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Tool wear-dependent process analysis by means of a statistical online monitoring system

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Abstract

Simulating milling processes can provide numerous optimization possibilities regarding process stability and surface quality. In tool and die manufacturing often long-running processes are necessary. In contrast to very time-consuming Finite-Element-based approaches, geometric physically-based simulation systems allow predictions for such processes because of their relatively short runtime. The machining of hardened material and varying engagement conditions between the tool and the workpiece provoke a gradually increasing influence of tool wear on the cutting edges. To consider these alterations while simulating milling processes, different approaches can be used. Because of the complex characteristics of tool wear, methods, which result in an increased simulation runtime, have to be used for the geometric modeling of tool wear. In this paper, a novel approach for monitoring a milling process is presented, which utilizes an online-selection of pre-calculated simulation data to predict the process stability for different states of tool wear. To achieve this, measured data are compared to simulated data, which result from offline simulation conductions for each defined state of tool wear. As tool wear changes when the process is progressing, different simulation data for different states of tool wear have to be selected to ensure a valid stability prediction.

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Acknowledgements

This paper is based on investigations of the project DYNA-Tool: Efficiency improvement in machining of complex parts by optimizing the tool system dynamics, which is kindly supported by the Collective Research Networking (CORNET) and coordinated by the Forschungskuratorium Maschinenbau (FKM) and the Arbeitsgemeinschaft industrieller Forschungsvereinigungen (AiF) under Project no. 133 E.

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Authors and Affiliations

  1. Institute of Machining Technology (ISF), TU Dortmund University, Baroper Str. 303, 44227, Dortmund, Germany

    Felix Finkeldey, Stefan Hess & Petra Wiederkehr

  2. Virtual Machining, TU Dortmund University, Otto-Hahn-Str. 12, 44227, Dortmund, Germany

    Petra Wiederkehr

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  1. Felix Finkeldey
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  2. Stefan Hess
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  3. Petra Wiederkehr
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Correspondence to Felix Finkeldey.

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Finkeldey, F., Hess, S. & Wiederkehr, P. Tool wear-dependent process analysis by means of a statistical online monitoring system. Prod. Eng. Res. Devel. 11, 677–686 (2017). https://doi.org/10.1007/s11740-017-0773-0

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