Computational and Machine Learning-Reinforced Modeling and Design of Materials under Uncertainty
| dc.contributor.author | Hasan, Md Mahmudul | en |
| dc.contributor.committeechair | Acar, Pinar | en |
| dc.contributor.committeemember | Kapania, Rakesh K. | en |
| dc.contributor.committeemember | Pitchumani, Ranga | en |
| dc.contributor.committeemember | West, Robert L. | en |
| dc.contributor.department | Mechanical Engineering | en |
| dc.date.accessioned | 2023-07-06T08:00:28Z | en |
| dc.date.available | 2023-07-06T08:00:28Z | en |
| dc.date.issued | 2023-07-05 | en |
| dc.description.abstract | The component-level performance of materials is fundamentally determined by the underlying microstructural features. Therefore, designing high-performance materials using multi-scale models plays a significant role to improve the predictability, reliability, proper functioning, and longevity of components for a wide range of applications in the fields of aerospace, electronics, energy, and structural engineering. This thesis aims to develop new methodologies to design microstructures under inherent material uncertainty by incorporating machine learning techniques. To achieve this objective, the study addresses gradient-based and machine learning-driven design optimization methods to enhance homogenized linear and non-linear properties of polycrystalline microstructures. However, variations arising from the thermo-mechanical processing of materials affect microstructural features and properties by propagating over multiple length scales. To quantify this inherent microstructural uncertainty, this study introduces a linear programming-based analytical method. When this analytical uncertainty quantification formulation is not applicable (e.g., uncertainty propagation on non-linear properties), a machine learning-based inverse design approach is presented to quantify the microstructural uncertainty. Example design problems are discussed for different polycrystalline systems (e.g., Titanium, Aluminium, and Galfenol). Though conventional machine learning performs well when used for designing microstructures or modeling material properties, its predictions may still fail to satisfy design constraints associated with the physics of the system. Therefore, the physics-informed neural network (PINN) is developed to incorporate problem physics in the machine learning formulation. In this study, a PINN model is built and integrated into materials design to study the deformation processes of Copper and a Titanium-Aluminum alloy. | en |
| dc.description.abstractgeneral | Microstructure-sensitive design is a high-throughput computational approach for materials design, where material performance is improved through the control and design of microstructures. It enhances component performance and, subsequently, the overall system's performance at the application level. This thesis aims to design microstructures for polycrystalline materials such as Galfenol, Titanium-Aluminum alloys, and Copper to obtain desired mechanical properties for certain applications. The advantage of the microstructure-sensitive design approach is that multiple microstructures can be suggested, which provide a similar value of the design parameters. Therefore, manufacturers can follow any of these microstructure designs to fabricate the materials with the desired properties. Moreover, the microstructure uncertainty arising from the variations in thermo-mechanical processing and measurement of the experimental data is quantified. It is necessary to address the resultant randomness of the microstructure because it can alter the expected mechanical properties. To check the manufacturability of proposed microstructure designs, a physics-informed machine learning model is developed to build a relation between the process, microstructure, and material properties. This model can be used to solve the process design problem to identify the processing parameters to achieve a given/desired microstructure. | en |
| dc.description.degree | Doctor of Philosophy | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:37753 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/115651 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Multi-scale Design | en |
| dc.subject | Uncertainty Quantification | en |
| dc.subject | Design Under Uncertainty | en |
| dc.subject | Machine Learning | en |
| dc.subject | Process-Structure-Property Linkage | en |
| dc.subject | Physics-Informed Neural Network | en |
| dc.title | Computational and Machine Learning-Reinforced Modeling and Design of Materials under Uncertainty | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Mechanical Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |
Files
Original bundle
1 - 1 of 1