Machine Learning for Composite Materials with Abaqus
This tutorial package delves into an advanced inverse modeling approach for predicting carbon fiber properties in composite materials using a machine learning (ML) technique. Specifically, it covers the use of Gaussian Process Regression (GPR) to build a surrogate model for accurate predictions of fiber properties based on data from unidirectional (UD) lamina. By leveraging Finite Element (FE) homogenization, synthetic data is generated for training the GPR model, accounting for variations in fiber, matrix properties, and volume fractions. This framework’s efficiency and accuracy are validated using real-world data, highlighting its potential as a computational alternative to traditional experimental methods. The package includes detailed explanations, case studies, and practical exercises, equipping users with hands-on experience in applying this ML-based approach to composite material analysis.
Full Composite fatigue Add-on (Academic and industrial usage)
Composite Fatigue Simulation with VUMAT Subroutine in ABAQUS
Simulation of Unidirectional Composite Damage in ABAQUS
Abaqus composite modeling of Woven & Unidirectional + RVE method
This training package provides comprehensive basic information and examples on for composite modeling in ABAQUS FEM software in accordance with subsequent packages. The methods of modeling these materials are in two ways: micro and macro, which vary according to the type of material selected and how they are used. Next packages focus on two modeling types professionally.