AI-assisted study of auxetic structures

In this study, the viability of using machine learning models to predict stress-strain curves of auxetic structures based on geometry-describing parameters is explored. Given the computational cost and time associated with generating these curves through numerical simulations, a machine learning-based approach promises a more efficient alternative. A range of machine learning models, including Artificial Neural Networks, k-Nearest Neighbors Regression, Support Vector Regression, and XGBoost, is implemented and compared regarding the aptitude to predict stress-strain curves under quasi-static compressive loading. Training data is generated using validated finite element simulations. The performance of these models is rigorously tested on data not seen during training. The Feed-Forward Artificial Neural Network emerged as the most proficient model, achieving a Mean Absolute Percentage Error of 0.367 ± 0.230

AI-assisted study of auxetic structures

In this study, the viability of using machine learning models to predict stress-strain curves of auxetic structures based on geometry-describing parameters is explored. Given the computational cost and time associated with generating these curves through numerical simulations, a machine learning-based approach promises a more efficient alternative. A range of machine learning models, including Artificial Neural Networks, k-Nearest Neighbors Regression, Support Vector Regression, and XGBoost, is implemented and compared regarding the aptitude to predict stress-strain curves under quasi-static compressive loading. Training data is generated using validated finite element simulations. The performance of these models is rigorously tested on data not seen during training. The Feed-Forward Artificial Neural Network emerged as the most proficient model, achieving a Mean Absolute Percentage Error of 0.367 ± 0.230

Applicability of correlated digital image correlation and infrared thermography for measuring mesomechanical deformation in foams and auxetics

Cellular materials such as metal foams or auxetic metamaterials are interesting microheterogeneous materials used for lightweight construction and as energy absorbers. Their macroscopic behavior is related to their specific mesoscopic deformation by a strong structure-property-relationship. Digital image correlation and infrared thermography are two methods to visualize and study the local deformation behavior in materials. The present study deals with the full-field thermomechanical analysis of the mesomechanical deformation in Ni/PU hybrid foams and Ni/polymer hybrid auxetic structures performing a correlative digital image correlation and infrared thermography. Instead of comparing and correlating only the primary output variables of both methods, strain and temperature, also strain rates and temperature rates occurring during deformation were compared. These allow for a better correlation and more conclusive results than obtained using only the primary output variables