Integrating experiments, finite element analysis, and interpretable machine learning to evaluate the auxetic response of 3D printed re-entrant metamaterials

Integrating experiments, finite element analysis, and interpretable machine learning to evaluate the auxetic response of 3D printed re-entrant metamaterials

Metamaterials have received extensive attention in fundamental and applied research over the past two decades due to their unique mechanical behavior. This paper presents an interpretable machine learning (ML) approach for efficient response prediction of three-dimensional (3D)-printed metamaterials...

Full description

Bibliographic Details
Main Authors: Bolaji Oladipo, Helio Matos, N.M. Anoop Krishnan, Sumanta Das
Format: Article
Language:English
Published: Elsevier 2023-07-01
Series:Journal of Materials Research and Technology
Subjects:
Machine learning
Neural network
Re-entrant
Auxetic behavior
SHAP
Finite element analysis
Online Access:http://www.sciencedirect.com/science/article/pii/S2238785423012851

Similar Items

Similar Items