Development of a Greybox Model for the Prediction of the Performance of PVD Coated Carbide Tools

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According to the current state of the art, the real, complex application behaviour of PVD-coated tools can neither be measured satisfactorily nor described sufficiently in models. In order to be able to conduct a knowledge-based qualification of these tools, it is necessary to gain knowledge about the start of failure, the progress of wear and the remaining tool life. In particular, discontinuous coating failure cannot be modelled due to a lack of knowledge of the fundamental relationships. In addition, both the coating properties and the thermomechanical loads applied change with increasing operating time as a result of wear. The proposed research project addresses the dynamic relationship between variable coating properties on the one hand and the variable process-related loads on the other hand for external longitudinal turning of the alloys C45N and 42CrMo4+QT. It is known from the state of the art that the coating properties have a significant influence on the application behaviour of the tools. It is also known that both the coating properties and the thermomechanical load stresses change significantly due to tool wear. Taking into account the varying properties and loads allows for a more accurate prediction of the performance of the cutting tools and helps to build up fundamental understanding to design and select tools in a load-specific way. For this purpose, the tools are extensively characterised at the beginning of the research project with regard to coating and interface properties between the coating and the carbide substrate. This is followed by the development of a detailed data basis for the subsequent modelling. With the help of a new method based on investigations on a planing test rig in combination with microkinematography, it is possible for the first time to map the load stresses acting on the cutting edge as tool wear progresses. Together with the thermal load, which also changes over time, these load stresses serve as input variables for an FEM model for mapping critical stresses in the cutting wedge that promote coating failure. The predictive capability and accuracy of this whitebox model is extended by the use of artificial intelligence methods. Since it is difficult to map the changed coating properties on the basis of physical relationships and use them for such a prediction, data-driven approaches based on high-resolution analyses of the coating properties and residual stresses are employed. By observing the relationship between stresses and coating properties, it is possible to predict the further behaviour of the tools and gain new insights into the underlying mechanisms.