Project Details
Projekt
Material of workpiece and tool
Subject Area
Metal-Cutting and Abrasive Manufacturing Engineering
Mechanical Properties of Metallic Materials and their Microstructural Origins
Mechanical Properties of Metallic Materials and their Microstructural Origins
Term
since 2026
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 546480484
The detailed analysis of the deformation and failure behavior of workpieces and tools, including their changes during machining, is a fundamental pillar of process informatics. This sub-project incorporates changes in the condition of the workpiece and tool materials involved in the machining process into the data-driven modeling via material models. These also serve as a basis for interpreting automated data acquisition via process-integrated sensors and for translating thermal effects into material and tool states. To this end, the data obtained from material testing and the material models derived from it are to be used to assign real state variables and material changes in the chip forming zone to measured effects and sensor variables. This is achieved through a targeted combination of data from machining experiments, FE chip formation simulation with thermo-mechanical-metallurgical material models and material engineering experiments, whereby the basis of an efficient, process-informed overall machining model is laid. The corresponding material data analysis by means of machine learning (ML) and optimization algorithms is carried out by detailed characterization of individual material state variables and their influence on the batch-specific machining process with regard to workpiece and tool (e.g. heat treatment, deformation, defects, microstructure gradients). This should provide information on which data must be recorded in parallel with the process. ML approaches including experience-based and deductive knowledge for model acceleration as well as empirical model approaches for the description of material mechanisms are used. In this way, efficient, robust models are developed, taking into account uncertainties of the individual initial material state and its process-inherent development. The following key questions are addressed: 1) How can effects of deformation, failure and microstructure evolution in the process be attributed to the surface layer integrity through interactions of temperatures, forces and rates? 2) Which process sensor technology enables efficient and robust data acquisition of material conditions for the prediction of workpiece and tool surface layers in process-informed models? 3) How can uncertainties in the identification of material parameters and process boundary conditions be estimated and translated into uncertainties in the predicted material states?
DFG Programme
Research Units
