Neural network utilization for evaluation of the steel material properties | Zendy

J. Špička | Zendy; Ladislav Kander | Zendy; Petr Čížek | Zendy

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Neural network utilization for evaluation of the steel material properties

Author(s) -

J. Špička,

Ladislav Kander,

Petr Čížek

Publication year - 2018

Publication title -

ubiquity proceedings

Language(s) - English

Resource type - Journals

ISSN - 2631-5602

DOI - 10.5334/uproc.45

Subject(s) - young's modulus , finite element method , material properties , artificial neural network , work (physics) , structural engineering , ultimate tensile strength , mechanical engineering , engineering , materials science , manufacturing engineering , computer science , composite material , machine learning

The aim of this work is to develop and test a new method for identification of material properties of the steel. This work deals with application of the small punch test for evaluation of material degradation of power station in the CEZ company (main Czech energetic company) within the project TE01020068 “Centre of research and experimental development of reliable energy production, work package 8: Research and development of new testing methods for evaluation of material properties”. The main effort is here an improvement of empirical correlation of selected steel materials used in power industry for manufacturing of the critical components (rotors, steam-pipes, etc.). The effort here is on the utilization of the finite element method (FEM) and the neural network (NN) for evaluation of mechanical properties (Young modulus of elasticity, yield stress, tensile strength) of the selected material, based on SPT results only.

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