Department of Model-based process planning (MP)

Head of the department: Dipl.-Ing. Sebastian Langula

The department researches and works at the interface between production technology, digitalization and artificial intelligence. Our aim is to make the production of the future more sustainable, flexible and efficient. In doing so, we focus on developing innovative technologies that meet both the ecological and economic requirements of modern production processes.

© Martin Erler

Autonomous production planning is at the heart of our department. Our aim is to develop intelligent systems that can independently analyze, optimize and adapt production processes. The focus is on the automation of process planning, from the generation of tool paths to the control of complex process chains. We also rely on the integration of modern technologies such as artificial intelligence (AI) and machine learning (ML) to enable data-driven decisions and implement adaptive manufacturing processes. A particular challenge is flexibility in production, which is to be achieved by dynamically adapting manufacturing and logistics systems to changing requirements.

Sustainability is a central concern of our work. We develop innovative methods and technologies that optimize the use of materials and energy and minimize waste. We attach great importance to combining machining and additive processes in order to utilize the advantages of both approaches. We focus on process optimization to reduce emissions and material losses as well as life cycle-oriented manufacturing concepts that promote sustainability in production through innovative process chains.

Another focus of our research is on flexible manufacturing process chains. The aim is to design production processes in such a way that they are robust and adaptable to varying requirements. This includes hybrid manufacturing processes that seamlessly combine additive and subtractive technologies. We are also working on automated systems for planning and optimizing process chains that enable dynamic adaptation to different production scenarios. This is complemented by simulation-based approaches that provide a precise prediction of process behavior and thus a sound basis for decision-making.

Further information and overviews are also available in the TU Dresden's Research Information System.

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