Publication detail

Prediction of fracture toughness transition from tensile test data using artificial neural networks

AL KHADDOUR, S. STRATIL, L. VÁLKA, L. DLOUHÝ, I.

Original Title

Prediction of fracture toughness transition from tensile test data using artificial neural networks

English Title

Prediction of fracture toughness transition from tensile test data using artificial neural networks

Type

conference paper

Language

en

Original Abstract

The aim of this paper is develop prediction procedure for the fracture toughness transition from tensile test data using artificial neural networks. In total 29 experimental data sets from low alloy steels are applied to validate the model of reference temperature prediction. The tensile tests have been done at general yield temperature of circumferential notched tensile tests (purely brittle fracture temperature) and at room temperature (purely ductile fracture temperature). To build the model, all parameters of tensile test and hardness values were used as input variables. The study indicates that the reference temperature characterizing the fracture toughness transition behaviour in low alloy steels with predominantly ferritic structure is predictable on the basis of selected characteristics of tensile test.

English abstract

The aim of this paper is develop prediction procedure for the fracture toughness transition from tensile test data using artificial neural networks. In total 29 experimental data sets from low alloy steels are applied to validate the model of reference temperature prediction. The tensile tests have been done at general yield temperature of circumferential notched tensile tests (purely brittle fracture temperature) and at room temperature (purely ductile fracture temperature). To build the model, all parameters of tensile test and hardness values were used as input variables. The study indicates that the reference temperature characterizing the fracture toughness transition behaviour in low alloy steels with predominantly ferritic structure is predictable on the basis of selected characteristics of tensile test.

Keywords

steels; fracture toughness; tensile test; artificial neural networks; reference temperature

Released

02.06.2016

Publisher

Brno University of Technology

Location

Brno

ISBN

978-80-214-5358-6

Book

MULTI-SCALE DESIGN OF ADVANCED MATERIALS - CONFERENCE PROCEEDINGS

Edition number

1

Pages from

79

Pages to

86

Pages count

8

Documents

BibTex


@inproceedings{BUT126186,
  author="Samer {Al Khaddour} and Luděk {Stratil} and Libor {Válka} and Ivo {Dlouhý}",
  title="Prediction of fracture toughness transition from tensile test data using artificial neural networks",
  annote="The aim of this paper is develop prediction procedure for the fracture toughness transition from tensile test data using artificial neural networks. In total 29 experimental data sets from low alloy steels are applied to validate the model of reference temperature prediction. The tensile tests have been done at general yield temperature of circumferential notched tensile tests (purely brittle fracture temperature) and at room temperature (purely ductile fracture temperature). To build the model, all parameters of tensile test and hardness values were used as input variables. The study indicates that the reference temperature characterizing the fracture toughness transition behaviour in low alloy steels with predominantly ferritic structure is predictable on the basis of selected characteristics of tensile test.",
  address="Brno University of Technology",
  booktitle="MULTI-SCALE DESIGN OF ADVANCED MATERIALS - CONFERENCE PROCEEDINGS",
  chapter="126186",
  howpublished="online",
  institution="Brno University of Technology",
  year="2016",
  month="june",
  pages="79--86",
  publisher="Brno University of Technology",
  type="conference paper"
}