Detail publikace

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

Originální název

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

Anglický název

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

Jazyk

en

Originální abstrakt

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.

Anglický abstrakt

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.

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"
}