Open Access. Powered by Scholars. Published by Universities.®

Materials Science and Engineering Commons™

Open Access. Powered by Scholars. Published by Universities.®

Machine learning

Discipline
Institution
Publication Year
Publication
Publication Type
File Type

Articles 61 - 75 of 75

Full-Text Articles in Materials Science and Engineering

Understanding Structure/Process-Property Relationships To Optimize Development Lifecycle In Yttria-Stabilized Zirconia Aerogels For Thermal Management, Rebecca C. Walker Jan 2022

Understanding Structure/Process-Property Relationships To Optimize Development Lifecycle In Yttria-Stabilized Zirconia Aerogels For Thermal Management, Rebecca C. Walker

Theses and Dissertations

Aerogels are mesoporous materials with unique properties, including high specific surface area, high porosity, low thermal conductivity, and low density, increasing these materials’ effectiveness in applications such as catalyst supports, sorption media, and electrodes in solid oxide fuel cells. Zirconia (ZrO2) aerogels have special interest for high-temperature applications due to the high melting point of ZrO2 (2715°C) and stability between 600°C and 1000°C, where other aerogel systems often begin to sinter and densify. These properties and unique pore structure make zirconia aerogels advantageous as thermal management systems, especially in aeronautics and aerospace applications. However, to be effective …

Go to article

Predictive Computational Materials Modeling With Machine Learning: Creating The Next Generation Of Atomistic Potential Using Neural Networks, Mashroor Shafat Nitol Dec 2021

Predictive Computational Materials Modeling With Machine Learning: Creating The Next Generation Of Atomistic Potential Using Neural Networks, Mashroor Shafat Nitol

Theses and Dissertations

Machine learning techniques using artificial neural networks (ANNs) have proven to be effective tools to rapidly mimic first principles calculations. These tools are capable of sub meV/atom accuracy while operating with linear scaling with respect to the system size. Here novel interatomic potentials are constructed based on the rapid artificial neural network (RANN) formalism. This approach generates precise force fields for various metals that have historically been difficult to describe at the atomic scale. These force fields can be utilized in molecular dynamics simulations to provide new physical insights. The RANN formalism, which is incorporated into a LAMMPS molecular dynamics …

Go to article

Universal Image Segmentation For Optical Identification Of 2d Materials, Joshua Island, Randy M. Sterbentz, Kristine L. Haley Mar 2021

Universal Image Segmentation For Optical Identification Of 2d Materials, Joshua Island, Randy M. Sterbentz, Kristine L. Haley

Physics & Astronomy Faculty Research

Machine learning methods are changing the way data is analyzed. One of the most powerful and widespread applications of these techniques is in image segmentation wherein disparate objects of a digital image are partitioned and classified. Here we present an image segmentation program incorporating a series of unsupervised clustering algorithms for the automatic thickness identification of two-dimensional materials from digital optical microscopy images. The program identifies mono- and few-layer flakes of a variety of materials on both opaque and transparent substrates with a pixel accuracy of roughly 95%. Contrasting with previous attempts, application generality is achieved through preservation and analysis …

Go to article

Determination Of Hydrogel Degradation By Passive Mechanical Testing, Avery Rosh-Gorsky Jan 2021

Determination Of Hydrogel Degradation By Passive Mechanical Testing, Avery Rosh-Gorsky

Honors Theses

This paper details a new technique to measure the mechanical properties of ETTMP PEGDA hydrogels using Hertz Contact Theory and simultaneously analyze both the model drug release and gel erosion in situ. This method involves curing a drug loaded hydrogel in a standard cuvette and placing a glass bead and phosphate buffer solution (PBS). Over time, the cross-linked network of the hydrogel breaks down, and, as a result, the ball sinks into the hydrogel. This method provides a macroscopic and inexpensive way to continuously and passively measure properties of the hydrogel as the hydrogel degrades. By plotting both the …

