often unsystematic, making it difficult to compare the described effects. Furthermore, we were able to identify drivers that are particularly often considered in literature to be responsible for sustainability effects. As a result, we were able to derive direct implications for future research in the field of sustainability assessment of PSS.
  • Publication
    Assembly Issue Resolution System Using Machine Learning in Aero Engine Manufacturing
    ( 2020)
    Brünnhäußer, Jörg
    ;
    Gogineni, Sonika
    ;
    Nickel, Jonas
    ;
    Witte, Heiko
    ;
    Stark, Rainer
    Companies are progressively gathering data within the digitalization of production processes. By actively using these production data sets operational processes can be improved, hence empowering businesses to compete with other corporations. One way to achieve this is to use data from production processes to develop and offer smart services that enable companies to continuously improve and to become more efficient. In this paper, the authors present an industrial use case of how machine learning can be implemented into smart services in production processes to decrease the time for resolving upcoming issues in manufacturing. The implementation is carried out by using an assistance system that aids a team which attends to problems in the assembling of turbines. Therefore, the authors have analyzed the assembly problems from an issue management system that the team had to resolve. Subsequently three different approaches based upon natural language processing, regression and clustering were selected. This paper also presents the development and evaluation of the assistance system.
  • 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
    Digitaler Zwilling für Smart Services
    ( 2019)
    Exner, Konrad
    ;
    Preidel, Maurice
    ;
    Gogineni, Sonika
    ;
    Stark, Rainer