Project Information
| Title of the project: | ProModFun – Cross-process modeling for the production of functionally graded layer systems by thermal spraying and mechanical post-processing |
| Duration: | 10/2025 – 09/2028 |
| Funding programme: | Individual research grant (SPP 2476) |
| Project execution organisation: | Deutsche Forschungsgemeinschaft (German Research Foundation) |
| Project leader: | Prof. Dr.-Ing. Andreas Schubert |
| Staff: | |
| Project partner: | Chair of Materials and Surface Engineering (Chemnitz University of Technology) |
| Abstract: | Industrial manufacturing of components entails the sequential combination of production processes. This results in the creation of products with defined functional properties. The challenges lie in optimising both the individual processes and the process chain in terms of different requirements, while addressing process-related uncertainties. Multi-criteria optimisation is a demanding task due to the partly conflicting target values. The process chain in ProModFun is to be optimised based on experimental investigations and a data-driven modelling approach. Functional coatings of Hadfield steel are applied by thermal spraying. Their surface properties are defined by subsequent turning and diamond smoothing. The target variables of the process chain are to be influenced as follows: maximisation of surface hardness, oxide content of the coating and energy efficiency of the process chain, minimisation of surface roughness values. For the different processes, sensors are integrated into the machining systems. The corresponding measurement data is used for modelling. Statistical modelling and a multidimensional, pattern-based description using greybox AI algorithms (fuzzy pattern classification) serve as procedural approaches. These enable the quantification of various types of uncertainty and the forward coupling of the process chain. Based on this, a procedure for inverse process chain optimisation is developed. |
