ENGAGE-KI supports medium-sized companies in integrating Artificial Intelligence into their development processes. Together with companies, we develop practical applications, test new technologies, and convey implementation competencies. The focus is on AI-supported knowledge management, Copilots, AI agents, and humanoid robotics. The goal is more efficient processes, better utilization of corporate knowledge, and more room for creative development work.

In the context of stricter regulations and growing social expectations, many companies are striving to achieve ambitious targets for reducing emissions. In doing so, they face considerable challenges, particularly in the product development phase, which largely determines the subsequent environmental impact. Studies show that around 80% of future environmental impacts are already determined in the planning and development phase. Two approaches have particular potential to reduce emissions and conserve resources: the circular economy and efficient product operation. The circular economy makes it possible to close material and resource cycles, minimize new production and use resources sustainably. At the same time, the operation of products requires optimization in terms of efficiency and resource conservation. Digital technologies play a key role in both areas, as they enable innovative approaches and solutions. This is precisely where the GoProZero project comes in: The aim is to design the product creation process holistically in order to develop resource-efficient and recyclable products that meet the requirements of a modern, sustainable and digitalized industry. To this end, approaches to strategic planning, the design of processes and organizations and the use of digital technologies such as model-based design, data infrastructures and AI-supported systems are being developed. At the same time, the implementation of the project goals is ensured in practical lighthouse projects in order to address specific problems of the partner companies and establish innovative solutions. Through this comprehensive approach, GoProZero supports companies in reducing emissions, using resources efficiently and strengthening their competitiveness in a more sustainable industry.

In dem Transferpiloten soll ein Predictive Quality System (PQS) entwickelt und im Echtzeitbetrieb eva-luiert werden. Für ein zuverlässiges PQS ist es erforderlich, eine automatisierte Datenverarbeitungs-Pipeline zu implementieren. Weiterhin soll das CMS durch maschinelle Lernverfahren an unterschiedli-che Produktionsanlagen adaptiert werden können. Hierzu sollen Condition Monitoring Verfahren auf der Basis hybrider Prozessmodelle weiterentwickelt und in eine graphische Benutzeroberfläche inte-griert werden. Die Ergebnisse sollen über die Innovationsplattform in einen Transfer überführt werden.

Construction companies that manufacture precast reinforced concrete components for industrial and commercial construction have extensive outdoor warehouses where these components are temporarily stored and held. Current warehouse mapping systems and warehouse management processes are largely based on manual processes, which leads to inefficient placements of the precast parts and thus to a warehouse that is not optimally utilized. The transfer project "DeepConcrete" aims to automatically map BREMER AG's outdoor warehouse using camera systems and AI methods. For this purpose, image recordings from cameras on the gantry crane are to be segmented. The automated recording of the warehouse inventory thus enables the implementation of optical monitoring and AI-based warehouse management. The long-term vision of the project is a digital platform for order and goods flow management as well as a digital mapping of the warehouse to optimize customer offers, logistics and production planning.

Nowadays, efficiency and resource conservation are becoming increasingly important. This is also a current topic in waste disposal. This transfer project was therefore about how to evaluate measures and the efficiency of waste disposal in a city in general. The company c-trace GmbH offers IT and hardware solutions in the field of waste disposal. The company already collects a lot of data, such as telematics and weighing data from refuse collection vehicles. Together with Fraunhofer IOSB-INA, the goal of the project was to analyze this data and develop features from it. With the help of these features, potential for improvement can be identified and they can also help to enable transferability to new cities and municipalities.

Security is becoming increasingly relevant for the secure and resilient operation of industrial production facilities. In particular, attacks, e.g. with malware such as ransomware, pose an existential threat to companies of all sizes and domains. In addition, security is increasingly becoming a selling point for components and systems used in industry. A key element in the field of security is security assessments based on a risk evaluation by the manufacturer of the components or the operator of the systems. However, these are currently carried out manually by experts, which makes them time-consuming and resource-intensive, as well as heavily dependent on the individual experience of the respective expert. Small and medium-sized enterprises (SMEs) in particular are often unable to afford their own experts with the necessary security expertise. In addition, the required experts are often not available on the market. Within the scope of the proposed project, a software- and machine-learning-based support of risk assessments for industrial production facilities shall be developed in order to relieve experts of routine tasks, to reduce the costs for security risk assessments, to increase the degree of automation and to ensure a uniform quality of the assessments by formalizing the expert knowledge.

Inspection detects the slightest defects Friedrichs & Rath GmbH (F&R) is a specialist for precision parts made of thermoplastic materials and manufactures a high proportion of these parts for the automotive industry. In order to meet the high quality demands, especially of customers in the automotive industry, while at the same time producing large quantities, F&R would like to introduce a learning, precise automated optical quality control system. The inspection system should be able to reliably detect even the slightest defects and, if possible, classify and categorize them. The goal is an inspection application that can be adapted to different types of plastic parts with little effort, even by non-specialist employees.

Research-based transfer for an innovative circular economy With InCamS@BI, the Innovation Campus for Sustainable Solutions, Bielefeld University of Applied Sciences is positioning itself in the East Westphalia-Lippe region as an innovative transfer player in the field of the circular economy. The focus is on sustainable materials research. Resource scarcity, climate protection, regulations - the plastics industry is facing major challenges. The interdisciplinary transfer project InCamS@BI aims to accompany companies on their way to the Circular Economy and work with them to develop sustainable solutions in the field of optimizing plastics for circular value creation.

For high-quality large-scale production with low manufacturing tolerances, it is necessary to detect and correct dimensional deviations already in the machining process in order to reduce scrap production to a minimum. For this reason, an intelligent, automated In Process Measurement (IPM) system is being developed in the transfer pilot and evaluated in a productive cutting machine as a retrofit solution. This system aims to reduce scrap costs by up to 90% and energy costs by up to 8%. The results will be presented at events organized by the Leading-Edge Cluster.

As a service provider for IT systems and equipment supplier for vehicles in the waste management industry, c-trace makes a decisive contribution to sustainable waste management in municipalities and cities. Among other things, the collected waste quantities as well as the routes of waste collection vehicles are recorded and used in current products for optimal route planning, early emptying at waste collection points and overall reduction of driven kilometers. In this way, c-trace contributes directly to saving CO2 emissions in the urban environment. A central challenge lies in the transfer of the results of the use of such track-and-trace systems for refuse collection vehicles from one municipality to another. For such a transfer of results, it is necessary to develop and use public data sources that allow a data-based modeling of city districts and street clusters. The range of possible data extends from shapefiles of the street topology to standard land values and demographic and social factors. In a follow-up project, this new database is to be transferred into a new data product in two steps, together with appropriately processed telemetry data.