Composite Material Design for Optimized Fracture Toughness Using Machine Learning

TL;DR

This paper demonstrates how machine learning can efficiently optimize the fracture toughness of 2D and 3D composite structures by predicting crack propagation and material behavior, surpassing traditional analysis methods.

Contribution

The study introduces ML-based methods for composite design optimization, significantly reducing computational effort and improving prediction accuracy over conventional finite element approaches.

Findings

ML accurately predicts fracture toughness with limited data

Rapid exploration of composite design space achieved

Enhanced understanding of microstructure-property relationships

Abstract

This paper investigates the optimization of 2D and 3D composite structures using machine learning (ML) techniques, focusing on fracture toughness and crack propagation in the Double Cantilever Beam (DCB) test. By exploring the intricate relationship between microstructural arrangements and macroscopic properties of composites, the study demonstrates the potential of ML as a powerful tool to expedite the design optimization process, offering notable advantages over traditional finite element analysis. The research encompasses four distinct cases, examining crack propagation and fracture toughness in both 2D and 3D composite models. Through the application of ML algorithms, the study showcases the capability for rapid and accurate exploration of vast design spaces in composite materials. The findings highlight the efficiency of ML in predicting mechanical behaviors with limited training data, paving the way for broader applications in composite design and optimization. This work contributes to advancing the understanding of ML's role in enhancing the efficiency of composite material design processes.