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    As wind energy production drives the manufacturing of wind turbine blades, the utilization of glass and carbon fiber composites as a material of choice continuously increases. Consequently, the needs for accurate structural design and material qualification and certification as well as the needs for aging predictions further underline the need for accurate constitutive characterization of composites. In the present paper we describe an outline of a recently developed methodology that utilizes mutliaxial robotically controlled testing combined with design optimization for the automated constitutive characterization of composite materials for both the linear and non-linear regimes. Our approach is based on the generation of experimental data originating from custom-developed mechatronic material testing systems that can expose specimens to multidimensional loading paths and can automate the acquisition of data representing the excitation and response behavior of the specimens involved. Material characterization is achieved by minimizing the difference between experimentally measured and analytically computed system responses as described by strain fields and surface strain energy densities. Small and finite strain formulations based on strain energy density decompositions are developed and utilized for determining the constitutive behavior of composite materials. Examples based on actual data demonstrate the successful application of design optimization for constitutive characterization. Validation experiments and their comparisons to theoretical predictions demonstrate the power of this approach.

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    Michopoulos, John; Iliopoulos, Athanasios; Hermanson, John. 2012. Robotic and Multiaxial Testing for the Determination of the Constitutive Characterization of Composites. Michopoulos, J., Iliopoulos, A. and Hermanson, J. (2012) Robotic and Multiaxial Testing for the Determination of the Constitutive Characterization of Composites, In: Materials Challenges in Alternative and Renewable Energy II: Ceramic Transactions (eds G. Wicks, J. Simon, R. Zidan, R. Brigmon, G. Fischman, S. Arepalli, A. Norris and M. McCluer), John Wiley & Sons, Inc., Hoboken, NJ, USA. 14 p.


    hexapod, 6 degree-of-freedom, mechatronics, data-driven methodology, automation

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