Neural networks committee for improvement of metal's mechanical properties estimates

Authors:
Olga A. Mishulina;Igor A. Kruglov;Murat B. Bakirov
Affiliations:
National nuclear research university "MEPhI", Moscow, Russia;National nuclear research university "MEPhI", Moscow, Russia;"Center of material science and resource", Moscow, Russia
Venue:
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part I
Year:
2011

Citing 1
Cited 3

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Neural Networks: A Comprehensive Foundation

Neural networks committee decision making for estimation of metal's hardness properties from indentation data

Optical Memory and Neural Networks
Quantile based decision making rule of the neural networks committee for ill-posed approximation problems

Neurocomputing
Neural network modeling of vector multivariable functions in ill-posed approximation problems

Journal of Computer and Systems Sciences International

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Abstract

In this paper we discuss the problem of metal's mechanical characteristics estimation on the basis of indentation curves. The solution of this problem makes it possible to unify computational and experimental control methods of elastic properties of materials at all stages of equipment life cycle (manufacturing, maintenance, reparation). Preliminary experiments based on data obtained by the use of finite element analysis method have proved this problem to be ill-posed and impossible to be solved by a single multilayered perceptron at the required precision level. To improve the accuracy of the estimates we propose to use a special neural net structure for the neural networks committee decision making. Experimental results have shown accuracy improvement for estimates produced by the neural networks committee and confirmed their stability.