All Categories (1-3 of 3)

1. Hierarchical Multi-Response Gaussian Processes for Uncertainty Analysis with Multi-Scale Composite Manufacturing Simulation

   out of 5 stars 

   23 Feb 2022 | Contributor(s):: kai kai zhou zhou, Ryan Scott Enos, Dianyun Zhang

   Variations of constituent fiber and matrix properties and process conditions can cause significant variability in composite parts and affect their performance.  The focus of this paper is to establish a new computational framework that can efficiently quantify the uncertainty propagation of...

2. Machine Learning Assisted MSG-based Multiscale Constitutive Modeling

   out of 5 stars 

   05 Jul 2019 | Contributor(s):: Wenbin Yu

   Lecture given in Summer 2019 at Wuhan University of Technology.

3. Initial failure strength prediction of woven composites using a new yarn failure criterion constructed by deep learning

   out of 5 stars 

   05 Jun 2019 | Contributor(s):: Xin Liu, Federico Gasco, Johnathan Goodsell, Wenbin Yu

   A new failure criterion for fiber tows (i.e. yarns) is developed based on a micromechanical model using mechanics of structure genome (MSG) and deep learning neural network. The proposed failure criterion can be applied to yarns in mesoscale textile composites modeling while capturing the failure...