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Modeling, Simulation and Optimization of Multi-Frequency Induction Hardening

Working Group:WG Numerics of PDEs
Leadership: Prof. Dr. Alfred Schmidt ((0421) 218-63851, E-Mail: alfred.schmidt@uni-bremen.de )
Processor: Dr. Qingzhe Liu
Dr.-Ing. Jonathan Montalvo Urquizo
Funding: BMBF
Project partner: IWT, Stiftung Institut für Werkstofftechnik
Weierstraß-Institut (Berlin)
Institut für Mathematik, Universität Augsburg
Time period: 01.01.2011 - 31.12.2013
Website:http://www.me-fre-sim.de/index.php?lang=en
Bild des Projekts Modellierung, Simulation und Optimierung des Mehrfrequenzverfahrens für die Induktive Wärmebehandlung

The aim of the project is the modeling, simulation and optimization of multi-frequency hardening for gears made of steel. From industrial viewpoint one major task is the development of a software tool which allows for a detailed, workpiece related specification of the machine during the project planning phase. On the other hand it should be possible to compute optimal process parameters for industrial application in the process chain.

For a practical realization of the research results one has to ensure that and also how the computed optimal material parameters can be realized on the machine. Since the main application of the multi-frequency technology is for gear parts, corresponding three dimensional geometries of the workpiece must be considered. The chemical composition of the same steel in different charges undergoes considerable variations, also the initial microstructure varies. Therefore, it is important to estimate the effects of these variations on the process result.


Publications

  1. D. Hömberg, Q. Liu, J. Montalvo Urquizo, D. Nadolski, T. Petzold, A. Schmidt, A. Schulz.
    Simulation of multi-frequency-induction-hardening including phase transitions and mechanical effects.
    Finite Elements in Analysis and Design, Vol 121:86-100, Elsevier, 2016.
  2. J. Montalvo Urquizo, Q. Liu, A. Schmidt.
    Simulation of quenching involved in induction hardening including mechanical eff ects.
    Computational Materials Science, 79:639-649, Elsevier, 2013.

    DOI: 10.1016/j.commatsci.2013.06.058