UDC 004.032.26(08)

NEURAL NETWORK CLASSIFICATION OF STEEL TYPES FOR HARDNESS PROPERTIES ESTIMATION BASED ON KINETIC INDENTATION

Kruglov Igor Aleksandrovich
National research nuclear university “MEPhI”
PhD in computer science

Abstract
This paper focuses on neural networks based estimation of metals hardness properties using indentation data. An original type of a neural networks ensemble is employed which involves regularization. The study is based on experimental data of four steel types. For each steel type a separate instance of the ensemble is built. To extend the developed method to multiple types of metals it is proposed to use a neural networks classifier for indentation diagrams. It is demonstrated that for an observed sample of experimental indentation diagrams such classifier can be constructed from simple perceptrons.

Category: 05.00.00 Technical sciences

Article reference:
Neural network classification of steel types for hardness properties estimation based on kinetic indentation // Modern scientific researches and innovations. 2015. № 8. P. 1 [Electronic journal]. URL: http://web.snauka.ru/en/issues/2015/08/57116

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