Numéro
J. Phys. IV France
Volume 120, December 2004
Page(s) 355 - 362
DOI http://dx.doi.org/10.1051/jp4:2004120040


J. Phys. IV France 120 (2004) 355-362

DOI: 10.1051/jp4:2004120040

Nitriding parameters analized by neural network and genetic algorithm

T. Filetin, I. Zmak and D. Novak

Faculty of Mechanical Engineering and Naval Architecture, Department of Materials, University of Zagreb, I. Lucica 5, 10000 Zagreb, Croatia

tomislav.filetin@fsb.hr
irena.zmak@fsb.hr
davor.novak@fsb.hr

Abstract
The surface hardness and hardness profile of a nitrided workpiece depend on the chemical composition of the steel, nitriding temperature and time, and on type of the nitriding process (i.e. atmosphere). An issue in this approach was to test how the statistical analysis, artificial neural network, genetic algorithm and genetic programming may be used for determination of nitriding time and surface hardness, in case when the chemical composition of steel, nitriding temperature and required thickness of nitrided layer are known. In the neural network learning procedure datasets of results were used, after nitriding 5 different steel grades. Different combinations of time, temperature, surface hardness and thickness of plasma and gas nitriding layer are compiled from the experiments and industrial experience and also from the literature. The static multi-layer feed-forward neural network is proposed. To accelerate the convergence of the proposed static error-back propagation learning algorithm, the momentum method is applied. The mean error between experimental data of nitriding time and data predicted using a neural network, and also the standard deviation for both the learning and the testing dataset is found to be small and acceptable. The determination of time by genetic algorithm gives greater standard deviation than by using neural network. Determining surface hardness after nitriding by the use of neural network gives less reliable results due to relatively imprecise input data and a narrow learning dataset. Due to nitriding data insufficiency, the network was tested only with the learning dataset.



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