Increasing networking and standardization in industry are opening up new opportunities for the uniform mapping and control of production systems. In this position, you will have the opportunity to help design a forward-looking digital twin framework based on industry standards that combines machine control, simulation, and monitoring. You will work at the interface between research and practice and develop solutions that can be transferred to a wide range of production machines.

Additive manufacturing processes such as FFF are among the modern production processes. Their integration into cyber-physical production systems requires transparency and standardization. Digital twins offer great potential here for consistently mapping control, monitoring, simulation, and analysis in digital form. The work builds on existing research at the institute and aims to develop a transferable, standards-based approach to modeling additive manufacturing machines.

Intuitive and flexible control of production systems is one of the key challenges facing modern manufacturing environments. Language- and text-based AI opens up new possibilities in this area by allowing complex processes to be described in natural language and automatically translated into standardized, machine-readable skills. This creates low-threshold access to the programming and adaptation of industrial processes.

Rising product complexity, shorter development cycles, and sustainability demands are driving automation in product development. AI-based generative design, especially in topology optimization, offers efficient ways to explore complex design spaces and enable largely automated processes.

The efficient assessment of a component’s structural integrity using deep learning enables the joint optimization of material selection and distribution. This allows for a more flexible and exploratory design space exploration, fostering the development of more holistic design solutions.

Subject areas:

1. Simulate Contact Deformation in FEM (Siemens NX)

2. Parametric Simulation Studies

3. Stress Distribution and Stiffness Analysis

4. Computational Tolerance Analysis

Subject areas:

1. Finite Element Method (FEM), via Siemens NX

2. Parametric Simulation Studies

3. Stress Distribution and Stiffness Analysis

4. Computational Tolerance Analysis

Revoltech is a young start-up at TU Darmstadt that develops sustainable leather alternatives. In order to substantiate the sustainability of one of their products, MATTR, with facts and figures, a Life Cycle Assessment (LCA) is now to be carried out. The scientific challenge here is dealing with missing data on a product in the development stage and formulating sustainability requirements.

The aim of this work is to enable the automated consideration of life cycle assessment in the optimisation of design process.