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    In-process prediction of surface delamination during circumferential milling of carbon fibre-reinforced components
    Lightweight components made of carbon fiber reinforced plastic (CFRP) are manufactured close to their final contours, but almost always require machining to finish the edges, which often leads to component damage. Because CFRP machining differs fundamentally from metal machining due to the highly orthotropic material properties, machining models for metallic materials cannot be adequately transferred. Despite extensive research into individual influencing factors and damage mechanisms, there is a lack of holistic CFRP machining models that take micro-scale mechanical effects into account, which is why component damage cannot be reliably predicted. The project´s goal is therefore to create a 3D process model for a CFRP contour milling process that can predict top layer delamination based on surface deformation data collected during the process. The model will examine in particular the (interactive) effects of (wear-related) tool geometry, fiber orientation, and process parameters. The surface deformation of the component is determined using the laser speckle photography laser optical measurement method and converted into strains, which are compared with limit strain values derived from the process model, from which a delamination prediction can be derived. The process model and the measurement technology are designed and validated using a well-defined reference milling process, which is first used to collect and compare deformation data and damage information for various process parameters (e.g., cutting speed) for an ideally rigid workpiece clamping. In addition, the project investigates how non-ideal component clamping during the normal machining process affects the measurement uncertainty of the deformation measurement, what influence the lateral deformations to be expected in the process have on the measurement uncertainty, and how these can be separated from the deformations of interest here in the direction of the surface normals. Since the 3D process model is far too slow for use in delamination prediction during the process, additional research is being conducted into how and with what deviations the process model can be converted into a faster replacement model. Finally, in-process validation of delamination detection and prediction is to be carried out in a commercial milling machine. The aim is to use the results of the first phase to clarify the uncertainties involved in using in-process deformation measurement and a fast process model to achieve early detection of delamination formation in parallel with the process, which influencing factors contribute significantly to the uncertainty budget, and which process variables have a decisive influence on component damage. Based on these fundamentals, a process control system for preventing delamination damage will be developed in the second phase.
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    Transferprojekt: Wear behavior of coatings for machining of TiAl6V4
    Due to increasing scarcity of raw materials, the demand for near-net-shape finishing milling processes for additive manufactured components made of TiAl6V4 is growing. In order to achieve high economic efficiency, the focus is on increased cutting speed, which leads to high tool loads. An improvement in cutting performance can be obtained by the use of triboactive CrAlMoN hybrid coatings. Here, direct current magnetron sputtering is combined with high power pulsed magnetron sputtering (dcMS/HPPMS). Within the preceding project (project number 422345568), the oxidation and diffusion behavior as well as the application behavior of CrAlVN and CrAlMoN physical vapor deposition (PVD) coatings on carbide were investigated during the turning of the difficult-to-machine titanium alloy TiAl6V4. The formation of friction-reducing oxide phases in the area of the cutting edge was verified despite the use of cooling lubricant. With the CrAlMoN coating, a considerable improvement in cutting performance can be obtained, which for the first time exceeded that of the uncoated substrates used as state of the art. In addition, it is known from preliminary work that the HPPMS power component has a beneficial effect on shape contour accuracy as well as contributing to a denser morphology, smoother surface and higher coating hardness. However, still unknown is the impact of increasing the HPPMS power ratio on tool performance in the machining of TiAl6V4. Additionally, the cutting temperature in finishing milling processes of TiAl6V4 as well as the damage mechanisms have not been investigated yet. Main aim of the proposed transfer project is the transfer of knowledge gained in the preceding project to industrial fields of application for cutting titanium alloys. These include the coating production and the performance of triboactive CrAlMoN coatings on carbide tools, which have already been used in the finish turning of TiAl6V4 under laboratory conditions. For this purpose, a transfer of previous turning tests to the industrial application conditions as well as an application transfer from turning to milling is planned in cooperation with Premium AEROTEC GmbH. Furthermore, a transfer of the deposition process of the CrAlMoN coating to a coating unit with increased HPPMS ratio, the analysis of the coating properties and the tool performance will be carried out with the CemeCon AG. In addition, the tools and machined components will be subjected to extensive analyses to determine damage mechanisms, diffusion and oxidation processes. Due to it, initial wear behavior and the temperature of the tool cutting edge is measured under laboratory conditions during milling using a novel temperature measurement setup. Together with Schwegler Werkzeugfabrik GmbH & Co. KG and Karl-Heinz-Arnold GmbH, suitable tool geometries and substrate compositions will be developed and tested.
      25
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    SPP 2402: 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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