Greybox-modeling the run-in behavior of coated tools in the milling process as dynamic load profile based on operando, in situ and ex situ analyses

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The dynamic stability of a milling process is determined by cutting parameters as well as by structural-mechanical properties of the milling tool. In the cutting process, the tribological load changes properties of the tool cutting edge resulting in a transient system behavior. Consequently, dynamically unstable process conditions may drastically increase the tool wear and have a detrimental effect on the production quality. A wear protection layer of, e.g., TiAlN or TiAlSiN has a wear-reducing effect. A fundamental and holistic understanding of the transient system behavior of coated milling tools and its influence on the dynamic process stability allows for an efficient process design. In the project, measured data of the steady-state and transient system behavior are used to reveal the chemical and mechanical wear initiation and development of coated milling tools with respect to the dynamic run-in behavior by means of a multi-scale greybox modeling. The process-oriented design of the cutting-edge preparation as well as the PVD coating using TiAlN and TiAlSiN thin films consider tool concepts established for milling processes. Parametrically broadband measurement techniques quantify the wear behavior of the coated tool cutting edges spatially and temporally resolved, depending on the load profile. The targeted greybox modeling is based on (i) data from operando and in situ measurements of the transient system behavior as well as ex situ analyses of the “before and after” state of the milling tool, which are evaluated using artificial neural networks, and (ii) known physical as well as material and production technology causalities. Due to the interdisciplinary collaboration at TU Dortmund University between Prof. Dr. Dirk Biermann, Dr. Jörg Debus and Prof. Dr. Wolfgang Tillmann, expertise from the fields of machining technology, laser spectroscopy and multiparametric surface analysis as well as thin film technology is incorporated into the greybox model realized by a common platform. In the process, the onset of failure, wear progress and the end of lifetime of TiAlN and TiAlSiN coated tools are identified and predicted with reasonable confidence. The enhanced understanding of the system behavior allows for developing fundamental strategies to increase the tool life, process stability and efficiency regarding high manufacturing quality and productivity. The interdisciplinary findings and model-based predictions are used for the resiliently optimized design of the milling processes.