Online planning of multiaxial loading path for elastic material identification

While anisotropic materials provide more capabilities than regular isotropic materials, the directional behavior of the anisotropic materials complicates their elastic and inelastic behavior. Such nonlinear behavior can be effectively observed and characterized by multiaxial testing, but how to plan the loading path of a multiaxial test given a specimen and sensor readings while identifying material properties remains as an untouched issue. This paper presents a methodology that updates the loading path at every sensor reading to identify reliable elastic moduli of anisotropic materials. The multiaxial test must be quantified in order to find an effective loading path. The proposed methodology uses distinguishability and uniqueness as such quantities by firstly analyzing the specimen on a continuum basis and then applying singular value decomposition (SVD). Associating the distinguishability and uniqueness to the reliability of elastic moduli, the path planning problem is formulated such that an effective loading path can be found efficiently by a standard optimization method. Numerical examples first investigate the validity of the distinguishability and the uniqueness as performance measures to evaluate loading paths and the reliability of elastic moduli identified. The efficacy of the proposed methodology has been then confirmed by analyzing it with and applying it to identification of elastic moduli.