Dynamic Data Driven Experiment Control Coordinated with Anisotropic Elastic Material Characterization

Abstract

The goal of this paper is to propose and demonstrate a multi level design optimization approach for the coordinated determination of a material constitutive model synchronously to the design of the experimental procedure needed to acquire the necessary data. The methodology achieves both online (real-time) and offline design of optimum experiments required for characterization of the material system under consideration, while it also achieves the constitutive characterization of the system. The approach is based on the availability of mechatronic systems that can expose specimens to multidimensional loading paths and can automate the acquisition of data associated with stimulus and response behavior of the specimen. Material characterization is achieved by minimizing the difference between system responses that are measured experimentally and predicted based on the associated model representation. The performance metrics of the material characterization process are used to construct objective functions for the design of experiments at a higherlevel optimization. Distinguishability and uniqueness of solutions that characterize the system are used as two of many possible measures adopted for construction of objective functions required for design of experiments. Finally, a demonstration of the methodology is presented that considers the best loading path of a two degree-of-freedom loading machine for characterization of the linear elastic constitutive response of anisotropic materials.