Engineering Commons^™

Machine Learning For Electronic Structure Prediction, Shashank Pathrudkar

Dissertations, Master's Theses and Master's Reports

Kohn-Sham density functional theory is the work horse of computational material science research. The core of Kohn-Sham density functional theory, the Kohn-Sham equations, output charge density, energy levels and wavefunctions. In principle, the electron density can be used to obtain several other properties of interest including total potential energy of the system, atomic forces, binding energies and electric constants. In this work we present machine learning models designed to bypass the Kohn-Sham equations by directly predicting electron density. Two distinct models were developed: one tailored to predict electron density for quasi one-dimensional materials under strain, while the other is applicable …

Data-Driven 2d Materials Discovery For Next-Generation Electronics, Zeyu Zhang

Dissertations

The development of material discovery and design has lasted centuries in human history. After the concept of modern chemistry and material science was established, the strategy of material discovery relies on the experiments. Such a strategy becomes expensive and time-consuming with the increasing number of materials nowadays. Therefore, a novel strategy that is faster and more comprehensive is urgently needed. In this dissertation, an experiment-guided material discovery strategy is developed and explained using metal-organic frameworks (MOFs) as instances. The advent of 7r-stacked layered MOFs, which offer electrical conductivity on top of permanent porosity and high surface area, opened up new …

Utilization Of Machine Learning To Investigate Material State, Ana B. Abadie

Honors College Theses

The ability to predict material behavior that undergoes various loading conditions is critical to the development of reliable and safe components. Thermal and mechanical fatigue loading can cause significant damage to materials, leading to failure and potential safety hazards. Machine learning algorithms have emerged as a promising tool for improving accuracy and efficiency of predicting material behavior under such loading conditions. This research provides a comprehensive overview of a machine learning algorithm that is able to analyze and predict material state independently of the loading sequence. Unidirectional carbon fiber reinforced polymer (UD CFRP) composite which has undergone two different loading …

Lignin Copolymer Property Prediction Using Machine Learning, Collin Larsen

Chemical Engineering Undergraduate Honors Theses

Lignin, an abundant biopolymer, is a waste byproduct of the paper and pulp industry. Despite its renewable nature and potential applicability in various products, such as plastics and composites, the development of lignin-based materials has been impeded by the cumbersome, Edisonian process of trial and error. This research proposes a novel approach to forecasting the properties of lignin-based copolymers by utilizing a recurrent neural network (RNN) based on the Keras models previously created by Tao et al. Example units of modified lignin were synthesized via esterification and amination functional group modifications. To increase the efficiency and accuracy of the prediction …

A Machine Learning Approach To Robotic Additive Manufacturing Of Uv-Curable Polymers Using Direct Ink Writing, Luis A. Velazquez

LSU Master's Theses

This thesis presents the design and implementation of a robotic additive manufacturing system that uses ultraviolet (UV)-curable thermoset polymers. Its design considers future applications involving free-standing 3D printing by means of partial UV curing and the fabrication of samples that are reinforced with fillers or fibers to manufacture complex-shape objects.

The proposed setup integrates a custom-built extruder with a UR5e collaborative manipulator. The capabilities of the system were demonstrated using Anycubic resin formulations containing fumed silica (FS) at varying weight fractions from 2.8 to 8 wt%. To fully cure the specimens after fabrication, a UV chamber was used. Then, measurements …

Laser Surface Treatment And Laser Powder Bed Fusion Additive Manufacturing Study Using Custom Designed 3d Printer And The Application Of Machine Learning In Materials Science, Hao Wen

LSU Doctoral Dissertations

Selective Laser Melting (SLM) is a laser powder bed fusion (L-PBF) based additive manufacturing (AM) method, which uses a laser beam to melt the selected areas of the metal powder bed. A customized SLM 3D printer that can handle a small quantity of metal powders was built in the lab to achieve versatile research purposes. The hardware design, electrical diagrams, and software functions are introduced in Chapter 2. Several laser surface engineering and SLM experiments were conducted using this customized machine which showed the functionality of the machine and some prospective fields that this machine can be utilized. Chapter 3 …

Studying The Effects Of Various Process Parameters On Early Age Hydration Of Single- And Multi-Phase Cementitious Systems, Rachel Cook

Doctoral Dissertations

”The hydration of multi-phase ordinary Portland cement (OPC) and its pure phase derivatives, such as tricalcium silicate (C₃S) and belite (ß-C₂S), are studied in the context varying process parameters -- for instance, variable water content, water activity, superplasticizer structure and dose, and mineral additive type and particle size. These parameters are studied by means of physical experiments and numerical/computational techniques, such as: thermodynamic estimations; numerical kinetic-based modelling; and artificial intelligence techniques like machine learning (ML) models. In the past decade, numerical kinetic modeling has greatly improved in terms of fitting experimental, isothermal calorimetry to kinetic-based modelling …

Thermodynamic Investigations Of Pure And Blended Cement Mixtures, Jonathan Lapeyre

Doctoral Dissertations

” This research is made up of several studies. The first study focused on understanding the reaction kinetics of Ca₃SiO₅ and metakaolin (MK) mixtures compared to Ca₃SiO₅ and silica fume (SF) mixtures. It was shown that MK was a more effective additive than SF at small replacement levels (i.e. ≥ 10% by mass) while higher replacement levels of MK became a detriment due to excess (Al(OH)₄¯) ions preventing the nucleation and growth of C-S-H. In a follow-up study where the MK particle size distribution (PSD) was modified, similar effects were observed but …

Machine Learning Applications In Computer Integrated Material Design And Modeling, Joseph O. Oyedele

Mechanical Engineering Theses

This research seeks to develop and test a framework for considering Inductive Hierarchical Analysis (IHA) using Machine Learning (ML) technique in a multistage material design of a gear manufacturing process. A ML model was developed, which implements Random Forest (RF) regression algorithm together with analysis of variance (ANOVA) approach for mapping sets of material and product design variables, thus classifying the design space into feasible and non-feasible solution space. This approach is applied to the design of steel gear within specified performance requirements by exploring the design space for the Process-Structure-Property-Performance (PSPP) relation in the hot rolling process. With an …

Deformation Correlations And Machine Learning: Microstructural Inference And Crystal Plasticity Predictions, Michail Tzimas

Graduate Theses, Dissertations, and Problem Reports

The present thesis makes a connection between spatially resolved strain correlations and material processing history. Such correlations can be used to infer and classify prior deformation history of a sample at various strain levels with the use of Machine Learning approaches. A simple and concrete example of uniaxially compressed crystalline thin films of various sizes, generated by two-dimensional discrete dislocation plasticity simulations is examined. At the nanoscale, thin films exhibit yield-strength size effects with noisy mechanical responses which create an interesting challenge for the application of Machine Learning techniques. Moreover, this thesis demonstrates the prediction of the average mechanical responses …

Evolution Of Mg Az31 Twin Activation With Strain: A Machine Learning Study, Andrew D. Orme

Undergraduate Honors Theses

Machine learning is being adopted in various areas of materials science to both create predictive models and to uncover correlations which reveal underlying physics. However, these two aims are often at odds with each other since the resultant predictive models generally become so complex that they can essentially be described as a black box, making them difficult to understand. In this study, complex relationships between microstructure and twin formation in AZ31 magnesium are investigated as a function of increasing strain. Supervised machine learning is employed, in the form of J-48 decision trees. In one approach, strain is incorporated as an …