Extrapolative digital greybox models for describing and predicting the macroscopic system behavior of TiAlN-coated cutting tools

Tools with a hard coating make up the majority of cutting tools used today. They protect the substrate from abrasive wear, increase chemical resistance and reduce the coefficient of friction, resulting in increased tool life during turning and milling operations. Various empirical or physically based models exist for the estimation of tool life. Since the properties of these coatings depend on a large number of manufacturing parameters, no consistent models exist to predict the wear behavior of hard coatings. The objective of the proposed research project is to gain knowledge about the tribological cause-and-effect relationships of the wear behavior of coated cutting tools in interaction with the workpiece material and the process parameters under consideration of the particular mechanical, chemical-structural and tribological properties of the coating and to map this knowledge into a model. Therefor greybox models shall be used. These models are rely on artificial neural networks (blackbox) that use training data based on process parameters, measurements and metadata from machining tests. Physical and empirical models for wear rate prediciton and remaining tool life are additionally integrated into these models (whitebox). This allows the model, with the limited scope of training data due to the high experimental effort, to predict physically meaningful solutions. In machining tests, which are partially automated by integrating force, temperature, structure-borne sound and surface measurement techniques as well as microscopy in the workspace of a CNC lathe, a continuously growing database is created for the simultaneous model building. In order to minimize the experimental effort and thus the material and energy costs, the next experimental parameters are determined in each case on the basis of the current training state of the model using Active Learning. The recorded measurement and metadata are available in a wide variety of formats, such as image data, continuous measurement or discrete measurement points. They are processed and stored with their metadata in an digital lab book. Subsequently, the data is homogenized and reduced to obtain a balanced data set for training the neural networks. For final validation, blind tests, with cutting parameters and coating unknown to the model, are performed to confirm the interpolation and extrapolation capabilities of the greybox model.

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