Artificial neural network model for material characterization by indentation

Analytical methods to interpret the indentation load–displacement curves are difficult to formulate and solve due to material and geometric nonlinearities as well as complex contact interactions. In this study, large strain–large deformation finite element analyses were carried out to simulate indentation experiments. An artificial neural network model was constructed for the interpretation of indentation load–displacement curves. The data from finite element analyses were used to train and validate the artificial neural network model. The artificial neural network model was able to accurately determine the material properties when presented with the load–displacement curves that were not used in the training process. The proposed artificial neural network model is robust and directly relates the characteristics of the indentation load–displacement curve to the elasto-plastic material properties.

[1]  Ch. Tsakmakis,et al.  Determination of constitutive properties of thin metallic films on substrates by spherical indentation using neural networks , 2000 .

[2]  G. Pharr,et al.  An improved technique for determining hardness and elastic modulus using load and displacement sensing indentation experiments , 1992 .

[3]  Johann Michler,et al.  Determination of plastic properties of metals by instrumented indentation using different sharp indenters , 2003 .

[4]  K. Zeng,et al.  An analysis of load–penetration curves from instrumented indentation , 2001 .

[5]  Subra Suresh,et al.  DETERMINATION OF ELASTOPLASTIC PROPERTIES BY INSTRUMENTED SHARP INDENTATION , 1999 .

[6]  E. Tyulyukovskiy,et al.  A new loading history for identification of viscoplastic properties by spherical indentation , 2004 .

[7]  Huajian Gao,et al.  Identification of elastic-plastic material parameters from pyramidal indentation of thin films , 2002, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[8]  S. Suresh,et al.  Depth-sensing instrumented indentation with dual sharp indenters , 2003 .

[9]  Yang-Tse Cheng,et al.  Scaling approach to conical indentation in elastic-plastic solids with work hardening , 1998 .

[10]  Lu Shen,et al.  A new analysis of nanoindentation load-displacement curves , 2002 .

[11]  K. Zeng,et al.  Material characterization based on dual indenters , 2005 .

[12]  William D. Nix,et al.  A method for interpreting the data from depth-sensing indentation instruments , 1986 .

[13]  K. Zeng,et al.  Uniqueness of reverse analysis from conical indentation tests , 2004 .

[14]  Yang-Tse Cheng,et al.  Scaling relationships in conical indentation of elastic-perfectly plastic solids , 1999 .

[15]  M. J. Forrestal,et al.  Dynamic Spherical Cavity Expansion of Strain-Hardening Materials , 1991 .

[16]  Subra Suresh,et al.  Computational modeling of the forward and reverse problems in instrumented sharp indentation , 2001 .