About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Symposium
|
First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Presentation Title |
Nanoindentation Mapping Defects Filtration for Heterogeneous Materials Using Generative Adversarial Networks (GAN’s) |
Author(s) |
Giuseppe A. Bianco Atria, Ambreen Nisar, Cheng Zhang, Benjamin Boesl, Arvind Agarwal |
On-Site Speaker (Planned) |
Giuseppe A. Bianco Atria |
Abstract Scope |
Advanced materials with multiple phases and heterogeneous microstructure necessitate mapping their mechanical properties such as elastic modulus to develop constitutive relations. However, structural heterogeneities such as surface roughness, porosity, and secondary phases result in anomalous underrepresentation of the true materials’ properties that conventional experimental strategies cannot amend. This paper establishes a novel deep learning-based strategy to rectify incorrect experimental spatial measurements acquired during nanoindentation modulus mapping. The integrated bicubic interpolation and generative adversarial networks (GANs) model was trained using 14 ceramic and 18 metallic data sets, each comprising 65,536 measurements. The developed algorithm was validated against experimental measurements on 4 unknown specimens. The standard deviation in measured elastic modulus reduces by ~50% in ceramics and ~72% on metals. This computational framework thus constitutes a significant advancement in applying deep learning algorithms for developing advanced materials and their correlation with manufacturing. |
Proceedings Inclusion? |
Undecided |