Model Identification of Textile Composites: A Multi-Objective Inverse Method under Hyperelastic Material Behaviour

Numerical simulation of forming processes has become an important means for material selection, tool design, and process optimization. A critical component of simulation is an accurate material constitutive model, describing the response of the material under possible modes of deformation. The accuracy, in turn, is linked to the tests and techniques applied for the identification of constitutive models. This book elaborates on the identification of textile composite models using a new inverse method by means of a signal-to-noise weighting scheme, and two constitutive equations based on a phenomenological invariant-based approach. A full identification of the models for a typical woven fabric is applied using sets of data from standard testing methods, including a modified picture frame test. The identification framework makes it possible to include all sets of data from several deformation modes, along with their given test non-repeatabilities. The method has been presented in a general manner and it can be applied in other applications, where non-repeatability of measurements and the lack of agreement between identification parameters in different modes are practical problems.

Dr. Abbas S. Milani is a faculty member in the School of Engineering at the University of British Columbia Okanagan. He received his PhD from McGill University with a specialization in multi-objective modeling and identification of textile composites. He has also been an NSERC postdoctoral fellow at the Massachusetts Institute of Technology. Title