arXiv subjects

M. Amir Siddiq

Publications and source records attributed to M. Amir Siddiq.

3 recordsLinked to original sources

arXiv2021

Multiscale Materials Modelling through Machine Learning: Hydrogen-Steel Interaction during Deformation

This short paper presents the potential of using machine learning to predict materials behaviour in the context of hydrogen interaction with steel. Effort has been made to understand the quality, and amount of data needed to get improved predictions. An approach known as physics informed machine learning has been adapted in a simplified way through data classification to show the improvement in predictions. Proposed model eliminates the requirement to solve complex materials constitutive models and can work for any length scale, in the present case it is used for single crystalline steel interacting with steel under different types of loading.

cond-mat.mtrl-sci↗

arXiv2020

Data Driven Finite Element Method: Theory and Applications

A data driven finite element method (DDFEM) that accounts for more than two material state variables has been presented in this work. DDFEM framework is motivated from (1,2) and can account for multiple state variables, viz. stresses, strains, strain rates, failure stress, material degradation, and anisotropy which has not been used before. DDFEM is implemented in the context of linear elements of a nonlinear elastic solid. The presented framework can be used for variety of applications by directly using experimental data. This has been demonstrated by using the DDFEM framework to predict deformation, degradation and failure in diverse applications including nanomaterials and biomaterials for the first time. DDFEM capability of predicting unknown and unstructured dataset has also been shown by using Delaunay triangulation strategy for scattered data having no structure or order. The framework is able to capture the strain rate dependent deformation, material anisotropy, material degradation, and failure which has not been presented in the past. The predicted results show a very good agreement between data set taken from literature and DDFEM predictions without requiring to formulate complex constitutive models and avoiding tedious material parameter identification.

physics.comp-ph↗

arXiv2019

Deformation and failure in nanomaterials via a data driven modelling approach

A data driven computational model that accounts for more than two material states has been presented in this work. Presented model can account for multiple state variables, such as stresses, strains, strain rates and failure stress, as compared to previously reported models with two states. Model is used to perform deformation and failure simulations of carbon nanotubes and carbon nanotube/epoxy nanocomposites. The model capability of capturing the strain rate dependent deformation and failure has been demonstrated through predictions against uniaxial test data taken from literature. The predicted results show a good agreement between data set taken from literature and simulations.

cond-mat.mtrl-sci↗