Project Details
Projekt
Cross-process modeling for the production of functionally graded layer systems by thermal spraying and mechanical post-processing – ProModFun
Applicants
Privatdozentin Dr. Franziska Bocklisch; Professor Dr.-Ing. Thomas Lampke; Professor Dr.-Ing. Andreas Schubert
Subject Area
Metal-Cutting and Abrasive Manufacturing Engineering
Coating and Surface Technology
Coating and Surface Technology
Term
since 2025
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 558601614
The industrial production of components usually takes place through the sequential connection of several manufacturing processes that build on each other, interact with each other and, as a result, create products with a defined property profile. The challenges consist of (1) optimally designing the individual manufacturing process, (2) countering the uncertainties inherent in the process and (3) carrying out global optimization across the process chain. Important target variables for optimization include quality, safety, cost, energy and resource efficiency criteria as well as manufacturing flexibility. Modelling approaches can be used for multi-criteria optimization. White-box models and knowledge-based approaches are not equally applicable to every technical manufacturing process due to the complexity of the process or task. Therefore, the systematic, experimental approach is crucial for the generation of relevant data. In the proposed project, a process chain is to be set up with which the above-mentioned questions of (inverse) multi-criteria optimization, data-based modelling and uncertainty quantification can be addressed experimentally and by means of non-destructive process diagnostics using a specific example. As the primary shaping production step, “atmospheric plasma spraying” is to produce surfaces from manganese hard steel, which create adapted core/edge properties with a graded transition through the two mechanical finishing processes “turning” and “diamond smoothing”. The processes are to be optimized with regard to the following target values: (1) maximize surface hardness with depth grading, (2) minimize surface roughness and (3) design the process chain for maximum energy efficiency. The process chain is a new, innovative approach to linking the individual processes, e.g. for the purpose of targeted modification of the surface properties of rollers and rollers in forming processes (demonstrator). The components to be manufactured are rotationally symmetrical components. The modeling is based on the experimental measurement data. Two approaches are used: (1) statistical modeling (e.g. regression approach) and (2) pattern-based description in multidimensional state spaces using grey-box algorithms of artificial intelligence (AI). Both methods are initially used for the isolated design and optimization of individual processes. They enable the quantification of different types of uncertainty and form the basis for the forward coupling of the process chain. Cross-process and initial inverse analyses are carried out using the AI method, which incorporates the results of the statistical models. The aim of the project is to achieve global optimization of the target variables defined in the specifications.
DFG Programme
Priority Programmes
