Response classification of simple polycrystalline microstructures

Abstract A new method for approximate solution of mechanics problems is presented that uses a classifier to identify regions in a random heterogeneous material where stress is likely to be highly concentrated under a prescribed set of boundary conditions. The example problem studied is an aggregate of hexagonal grains, each modeled as orthotropic and linear elastic, and subject to uniaxial extension. It is shown that the Sobol’ decomposition can be used to determine which surrounding grains mechanical properties play the largest role in determining the average effective stress in any particular grain. It is also shown that the constituent functions of the Sobol’ decomposition determine a unique material pattern that corresponds to maximum stress concentration. A reduced order representation of the microstructure is developed that is in essence a projection of the microstructure description onto the material pattern. Finally, a classifier is developed that operates on this reduced order representation to predict the level of stress concentration. This classifier is shown to be over 90% accurate, and, when implemented in a moving window algorithm, to provide very good predictions of the subregions in a large microstructure where large stress concentration is likely.

[1]  Gerd Heber,et al.  Three-dimensional, parallel, finite element simulation of fatigue crack growth in a spiral bevel pinion gear , 2005 .

[2]  Sanjay R. Arwade,et al.  Random Composites Characterization Using a Classifier Model , 2007 .

[3]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[4]  I. Sobol,et al.  Global sensitivity indices for nonlinear mathematical models. Review , 2005 .

[5]  T. Iwakuma,et al.  Finite elastic-plastic deformation of polycrystalline metals , 1984, Proceedings of the Royal Society of London. A. Mathematical and Physical Sciences.

[6]  Carlos Armando Duarte,et al.  A Generalized Finite Element Method for polycrystals with discontinuous grain boundaries , 2006 .

[7]  M. Rashid Texture evolution and plastic response of two-dimensional polycrystals , 1992 .

[8]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[9]  Gorti B. Sarma,et al.  Texture predictions using a polycrystal plasticity model incorporating neighbor interactions , 1996 .

[10]  Mircea Grigoriu,et al.  PROBABILISTIC MODEL FOR POLYCRYSTALLINE MICROSTRUCTURES WITH APPLICATION TO INTERGRANULAR FRACTURE , 2004 .

[11]  Bruno Sudret,et al.  Global sensitivity analysis using polynomial chaos expansions , 2008, Reliab. Eng. Syst. Saf..

[12]  P. Dawson,et al.  An analysis of texture and plastic spin for planar polycrystals , 1993 .

[13]  P. Dawson,et al.  Modeling crystallographic texture evolution with finite elements over neo-Eulerian orientation spaces , 1998 .

[14]  R. Asaro,et al.  Geometrical effects in the inhomogeneous deformation of ductile single crystals , 1979 .

[15]  J. D. Eshelby The determination of the elastic field of an ellipsoidal inclusion, and related problems , 1957, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences.

[16]  T. C. T. Ting,et al.  Anisotropic Elasticity: Theory and Applications , 1996 .