Fraunhofer-Institut für Produktionsanlagen und Konstruktionstechnik IPK

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  • Publication
    Acoustic emission-based process monitoring in the milling of carbon fibre-reinforced plastics
    ( 2022)
    Uhlmann, E.
    ;
    Holznagel, Tobias
    Milling of fibre-reinforced plastics is a challenging task. The highly abrasive fibres lead to high tool wear and coating failures, which cause increasing process forces and temperatures. Machining with a worn tool, in turn, can result in unwanted workpiece damages such as delamination or fibre protrusion. Reliable monitoring of the process must therefore be able to detect damages to the milling tool and the workpiece alike. The presented process monitoring approach measures the acoustic emission generated by the milling tool cutting edge entering the workpiece with a sensor attached to the tool holder. Specific acoustic emission frequency spectra and waveforms are emitted in the cutting zone for different tool wear states. Coating failures as well as other acoustic emission events due to workpiece damages can be robustly detected and distinguished by feature extraction and signal processing as well. The developed setup, the monitoring parameterisation techniques and signal processing algorithms as well as experimental and monitoring results are presented and discussed in this paper.
  • Publication
    Practical Approaches for Acoustic Emission Attenuation Modelling to Enable the Process Monitoring of CFRP Machining
    ( 2022)
    Uhlmann, Eckart
    ;
    Holznagel, Tobias
    ;
    Clemens, Robin
    Acoustic emission-based monitoring of the milling process holds the potential to detect undesired damages of fibre-reinforced plastic workpieces, such as delamination or matrix cracking. In addition, abrasive tool wear, tool breakage, or coating failures can be detected. As measurements of the acoustic emission are impacted by attenuation, dispersion, and reflection as it propagates from source to sensor, the waveforms, amplitudes, and frequency content of a wave packet differ depending on the propagation length in the workpiece. Since the distance between acoustic emission sources and a stationary sensor attached to the workpiece changes continually in circumferential milling, the extraction of meaningful information from the raw measurement data is challenging and requires appropriate signal processing and frequency-dependent amplification. In this paper, practical and robust approaches, namely experimentally identified transfer functions and frequency gain parameter tables for attenuation modelling, which in reverse enable the reconstruction of frequency spectra emitted at the acoustic emission source, are presented and discussed. From the results, it is concluded that linear signal processing can largely compensate for the influence of attenuation, dispersion, and reflection on the frequency spectra and can therefore enable acoustic emission based process monitoring.
  • Publication
    Machine Learning of Surface Layer Property Prediction for Milling Operations
    ( 2021)
    Uhlmann, Eckart
    ;
    Holznagel, Tobias
    ;
    Schehl, Philipp
    ;
    Bode, Yannick
    Tool wear and cutting parameters have a significant effect on the surface layer properties in milling. Since the relation between tool wear, cutting parameters, and surface layer properties is mostly unknown, the latter cannot be controlled during production and may vary from part to part as tool wear progresses. To account for this uncertainty and to prevent premature failure, components often need to be oversized or surface layer properties need to be adjusted in subsequent manufacturing processes. Several approaches have been made to obtain models that predict the surface layer properties induced by manufacturing processes. However, those approaches need to be calibrated with a considerable number of experimental trials. As trials are time-consuming and surface layer measurements are laborious, no industrial applications have been realized. Complex models have one major drawback. They have to be re-parameterized as soon as process characteristics change. Therefore, manual experimental parameterization does not appear to be a feasible approach for industrial application. A highly automated approach for the machine learning of the relation between tool wear, cutting parameters and surface layer properties is presented in this paper. The amount of obtained measurement data allows a fundamental analysis of the approach, which paves the way for further developments.
  • Publication
    Online process control and self-configuration of turning operations
    ( 2020)
    Uhlmann, Eckart
    ;
    Holznagel, Tobias
    ;
    Alavi, Raheel Masood
    The surface roughness and the process efficiency may vary significantly in a turning process due to inevitable tool wear. Online process controls can therefore be employed in order to compensate for the influence of tool wear by adapting critical cutting parameters. In this study, the cutting parameters are adapted using a multiple-input, multiple-output controller to achieve reduced tool wear rates and high process efficiency while maintaining the surface roughness within defined limits. The measurement setup, controller design, self-configuration algorithm and a comparison of efficiencies of controlled and conventional machining are discussed.
  • Publication
    Constant surface roughness over tool-lifetime due to online process monitoring and cutting parameter adaption in turning of gear steel
    ( 2020)
    Uhlmann, Eckart
    ;
    Holznagel, Tobias
    ;
    Berardi, Patrick
    High process forces and temperatures in turning operations cause high tool wear rates. Tool wear such as flank face abrasion has direct impact on workpiece geometry and resulting surface roughness. Since tools are used until tool life criterion is reached, surface quality can vary widely over the workpiece even when constant cutting parameters are utilised. A measurement system based on laser triangulation has been developed which enables the online measurement of surface roughness on the workpiece during the turning process. Using the online surface roughness measurements, closed-loop controllers were developed in order to adapt the tool feed and the cutting velocity to retain constant surface roughness even when tool wear is progressing. An optimised process with constant cutting parameters was benchmarked to the developed processes with adaptive cutting parameters. It can be shown that parameter adaption has the potential to lead to efficient processes and increases the tool-lifetime TToollife significantly.