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

21st Chemnitz Seminar on Materials Engineering – 21. Werkstofftechnisches Kolloquium

Pribbenow, J.^1 ; Mejauschek, M.^2 ; Landgraf, P.^1 ; Grund, T.^1 ; Braüer, G.^2 ; Lampke, T.^1

Chemnitz University of Technology, Materials and Surface Engineering Group, Germany^1

Fraunhofer Institute for Surface Engineering and Thin Films, Germany^2

关键词: Back propagation neural networks;    Chemical compositions;    Indentation measurements;    Levenberg-Marquardt algorithm;    Neuron activation function;    Nitriding parameters;    Thermochemical treatments;    Weight distributions;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/480/1/012019/pdf
DOI  :  10.1088/1757-899X/480/1/012019

来源: IOP

【 摘 要 】

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.