Development of a Greybox model for wear prognosis of PVD coated carbide tools during high performance turning of steels

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The continuous coating and cutting material degeneration during the cutting process contribute to an instationary thermomechanical stress collective of the physical vapor deposition (PVD) coated tool. However, the currently developed analytical Whitebox models for tool wear prediction are based on a stationary integral thermomechanical loading of uncoated tools, whereby the transition from linear to progressive wear increase is not considered. Data-driven Blackbox models cannot represent the physical interactions and their robustness is associated with uncertainties when it comes to variable boundary conditions. Consequently, an accurate prediction of tool service life and knowledge-based qualification of coated tools for demanding machining processes is not possible.The main aim of the research project is the development of a Greybox model for an accurate prediction of tool life and remaining service life of PVD coated carbide tools during high-performance turning of steels. For this purpose, a coupling between analytical Whitebox models for determination of the instationary thermomechanical stresses in the cutting process with data-driven Blackbox models is proposed in the first phase of the project. Moreover, the influence of temperature dependent coating properties on tool wear progress will be thoroughly investigated. Firstly, TiAlCrSiN and TiAlCrSiON coatings, each with two different coating thicknesses, will be deposited on the cutting inserts and characterized. Subsequently, the temperature dependent elastic-plastic coating properties and the system behavior are to be determined. The coated tools will be used for rough turning of heat-treated C45 and 42CrMo4 steels under dry condition. In order to determine the thermomechanical tool load at different process parameters, required for development of the Whitebox model, analogy cutting tests are planned. Cutting tests with in situ acquisition of the process state variables and focus on linear to progressive tool wear transition zone will form the experimental basis for quantitative tool damage analysis. Finally, a Greybox model will be developed and validated to enable online remaining tool life prediction during the cutting process. For this purpose, the Whitebox model and experimentally determined data on coating properties and tool wear progress will be coupled by means of machine learning based Blackbox models. In the second phase of the project, accuracy, sensitivity and error analyses will be carried out to further increase the prediction accuracy as well as the transferability of the developed Greybox model. In the process, previously undiscovered research areas for the emerging digitalized machining technology can be explored.