An intelligent inverse method for characterization of textile reinforced thermoplastic composites using a hyperelastic constitutive model

Uncontrollable factors such as contact friction, misalignment, slip, variations in local fiber volume due to fiber spreading or bunching, and tow compaction are a few sources leading to scatter (noise) in the response (signal) of textile composites. Accordingly, characterization methods often have difficulty due to non-repeatability of test data. If variance of such response within the replication of tests is neglected, then the identification of model parameters can be far from describing the true material behavior. In order to confront this shortcoming, the main objective of this paper is to elaborate on characterization of textile composites using a new inverse method by means of the signal-to-noise ratio. It will also be shown that using an appropriate constitutive model and statistical framework, the engagement of a larger range of test replications is not only useful but also may be critical for better characterization of this class of material.