Artificial neural network and finite element modeling of nanoindentation tests

Muliana et al present a study that offers two-dimensional (2-D) axisymmetric and three-dimensional (3-D) finite element (FE) models of nanoindentation tests. These models are shown to accurately predict the load-displacement behavior of a nonlinear homogeneous material as well as one with a hard fil...

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Veröffentlicht in:	Metallurgical and materials transactions. A, Physical metallurgy and materials science Physical metallurgy and materials science, 2002-07, Vol.33 (7), p.1939-1947
Hauptverfasser:	MULIANA, Anastasia, STEWARD, Rejanah, HAJ-ALI, Rami M, SAXENA, Ashok
Format:	Artikel
Sprache:	eng
Schlagworte:	Annealing Applied sciences Artificial neural networks Condensed matter: structure, mechanical and thermal properties Copper Displacement Exact sciences and technology Finite element analysis Finite element method Hardness tests Mathematical models Mechanical and acoustical properties of condensed matter Mechanical properties of solids Metallurgy Metals. Metallurgy Nanoindentation Neural networks Oxide coatings Physics Substrates Tribology and hardness
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Beschreibung
Zusammenfassung:	Muliana et al present a study that offers two-dimensional (2-D) axisymmetric and three-dimensional (3-D) finite element (FE) models of nanoindentation tests. These models are shown to accurately predict the load-displacement behavior of a nonlinear homogeneous material as well as one with a hard film, such as an oxide film on a relatively soft substrate.
ISSN:	1073-5623 1543-1940
DOI:	10.1007/s11661-002-0027-3