Go to article

Applied Machine Learning In Extrusion-Based Bioprinting, Shuyu Tian Jan 2021

Applied Machine Learning In Extrusion-Based Bioprinting, Shuyu Tian

Theses and Dissertations

Optimization of extrusion-based bioprinting (EBB) parameters have been systematically conducted through experimentation. However, the process is time and resource-intensive and not easily translatable across different laboratories. A machine learning (ML) approach to EBB parameter optimization can accelerate this process for laboratories across the field through training using data collected from published literature. In this work, regression-based and classification-based ML models were investigated for their abilities to predict printing outcomes of cell viability and filament diameter for cell-containing alginate and gelatin composite hydrogels. Regression-based models were investigated for their ability to predict suitable extrusion pressure given desired cell viability when keeping …

Go to article

Tool Life Prediction Of Ti [C,N] Mixed Alumina Ceramic Cutting Tool Using Gradient Descent Algorithm On Machining Martensitic Stainless Steel, Joseph Daniel S, Senthil Kumar A Nov 2020

Tool Life Prediction Of Ti [C,N] Mixed Alumina Ceramic Cutting Tool Using Gradient Descent Algorithm On Machining Martensitic Stainless Steel, Joseph Daniel S, Senthil Kumar A

Journal of Mechanical Engineering Science and Technology (JMEST)

In automated manufacturing systems, most of the manufacturing processes, including machining, are automated. Automatic tool change is one of the important parameters for reducing manufacturing lead time. Machining studies on Martensitic Stainless Steel was conducted using Ti[C,N] mixed alumina ceramic cutting tool. Tool life was evaluated using flank wear criterion. The tool life obtained from experimental machining process was taken as training dataset and test dataset for machine learning. Tool life model was developed using Gradient Descent Algorithm. The accuracy of the machine learning model was tested using the test data, and 99.83% accuracy was obtained.

Go to article

Prediction Of Flotation Efficiency Of Metal Sulfides Using An Original Hybrid Machine Learning Model, Rachel Cook, Keitumetse Cathrine Monyake, Muhammad Badar Hayat, Aditya Kumar, Lana Alagha Jun 2020

Prediction Of Flotation Efficiency Of Metal Sulfides Using An Original Hybrid Machine Learning Model, Rachel Cook, Keitumetse Cathrine Monyake, Muhammad Badar Hayat, Aditya Kumar, Lana Alagha

Materials Science and Engineering Faculty Research & Creative Works

Froth flotation process is extensively used for selective separation of base metal sulfides from uneconomic mineral resources. Reliable prediction of process outcomes (metal recovery and grade) is vital to ensure peak performance. This work employs an innovative hybrid machine learning (ML) model—constructed by combining the random forest model and the firefly algorithm—to predict froth flotation efficiency of galena and chalcopyrite in relation to various experimental process parameters. The hybrid model's prediction performance was rigorously evaluated and compared against four different standalone ML models. The outcomes of this study illustrate that the hybrid ML model has the prediction ability to process …

Go to article

Artificial Intelligence In Plasma Electrolytic Micro-Oxidation For Surface Hardening - Insights From Scholarly Citation Networks And Patents., Priya Jadhav, Dr.Arun Bongale, Dr.Satish Kumar, Dr.Amit Kumar Tiwari Jan 2020

Artificial Intelligence In Plasma Electrolytic Micro-Oxidation For Surface Hardening - Insights From Scholarly Citation Networks And Patents., Priya Jadhav, Dr.Arun Bongale, Dr.Satish Kumar, Dr.Amit Kumar Tiwari

Library Philosophy and Practice (e-journal)

Objective - The purpose of this article is to analyze the top work areas and patent domains in the field of surface hardening by micro-arc oxidation. Also, it is directed on the opportunities of data analysis by different machine learning tools. Material and methods - The www.lens.org database is used to collect articles from Elsevier, Trans tech publications, Springer New York, MDPI, etc. to review the relevant articles as well as patents related to the topic. The result - A total of 1057 articles were published in 60 different journals and 756 patents in the area of research under various …

Go to article

Machine Learning Predictions Electronic Couplings For Charge Transport Calculations Of P3ht, Evan D. Miller, Matthew L. Jones, Mike M. Henry, Bryan Stanfill, Eric Jankowski Dec 2019

Machine Learning Predictions Electronic Couplings For Charge Transport Calculations Of P3ht, Evan D. