Engineering Neutron Diffraction Data Analysis with Inverse Neural Network Modeling

Abstract:

Article Preview

Integration of engineering neutron diffraction data analysis and solid mechanics modeling is a powerful method to deduce in-situ constitutive behavior of materials. Since diffraction data originates from spatially discrete subsets of the material volume, extrapolation of the data to the behavior of the overall sample is non-trivial. The finite element model has been widely used for interpreting diffraction data by optimizing model parameters via iterative processes. In order to maximize the rigor of such analysis and to increase fitting efficiency and accuracy, we have developed an optimization algorithm based on the neural network architecture. The inverse neural network model reveals parameter sensitivity quantitatively during a training process, and yields more accurate phase specific constitutive laws of the composite materials compared to the conventional method once networks are successfully trained.

Info:

Periodical:

Edited by:

Heinz Günter Brokmeier, Martin Müller, P. Klaus Pranzas, Andreas Schreyer and Peter Staron

Pages:

39-44

Citation:

B. Denizer et al., "Engineering Neutron Diffraction Data Analysis with Inverse Neural Network Modeling", Materials Science Forum, Vol. 772, pp. 39-44, 2014

Online since:

November 2013

Export:

Price:

$41.00

[1] X.L. Wang, The application of neutron diffraction to engineering problems, Jom, 58 (2006) 52-57.

[2] T.M. Holden, A Canadian perspective on engineering strain measurements by neutron diffraction, Can. J. Phys., 88 (2010) 799-808.

[3] S.S. Cross, R.F. Harrison, R.L. Kennedy, Introduction to neural networks, Lancet, 346 (1995) 1075-1079.

[4] G.Q. Zhang, B.E. Patuwo, M.Y. Hu, Forecasting with artificial neural networks: The state of the art, Int. J. Forecast., 14 (1998) 35-62.

[5] C.M. Bishop, Neural networks and their applications, Rev. Sci. Instrum., 65 (1994) 1803-1832.

[6] D.E. Rumelhart, G.E. Hinton, R.J. Williams, Learning representations by back-propagating errors, Nature, 323 (1986) 533-536.

DOI: https://doi.org/10.1038/323533a0

[7] D. Dragoi, E. Ustundag, B. Clausen, M.A.M. Bourke, Investigation of thermal residual stresses in tungsten-fiber/bulk metallic glass matrix composites, Scr. Mater., 45 (2001) 245-252.

DOI: https://doi.org/10.1016/s1359-6462(01)01031-4

[8] B. Clausen, S.Y. Lee, E. Ustundag, C.C. Aydiner, R.D. Conner, M.A.M. Bourke, Compressive yielding of tungsten fiber reinforced bulk metallic glass composites, Scr. Mater., 49 (2003) 123-128.

DOI: https://doi.org/10.1016/s1359-6462(03)00237-9

[9] E. Voce, The relationship between stress and strain for homogeneous deformation, Journal of the Institute of Metals, 74 (1948) 537-562.

[10] J.H. Hollomon, Tensile deformation, Transactions of the American Institute of Mining and Metallurgical Engineers, 162 (1945) 268-290.