Detail publikace

Material parameters estimation of Small Punch test by two variants of genetic algorithms

Originální název

Material parameters estimation of Small Punch test by two variants of genetic algorithms

Anglický název

Material parameters estimation of Small Punch test by two variants of genetic algorithms

Jazyk

en

Originální abstrakt

The issue of material data and obtaining parameters is fundamental to all engineering applications. To perform required analyses and obtain relevant results it is necessary to have sufficient input data and information. For designing and solving a given problem it is crucial to know material properties. We have several methods for obtaining material data. One method is the Small Punch Test (SPT). This method is considered non-destructive. The experimental samples for SPT have small dimensions and therefore can be obtained from a tested structure without significant destruction. When respecting certain assumptions, this method can be considered equivalent to tensile experimental procedure. By using this method it is possible to measure the same parameters as when using a tensile test. The typical results of the SPT measurements are force-deflection curves. It is possible to use numerical simulation to convert a force-deflection curve to a stress strain curve. This paper presents two methods of determining suitable material parameters for transformation of force-deflection curves to stress-strain curve for defined steel at elevated temperature at the Ansys Workbench. To determine the appropriate transformation, two types of material models (Ramberg-Osgood model, Chaboche model) and two variants of genetic algorithms were used. For the first variant, the optimization toolbox implemented in the Ansys Workbench was used. For the second variant, a genetic algorithm from the Python scripting language into the Ansys Workbench was implemented. The presented results show the advantages and disadvantages of considered methods and it is possible to select a method for many engineering applications. The estimation of the material parameters were studied for steel with a higher content of silicon.

Anglický abstrakt

The issue of material data and obtaining parameters is fundamental to all engineering applications. To perform required analyses and obtain relevant results it is necessary to have sufficient input data and information. For designing and solving a given problem it is crucial to know material properties. We have several methods for obtaining material data. One method is the Small Punch Test (SPT). This method is considered non-destructive. The experimental samples for SPT have small dimensions and therefore can be obtained from a tested structure without significant destruction. When respecting certain assumptions, this method can be considered equivalent to tensile experimental procedure. By using this method it is possible to measure the same parameters as when using a tensile test. The typical results of the SPT measurements are force-deflection curves. It is possible to use numerical simulation to convert a force-deflection curve to a stress strain curve. This paper presents two methods of determining suitable material parameters for transformation of force-deflection curves to stress-strain curve for defined steel at elevated temperature at the Ansys Workbench. To determine the appropriate transformation, two types of material models (Ramberg-Osgood model, Chaboche model) and two variants of genetic algorithms were used. For the first variant, the optimization toolbox implemented in the Ansys Workbench was used. For the second variant, a genetic algorithm from the Python scripting language into the Ansys Workbench was implemented. The presented results show the advantages and disadvantages of considered methods and it is possible to select a method for many engineering applications. The estimation of the material parameters were studied for steel with a higher content of silicon.

Dokumenty

BibTex


@inproceedings{BUT111496,
  author="Jozef {Hrabovský} and Petr {Lošák} and Jaroslav {Horský}",
  title="Material parameters estimation of Small Punch test by two variants of genetic algorithms",
  annote="The issue of material data and obtaining parameters is fundamental to all engineering
applications. To perform required analyses and obtain relevant results it is necessary to have
sufficient input data and information. For designing and solving a given problem it is crucial to
know material properties. We have several methods for obtaining material data. One method
is the Small Punch Test (SPT). This method is considered non-destructive. The experimental
samples for SPT have small dimensions and therefore can be obtained from a tested
structure without significant destruction. When respecting certain assumptions, this method
can be considered equivalent to tensile experimental procedure. By using this method it is
possible to measure the same parameters as when using a tensile test. The typical results of
the SPT measurements are force-deflection curves. It is possible to use numerical simulation
to convert a force-deflection curve to a stress strain curve. This paper presents two methods
of determining suitable material parameters for transformation of force-deflection curves to
stress-strain curve for defined steel at elevated temperature at the Ansys Workbench. To
determine the appropriate transformation, two types of material models (Ramberg-Osgood
model, Chaboche model) and two variants of genetic algorithms were used. For the first
variant, the optimization toolbox implemented in the Ansys Workbench was used. For the
second variant, a genetic algorithm from the Python scripting language into the Ansys
Workbench was implemented. The presented results show the advantages and
disadvantages of considered methods and it is possible to select a method for many
engineering applications. The estimation of the material parameters were studied for steel
with a higher content of silicon.",
  address="SVS FEM s.r.o.",
  booktitle="22nd SVSFEM ANSYS Users' Group Meeting and Conference 2014",
  chapter="111496",
  edition="1",
  howpublished="online",
  institution="SVS FEM s.r.o.",
  year="2014",
  month="june",
  pages="57--65",
  publisher="SVS FEM s.r.o.",
  type="conference paper"
}