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Neural network for prediction of hardness profiles for steel alloys after plasma nitriding

: Pribbenow, J.; Mejauschek, M.; Landgraf, P.; Grund, T.; Bräuer, G.; Lampke, T.

Fulltext ()

Lampke, T. ; Institute of Physics -IOP-, London; TU Chemnitz, Institut für Werkstoffwissenschaft und Werkstofftechnik:
21st Chemnitz Seminar on Materials Engineering - 21. Werkstofftechnisches Kolloquium 2019 : 6-7 March 2019, Chemnitz, Germany
Bristol: IOP Publishing, 2019 (IOP conference series. Materials science and engineering 480)
ISBN: 978-3-00-062158-1
Art. 012019, 6 pp.
Seminar on Materials Engineering <21, 2019, Chemnitz>
Werkstofftechnisches Kolloquium <21, 2019, Chemnitz>
Conference Paper, Electronic Publication
Fraunhofer IST ()

Plasma nitriding is a state-of-the-art thermochemical treatment, which is used by the processing industry for hardening of the surface-near material volumes of tools and components. An artificial neural network, particularly backpropagation neural network, has been developed to investigate the hardness behavior of different steel types according to various process parameters. Therefor eleven steels with different chemical compositions were plasma nitrated with different concentrations of nitrogen at various temperatures. The hardness profiles were studied with indentation measurement. The experimental data were used to train the neural network with the Levenberg-Marquardt algorithm. For an optimal training, weight distribution, hidden neurons and gradients of neuron activation functions were varied, to allow simulations of hardness profiles for the different types of steel with deviant nitriding parameters.