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Research Papers

Data-Driven Design Optimization for Composite Material Characterization

[+] Author and Article Information
John G. Michopoulos

Naval Research Laboratory, Center of Computational Material Science,  Computational Multiphysics Systems Laboratory, Washington, DC 20375

John C. Hermanson

USDA Forest Service,  Forest Products Laboratory, Engineering Properties of Wood, Wood Based Materials, and Structures, Madison, WI 53726

Athanasios Iliopoulos

 Science Applications Int. Corp. Resident at Naval Research Laboratory, Center of Computational Material Science, Computational Multiphysics Systems Laboratory, Washington DC 20375

Samuel G. Lambrakos

Naval Research Laboratory, Center of Computational Material Science,  Computational Multiphysics Systems Laboratory, Washington DC 20375

Tomonari Furukawa

 Virginia Polytechnic Institute and State University, Department of Mechanical Engineering, Computational Multiphysics Systems Laboratory, Danville, VA 24540

J. Comput. Inf. Sci. Eng 11(2), 021009 (Jun 22, 2011) (11 pages) doi:10.1115/1.3595561 History: Received October 29, 2010; Revised April 12, 2011; Published June 22, 2011; Online June 22, 2011

The main goal of the present paper is to demonstrate the value of design optimization beyond its use for structural shape determination in the realm of the constitutive characterization of anisotropic material systems such as polymer matrix composites with or without damage. The approaches discussed are based on the availability of massive experimental data representing the excitation and response behavior of specimens tested by automated mechatronic material testing systems capable of applying multiaxial loading. Material constitutive characterization is achieved by minimizing the difference between experimentally measured and analytically computed system responses as described by surface strain and strain energy density fields. Small and large strain formulations based on additive strain energy density decompositions are introduced and utilized for constructing the necessary objective functions and their subsequent minimization. Numerical examples based on both synthetic (for one-dimensional systems) and actual data (for realistic 3D material systems) demonstrate the successful application of design optimization for constitutive characterization.

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Copyright © 2011 by American Society of Mechanical Engineers
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Figures

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Figure 1

NRL66.3: Most recent 6-DoF mechatronically automated system for the multiaxial testing of materials

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Figure 2

Systemic outline of design optimization for constitutive material characterization

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Figure 3

Difference of 1- and 2-term approximations of normalized irrecoverable or dissipated energy density as a function of strain relative to the exact model

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Figure 4

Distribution of elastic, dissipated (or irrecoverable) and total SEDs for one-dimensional case (a) and corresponding stress distributions as a function of strain (b)

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Figure 5

Discretization and boundary conditions of typical specimen used for material characterization (a) and detail of FEA model near one of the notches (b)

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Figure 6

Distributions of the vertical component of strain (ɛyy ) as determined experimentally via MRGM (a) and the corresponding matched distribution of the numerical model as it was produced by FEA (b) for the purpose of determining the five elastic constants of AS4/3506-1 composite lamina material

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Figure 7

Decay of the objective function versus iteration number

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Figure 8

Comparison between the small strain formulation FEA results of the vertical component of strain (ɛyy ) (a) and the corresponding finite formulation results (b) for the case of a specimen loaded both in tension and torsion

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Figure 9

Comparison of the load versus the vertical component of strain (ɛyy ) at a point in front of the notch, between the target and the identified model by using the FSF

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