Towards the robotic characterization of the constitutive response of composite materials

A historical and technical overview of a paradigm for automating research procedures on the area of constitutive identification of composite materials is presented. Computationally controlled robotic, multiple degree-of-freedom mechatronic systems are used to accelerate the rate of performing data-collecting experiments along loading paths defined in multidimensional loading spaces. The collected data are utilized for the inexpensive data-driven determination of bulk material non-linear constitutive behavior models as a consequence of generalized loading through parameter identification/estimation methodologies based on the inverse approach. The concept of the dissipated energy density is utilized as the representative encapsulation of the non-linear part of the constitutive response that is responsible for the irreversible character of the overall behavior. Demonstrations of this paradigm are given for the cases of polymer matrix composite materials systems. Finally, this computational and mechatronic infrastructure is used to create conceptual, analytical and computational models for describing and predicting material and structural performance.

[1]  John G. Michopoulos,et al.  Characterization of strain-induced damage in composites based on the dissipated energy density Part III. General material constitutive relation , 1995 .

[2]  Wing Kam Liu,et al.  Nonlinear Finite Elements for Continua and Structures , 2000 .

[3]  Tomonari Furukawa,et al.  Multi-level Coupling of Dynamic Data-Driven Experimentation with Material Identification , 2007, International Conference on Computational Science.

[4]  Zaira P. Marioli-Riga,et al.  Recent Advances in Composite Materials , 2003 .

[5]  Bernard P. Zeigler,et al.  Theory of modeling and simulation , 1976 .

[6]  Charbel Farhat,et al.  On a data-driven environment for multiphysics applications , 2005, Future Gener. Comput. Syst..

[7]  D. Krajcinovic,et al.  Introduction to continuum damage mechanics , 1986 .

[8]  J. G. Michopoulos,et al.  Computational and Mechatronic Automation of Multiphysics Research for Structural and Material Systems , 2003 .

[9]  John G. Michopoulos,et al.  Simultaneous Hierarchical and Multi-Level Optimization for Material Characterization and Design of Experiments , 2007 .

[10]  Daina Taimina,et al.  Experiencing Geometry: In Euclidean, Spherical and Hyperbolic Spaces , 2000 .

[11]  Taehyo Park,et al.  Anisotropic Damage Effect Tensors for the Symmetrization of the Effective Stress Tensor , 1997 .

[12]  Dusan Krajcinovic,et al.  Constitutive Equations for Damaging Materials , 1983 .

[13]  John G. Michopoulos,et al.  Characterization of strain-induced damage in composites based on the dissipated energy density part I. Basic scheme and formulation , 1995 .

[14]  P. Thagard,et al.  Computational Philosophy of Science , 1988 .

[15]  M. J. D. Powell,et al.  A fast algorithm for nonlinearly constrained optimization calculations , 1978 .

[16]  R. Fletcher Practical Methods of Optimization , 1988 .

[17]  A. Belegundu,et al.  Introduction to Finite Elements in Engineering , 1990 .

[18]  John G. Michopoulos,et al.  Characterization of strain-induced damage in composites based on the dissipated energy density part II. Composite specimens and naval structures , 1995 .

[19]  Philip E. Gill,et al.  Numerical methods for constrained optimization , 1974 .