Prediction of Elastic Stresses in Porous Materials Using Fully Convolutional Networks
| Autor: | Özgür Keleṣ, Yinchuan He, Birsen Sirkeci-Mergen |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
010302 applied physics
Materials science Mechanical Engineering Isotropy Metals and Alloys Fracture mechanics 02 engineering and technology Mechanics 021001 nanoscience & nanotechnology Condensed Matter Physics 01 natural sciences Finite element method Stress (mechanics) Brittleness Flexural strength Mechanics of Materials 0103 physical sciences Fracture (geology) General Materials Science Composite material 0210 nano-technology Porous medium Stress concentration |
| Zdroj: | SSRN Electronic Journal. |
| ISSN: | 1556-5068 |
| DOI: | 10.2139/ssrn.3714501 |
| Popis: | Machine learning (ML) models enable exploration of vast structural space faster than the traditional methods, such as finite element method (FEM). This makes ML models suitable for stochastic fracture problems in brittle porous materials. In this work, fully convolutional networks (FCNs) were trained to predict stress and stress concentration factor distributions in two-dimensional isotropic elastic materials with uniform porosity. We show that even with downsampled data, FCN models predict the stress distributions for a given porous structure. FCN predicted stress concentration factors 10,000 times faster than the FEM simulations. The FCN-predicted stresses combined with fracture mechanics captured the effect of porosity on the strength of porous glass. Increasing variations in pore size increased the variations in fracture strength. Furthermore, the FCN model predicts the pore configurations with the lowest and highest stresses from a set of structures, enabling ML optimization of porous microstructures for increased reliability. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|