Speaker
Description
Textile reinforcements offer numerous advantages over conventional materials, including net-zero emissions, net-zero waste, durability, and design flexibility, rendering them ideally suited for aerospace, automotive, marine, and defense sectors. Nevertheless, understanding and predicting the mechanical behavior of textile reinforcements, especially in complex scenarios like when fibers slide and bend, remain challenging. In this contribution, we aim to revolutionize the current data-driven computational mechanics by integrating it with statistical learning to construct a pioneering framework that can transfer large datasets of generalized mechanics of textile reinforcements into robust constitutive formulations with reliability beyond existing models. To this end, the collection of comprehensive mechanical datasets will be accomplished through advanced experimental mechanics. A supervised-learning algorithm will be developed on the basis of the principle of virtual work. By balancing bias and variance of the learner, a small generalization error of the proposed data-driven statistical-learning framework will be guaranteed and quantified. This novel methodology will allow for material-modeling with predictive capability beyond observed data, a critical aspect particularly relevant in the defense sector.
