Fraunhofer-Institut für Produktionsanlagen und Konstruktionstechnik IPK

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  • 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.