fracture

Published in Extreme Mechanics Letters: https://doi.org/10.1016/j.eml.2020.100685. Catastrophic failure of materials and structures due to unstable crack growth could be prevented if fracture toughness could be enhanced at will through structural design, but how can this be possible if fracture toughness is a material constant related to energy dissipation in the vicinity of a propagating crack tip.

Safer batteries, more efficient gas-turbine engines and solar cells, all require better-engineered nanocomposite materials. There is a limitation though -- how to investigate the fracture mechanics of these materials? Machine learning can help us overcome this limitation. Read more in our just-published paper: https://lnkd.in/e7dPBtx

Abstract: Advanced machine learning methods could be useful to obtain novel insights into some challenging nanomechanical problems. In this work, we employed artificial neural networks to predict the fracture stress of defective graphene samples. First, shallow neural networks were used to predict the fracture stress, which depends on the temperature, vacancy concentration, strain rate, and loading direction.

Deadline for short abstract submission - February 15, 2020

https://lnkd.in/dQUgfhv

JMPS link: https://www.sciencedirect.com/science/article/pii/S0022509619304405?dgcid=raven_sd_aip_email

Arxiv link to full text: https://arxiv.org/abs/1905.08485

An Adaptive Quasi-Continuum Approach for Modeling Fracture in Networked Materials: Application to Modeling of Polymer Networks

Dear iMechanicians,

Please find below a paper, co-authored with Ivan Cuesta and Norman Fleck, on the mode II fracture of elastic-plastic sandwich layers. The paper is part of the special issue in the Journal of Applied Mechanics dedicated to John Hutchinson's 80th anniversary and the Century Fracture Mechanics Summit.