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Identification of viscoplastic material parameters from spherical indentation data: Part II. Experimental validation of the method

  • A neural network-based analysis method for the identification of a viscoplasticity model from spherical indentation data, developed in the first part of this work [J. Mater. Res. 21, (2006)], was applied for different metallic materials. Besides the comparison of typical parameters like Young's modulus and yield stress with values from tensile experiments, the uncertainties in the identified material parameters representing modulus, hardening behavior, and viscosity were investigated in relation to different sources. Variations in the indentation position, tip radius, force application rate, and surface preparation were considered. The extensive experimental validation showed that the applied neural networks are very robust and show small variation coefficients, especially regarding the important parameters of Young's modulus and yield stress. On the other hand, important requirements were quantified, which included a very good spherical indenter geometry and good surface preparationA neural network-based analysis method for the identification of a viscoplasticity model from spherical indentation data, developed in the first part of this work [J. Mater. Res. 21, (2006)], was applied for different metallic materials. Besides the comparison of typical parameters like Young's modulus and yield stress with values from tensile experiments, the uncertainties in the identified material parameters representing modulus, hardening behavior, and viscosity were investigated in relation to different sources. Variations in the indentation position, tip radius, force application rate, and surface preparation were considered. The extensive experimental validation showed that the applied neural networks are very robust and show small variation coefficients, especially regarding the important parameters of Young's modulus and yield stress. On the other hand, important requirements were quantified, which included a very good spherical indenter geometry and good surface preparation to obtain reliable results.zeige mehrzeige weniger

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Metadaten
Autor*innen:Daniel Reese, Christian Ullner, E. Tyulyukovskiy, N. Huber
Dokumenttyp:Zeitschriftenartikel
Veröffentlichungsform:Verlagsliteratur
Sprache:Englisch
Titel des übergeordneten Werkes (Englisch):Journal of materials research
Jahr der Erstveröffentlichung:2006
Verlag:Materials Research Society
Verlagsort:Warrendale, Pa.
Jahrgang/Band:21
Ausgabe/Heft:3
Erste Seite:677
Letzte Seite:684
Freie Schlagwörter:Neuronale Netze; Spannungs-Dehnungs-Kurve; Validierung; Viskoplastizität
DOI:10.1557/JMR.2006.0077
ISSN:0884-2914
Bemerkung:
Geburtsname von Reese, Daniel: Klötzer, D. -  Birth name of Reese, Daniel: Klötzer, D.
Verfügbarkeit des Dokuments:Physisches Exemplar in der Bibliothek der BAM vorhanden ("Hardcopy Access")
Bibliotheksstandort:Sonderstandort: Publica-Schrank
Datum der Freischaltung:19.02.2016
Referierte Publikation:Ja
Datum der Eintragung als referierte Publikation:20.04.2006
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