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A Development of the Hole Drilling Method to Plasticity for a Thin Plate

Result description

The hole drilling method is a popular method for the residual stresses measuring. This method is developed only for elastic material properties. However, the stress concentration near the drilled hole can cause plasticity. This paper describes a development to the plasticity by using a neural network. The function of the neural network is an approximator between measured strains and evaluated residual stresses. The neural network is trained by values strains - stresses (inputs - outputs) obtained by a finite element method by using the program ANSYS. This development is performed for the thin plate.

Keywords

Residual stresseshole-drilling methodneural networkplasticityFEM

The result's identifiers

Alternative languages

  • Result language

    čeština

  • Original language name

    Rozvoj metody vrtání otvoru do oblasti pružně plastických deformací v tenké stěně

  • Original language description

    The hole drilling method is a popular method for the residual stresses measuring. This method is developed only for elastic material properties. However, the stress concentration near the drilled hole can cause plasticity. This paper describes a development to the plasticity by using a neural network. The function of the neural network is an approximator between measured strains and evaluated residual stresses. The neural network is trained by values strains - stresses (inputs - outputs) obtained by a finite element method by using the program ANSYS. This development is performed for the thin plate.

  • Czech name

    Rozvoj metody vrtání otvoru do oblasti pružně plastických deformací v tenké stěně

  • Czech description

    The hole drilling method is a popular method for the residual stresses measuring. This method is developed only for elastic material properties. However, the stress concentration near the drilled hole can cause plasticity. This paper describes a development to the plasticity by using a neural network. The function of the neural network is an approximator between measured strains and evaluated residual stresses. The neural network is trained by values strains - stresses (inputs - outputs) obtained by a finite element method by using the program ANSYS. This development is performed for the thin plate.

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    JR - Other machinery industry

  • OECD FORD branch

Result continuities

Others

  • Publication year

    2005

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Experimentální analýza napětí 2005

  • ISBN

    80-214-2941-0

  • ISSN

  • e-ISSN

  • Number of pages

    10

  • Pages from-to

    87-96

  • Publisher name

    Institute of Solid Mechanics, Mechatronics and Biomechanics, Faculty of Mechanical Engineering, Brno University of Technology

  • Place of publication

    Skalský dvůr, Czech Republic

  • Event location

    Skalský Dvůr

  • Event date

    Jun 7, 2005

  • Type of event by nationality

    EUR - Evropská akce

  • UT code for WoS article