Fraunhofer-Institut für Produktionsanlagen und Konstruktionstechnik IPK

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Characterization and application of assistance systems in digital engineering

2021 , Stark, Rainer , Brandenburg, Elisabeth , Lindow, Kai

A broad range of assistance systems can be found in manufacturing practice as well as in the corresponding literature. Similarly, it can be observed that there is a growing need for and an increasing supply of assistance systems of all kinds. However, for digital manufacturing, the assistance systems are not clearly characterized. The diversity in application areas and possible uses varies and there are no possibilities for comparison. This paper addresses the topic of assistance systems and examines the various basic elements of engineering activities in terms of possible types of assistance systems based on research in manufacturing industry. Crucial aspects of assistance capabilities for engineering are elaborated and possible digital approaches are validated based on investigations in the field of aircraft engine design and assembly.

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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.

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Development and operation of Digital Twins for technical systems and services

2019 , Stark, Rainer , Fresemann, Carina , Lindow, Kai

Digital Twins are new solution elements to enable ongoing digital monitoring and active functional improvement of interconnected products, devices and machines. In addition, benefits of horizontal and vertical integration in manufacturing are targeted by the introduction of Digital Twins. Using the test environment of smart factory cells, this paper investigates methodological, technological, operative, and business aspects of developing and operating Digital Twins. The following Digital Twin dimensions are considered in scientific and application oriented analysis: (1) integration breadth, (2) connectivity modes, (3) update frequency, (4) CPS intelligence, (5) simulation capabilities, (6) digital model richness, (7) human interaction, and (8) product lifecycle. From this, design elements for the development of Digital Twins are derived and presented.

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Smart Industrial Products - Smarte Produkte und ihr Einfluss auf Geschäftsmodelle, Zusammenarbeit, Portfolios und Infrastrukturen

2019 , Lünnemann, Pascal , Wang, Wei Min , Lindow, Kai , Müller, Patrick , Lindow, Kai , Gregorzik, Stefan , Stark, Rainer

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Progress for Life Cycle Sustainability Assessment by Means of Digital Lifecycle Twins - A Taxonomy

2021 , Riedelsheimer, Theresa , Neugebauer, Sabrina , Lindow, Kai

To understand and optimize the impact of a product along its lifecycle, the consideration of social, economic and environmental factors is of increasing interest for customers and regulating institutions. In this context, Life Cycle Sustainability Assessment (LCSA) is used to monitor and understand the trade-offs of the three sustainability dimensions. Today, LCSA still faces major challenges, such as availability, actuality and validity of data or consistent and appropriate measures to support Design for Sustainability. New technological innovations may support the enhancement of the methodology. In the background of a digitized product and service lifecycle, especially Industry 4.0 technologies, Digital Twins and the integration of Artificial Intelligence may solve data and feedback challenges through new ways of data collection, transfer, validation and intelligent analysis. This paper aims at exploring this potential of new technological innovations for an enhanced LCSA of capital goods and durable consumer goods as well as related services and proposes a taxonomy. Therefore, a literature review to identify existing digital solutions and research gaps is established. For the identified gaps, a new concept, the Digital Lifecycle Twin for LCSA is presented. The authors address both, the positive but also the negative implications put on the LCSA framework from a sustainability perspective. Ultimately, these findings will contribute to the enhancement of the LCSA methodology as well as to the design of a support system to enable environmentally and socially sound design of products and services.

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OPEN.Effect. Effectiveness of open-source hardware in times of a pandemic

2020 , Gogineni, Sonika , Tanrikulu, Cansu , Konietzko, Erik Paul , Lindow, Kai , Read, Robert L.

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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.

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Development of an Industrial Internet of Things Ontologies System

2020 , Fedotova, Alena V. , 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.

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Data preparation for data analytics (DPDA)

2019 , Stark, Rainer , Deuse, Jochen , Damerau, Thomas , Reckelkamm, Thorsten , Lindow, Kai

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From process to activity in the data flow

2019 , Lünnemann, Pascal , Riedelsheimer, Theresa , Wehking, Sebastian , Lindow, Kai

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