Inverse Characterization of Composite Materials Using Surrogate Models

Abstract

In recent years, methods for the inverse characterization of mechanical properties of materials have seen significant growth, mainly because of the availability of enabling technologies like full-field measurement techniques, inexpensive high performance computing resources, and automated testing. Unfortunately, as the complexity of the material system increases even the most advanced methods for inverse characterization produce results in compute times that are not practical for real time applications. To overcome this limitation we present a method that uses Non-Uniform Rational B-spline (NURBs) based surrogate modeling to generate a very efficient representation of the material model and the associated objective function. In addition, we present a method for identifying the global minimum of this objective function that corresponds to the elastic properties that characterize the material. Validation of this methodology is achieved through synthetic numerical experiments that include both isotropic and orthotropic specimens defined both analytically and numerically. Statistical analyses on the effects of experimental noise supplement these results. We conclude with remarks regarding the use of this technique to recover the elastic properties from materials tested utilizing multiaxial robotic systems.

Inverse characterization of composite materials via surrogate modeling

“…The findings presented here are an extension of the developmental work found in [7]. The principle novelty of the present work is the mathematically rigorous description of the methodology employed.…”

“…Another approach based on the use of artificial neural networks [31] is presented in [32] and further demonstrated in [33]. As neural networks may be considered a physics-agnostic surrogate model, their utilization is most closely related to our earlier work utilizing NURBs-based surrogates [7].…”