Model-based in-process determination of the tool wear at high performance turning

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Despite the variety of coated carbide tools in use, the understanding of tool wear underlying mechanisms still shows considerable deficits. In this project, a (whitebox) model based on chip formation simulations is to be combined with an artificial neural network (ANN, blackbox model) to form a greybox model in order to develop a significantly improved understanding of the wear formation and development of coated carbide tools for high-performance turning. The whitebox model is intended to enable an approximate determination of current tool wear parameters on the basis of the measured thermo-mechanical load spectrum. The estimated wear will then be used together with in-process measurement data as input for a trained black box model to precisely predict tool wear. In particular, the information on the wear condition of the tool contained in structure-borne noise signals, Barkhausen noise amplitudes, workpiece dimensions and surface roughness will be taken into account, in addition to the information contained in the thermo-mechanical load spectrum. With this research approach, the priority program should succeed in mapping and identifying previously unknown mechanisms of coating degradation and tool wear and thus contribute to an improved knowledge-based qualification of tool coatings for high-performance cutting. The whitebox model is developed on the basis of an existing finite element chip formation model. The valid embedding of the coating properties and the inverse model usage are clarified. The blackbox model is realized by means of an artificial neural network. Its training requires a fast direct detection of the tool wear, so that two measurement procedures based on optical principles are realized and used, which enable a precise in-situ determination of the tool geometry and the coating thickness. For the black box model, the structure of an artificial neural network and the sensor data are investigated to enable wear determination with minimal uncertainty. In order to minimize the number of inputs of the blackbox model, it is clarified how a signal preprocessing of the in-process collected data with negligible information loss can be realized. The resulting greybox model is developed and validated for external longitudinal turning of quenched and tempered 42CrMo4. In addition, an extended validation on the cross-section material of the priority program C45 is planned. In the second phase of the priority program, the greybox model is to be extended for the prediction of wear development as well as for a broader application range of actuating and system variables.