Ziel des Exzellenzclusters IoP ist es, die Digitalisierung in der Produktion voranzutreiben und die Produktionstechnik dazu zu befähigen, effizienter und nachhaltiger zu agieren sowie das Fähigkeitsspektrum durch neuartige digitale Technologien zu erweitern. Dieser Fortschritt soll dabei auf allen Ebenen von der Produktionsplanung bis hin zum spezifischen Werkzeug sowie entlang der gesamten Wertschöpfungskette von der Produktentwicklung bis in den Produktlebenszyklus erreicht werden.
In the Cluster of Excellence “Internet of Production” (EXC IoP), a consortium of more than 35 participating institutes at RWTH Aachen University has been researching the topic of digitalization in production processes since January 2019. Divided into six research areas, the EXC IoP focuses on digitalization from materials to product and process development, production planning and process management through to the product life cycle. Researchers from various disciplines are working on this topic in an enormous range of areas, from basic research to concrete applications. The Internet of Production adds essential aspects to the topic of Industry 4.0, on the basis of which the production of tomorrow can be designed.
Research objective:
The aim of the Cluster of Excellence IoP is to promote digitalization in production and to enable production technology to operate more efficiently and sustainably and to expand the range of capabilities through innovative digital technologies. This progress is to be achieved at all levels, from production planning through to the specific tool and along the entire value chain from product development through to the product life cycle. Various specialist areas and production domains are represented in the consortium in order to drive research forward efficiently and achieve a targeted interdisciplinary transfer of knowledge.
© IKVExcellent continuation:
This broad range of capabilities is to be further expanded in the future as part of the new funding phase of the Excellence Strategy towards “Sustainable Production using digital product passports” (Figure 1). IKV’s ongoing work, which is reflected in the following research areas, serves as the basis for this:
CRD A2 Infrastructure:
Basic work in the area of data infrastructure is a fundamental building block in the EXC IoP, because data infrastructure is the basis for successful digitalization of production. In this area, an interdisciplinary consortium is researching the foundations for the digital representation of real processes in the form of digital shadows. The focus here is on the standardized, structured and consistent description of information technology relationships and data streams of the complex processes under consideration and all their data sources. The overriding aim is to model the real process in its immediate state using data technology and to make it describable so that added value can be generated on this basis, such as process optimization.
CRD B1 Materials and substances:
Due to the complex interactions between process and material, consistent consideration of the current material properties is essential. In plastics processing, these effects are particularly evident due to increasing demands on process quality in conjunction with increasing uncertainties regarding material properties (e.g. batch fluctuations or increasing recycling rates). The consistent description of current material properties is therefore a basic requirement for many steps along the value chain, from product development to the life cycle. The Materials and Materials Research Division (CRD B1) is therefore researching the fundamentals for the cross-scale description and availability of material information at all required granularities. Modern methods of molecular simulation, for example, enable the prediction of material changes based on various influences such as moisture exposure or chain shortening through recycling processes.
CRD B2 Production Technology:
In the area of “Production Technology”, specific methods are developed from a production technology perspective that can be used to model and optimize manufacturing processes. In this area, data-driven approaches based on machine learning methods are used in particular. In collaboration with partners from the fields of computer science and numerics in particular, real data and simulated synthetic data are combined with machine learning approaches to generate virtual process models in the field of plastics processing. In the field of injection moulding, for example, these enable an efficient and structured process setup or, in the field of extrusion, the optimization of profile extrusion. The respective quality requirements are taken into account and used as a target. These processes are extended and enabled for industrial use by transfer learning methods, which can significantly reduce the need for initial process data by using synthetic data from simulations and real data from past, similar processes. IKV also uses AI-based approaches for extrusion and additive manufacturing in order to achieve efficient process model training with minimal use of resources by combining automatically recorded data with synthetic data and to use this for process optimization or optimized process setup.
CRD B3 Production Management:
Digital infrastructure concepts in particular are used in the area of production management to enable targeted automated production planning or adjustment based on order data and the complex interplay of availability and suitability of various production systems and resources. Additive manufacturing is being considered as a concrete application scenario at IKV. In accordance with the principles developed in CRD A, digital shadows of production can be set up and live planning or optimization can be carried out. Based on historical data, a system-specific selection can be made for optimized product quality.
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