Applied research and development projects
In the past, the chair was able to build up long-standing expertise in data-driven digitization and AI solutions for industrial applications in a variety of applied research and development projects in the industrial environment. In the following, some selected completed and ongoing R&D projects in close cooperation with industrial companies are presented.
An essential component in the context of Industrie 4.0 is the collection, management and processing of data. Many companies do not have sufficient capacity to i) efficiently manage the large number of existing diverse data sources and ii) employ skilled personnel, such as data scientists. However, with the advancing developments in semantic data management and the ongoing breakthroughs in automated machine learning (AutoML), there are already tools that address these challenges. In order to make these breakthroughs visible in the industry in the long term and to carry them into broad application, the TMDT together with Siemens AG is researching solutions to give companies better access to Data Science by means of semantic data management and AutoML.
As part of a doctoral fellowship funded by the Phoenix Contact Foundation, the TMDT chair is conducting research together with Phoenix Contact GmbH on AI-based optimization approaches in the production and assembly of printed circuit boards. Phoenix Contact is primarily active in the production of electronic connection elements and control cabinets in the industrial sector. A central research topic is the development and implementation of an AI-based anomaly detection in visual quality inspection. For this purpose, state-of-the-art Deep Learning methods are developed to automatically identify rarely occurring defects during assembly. The methods will be implemented and validated in the real production line.
The occurrence of water damage in the basement of a house and the associated costly repair and renovation work are a major annoyance for homeowners and residents. In order to be able to take countermeasures at an early stage and at comparatively low cost in the future, options are required for continuous monitoring of the condition of the insulation layer and corresponding early detection of moisture in cases of damage. With the aim of implementing a first proof-of-concept, the TMDT is conducting research together with Achim Wunderlich Bauunternehmung GmbH & Co. KG on the use of digitalization in the construction industry. The integration of IoT sensors during the construction phase is intended to enable continuous quality monitoring even after completion of a construction project. In the course of the project, moisture sensors will be integrated into basement elements built specifically for this purpose. The sensor values are then continuously monitored and evaluated using a custom-developed dashboard. In real experiments, different influencing factors, including the quality of the mortar and the position and size of the damage, are examined with regard to their effects on the sensor measurements.
In the production of windshields, cutting-edge solutions such as augmented reality head-up displays (AR-HUD) are placing new quality demands on the existing production. New technologies of digitalization as well as artificial intelligence promise great potential for process optimization. As part of the project, the TMDT is conducting research together with Saint-Gobain Sekurit and HotSprings GmbH on AI-based quality predictions in the manufacturing process. One of the main goals is to use collected process data and trained machine learning models to make inline quality predictions and to derive quality-improving and process-stabilizing action recommendations for the process.
Recommender systems are encountered every day in online stores, on streaming platforms, and on social media platforms. An essential functionality is to help customers find new and interesting offers according to their known but especially unknown preferences. In this project, the TMDT is conducting research together with the start-up Breinify (San Francisco, USA), the leading provider of software for predictive personalization at the customer level, on the next generation of Deep Learning-based recommender systems. In this context, the solution concept aims at responding to highly fluctuating variables and adapting recommendations to these changes. During the project, the developed concepts will be tested in production environments and used in real-world scenarios, thus generating important insights for further research as well as for the AI scientific community.