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Professorship Micromanufacturing Technology
Projects
Professorship Micromanufacturing Technology 

Project Information

Title of the project: Adjustment of the surface properties in turning for the prediction and improvement of the fatigue strength of components using the example of martensitic steel
Duration: 01/2025 – 12/2027
Funding programme: Individual research grant (Package proposal)
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)
Research group Scientific Computing and Optimization (Heidelberg University)
Abstract:

Finish machining is the final step in the production of dynamically loaded components. In addition to the workpiece geometry, it determines the surface structure and the properties of the surface layer. Both surface structure and surface layer influence the fatigue strength. Consequently, there is great interest in custom-designed surface properties and their reliable generation. The complex interactions between the machining conditions and the resulting component and functional properties are superimposed by disturbance variables such as tool wear. Currently this impact is addressed through appropriate correction and safety factors in component design. Hence, it is possible to increase resource efficiency by specifically adapting the properties of the component surface layer. Additionally, the surface properties can be monitored and the turning process controlled.

The project aims to improve the understanding of the turning process for the martensitic steel X46Cr13. This requires the integration of sensors to record acoustic emission signals, the components of the resultant force as well as Seebeck current and voltage during machining. On this basis, systematical investigations into the influence of the cooling lubrication strategy, cutting parameters, tool geometry, tool wear, and heat treatment condition of the specimens are carried out. In this context, suitable data processing methods are identified and relevant measurands are determined. After machining, the geometrical surface properties of the specimens are analysed using tactile and optical measuring methods as well as SEM. Moreover, surface layer characterisation and fatigue testing are carried out. Based on the process input variables and the in-situ and ex-situ measured variables, a model is developed to represent the cause-effect relationships using correlations. The model is utilised to specify relevant data points and test combinations for machining.

As a result, the understanding of the interdependencies between cutting parameters, tool geometry, tool wear, heat treatment condition and resulting surface properties in turning of X46Cr13 is extended. In this context, the suitability of selected in-situ measurements for process monitoring and evaluation is quantified. Furthermore, the possibilities and limitations of increasing the fatigue strength by adapting the cutting edge geometry are derived. The results shall enable the prediction and enhancement of the fatigue strength of machined components.