Detail publikace

Neural network prediction of fracture toughness from tensile test

Originální název

Neural network prediction of fracture toughness from tensile test

Anglický název

Neural network prediction of fracture toughness from tensile test

Jazyk

en

Originální abstrakt

The reference temperature localizing the fracture toughness temperature diagram on temperature axis was predicted based on tensile test data. Regularization neural network was developed to solve the correlation between these properties. First of all standard methodology of testing was applied to determine fracture toughness from three-point bend specimens. The fracture toughness transition dependence was quantified by means of master curve concept enabling to represent it using one parameter, i.e. reference temperature. The reference temperature was calculated applying the multi-temperature method. In next the different strength and deformation characteristics and parameters were determined from standard tensile specimens focusing on data from localized deformation during specimen necking. Tensile samples with circumferential notch were also examined. In total 29 data sets from low-alloy steels were applied for the analyses. A very promising correlation of predicted and experimentally determined values of reference temperature was found. Further analysis is however needed to increase the accuracy of predicted values and decrease the quantity of input data

Anglický abstrakt

The reference temperature localizing the fracture toughness temperature diagram on temperature axis was predicted based on tensile test data. Regularization neural network was developed to solve the correlation between these properties. First of all standard methodology of testing was applied to determine fracture toughness from three-point bend specimens. The fracture toughness transition dependence was quantified by means of master curve concept enabling to represent it using one parameter, i.e. reference temperature. The reference temperature was calculated applying the multi-temperature method. In next the different strength and deformation characteristics and parameters were determined from standard tensile specimens focusing on data from localized deformation during specimen necking. Tensile samples with circumferential notch were also examined. In total 29 data sets from low-alloy steels were applied for the analyses. A very promising correlation of predicted and experimentally determined values of reference temperature was found. Further analysis is however needed to increase the accuracy of predicted values and decrease the quantity of input data

BibTex


@misc{BUT61115,
  author="Luděk {Stratil} and Samer {Al Khaddour} and Hynek {Hadraba} and Ivo {Dlouhý}",
  title="Neural network prediction of fracture toughness from tensile test",
  annote="The reference temperature localizing the fracture toughness temperature diagram on temperature axis was predicted based on tensile test data. Regularization neural network was developed to solve the correlation between these properties. First of all standard methodology of testing was applied to determine fracture toughness from three-point bend specimens. The fracture toughness transition dependence was quantified by means of master curve concept enabling to represent it using one parameter, i.e. reference temperature. The reference temperature was calculated applying the multi-temperature method. In next the different strength and deformation characteristics and parameters were determined from standard tensile specimens focusing on data from localized deformation during specimen necking. Tensile samples with circumferential notch were also examined. In total 29 data sets from low-alloy steels were applied for the analyses. A very promising correlation of predicted and experimentally determined values of reference temperature was found.  Further analysis is however needed to increase the accuracy of predicted values and decrease the quantity of input data",
  chapter="61115",
  year="2010",
  month="october",
  type="abstract"
}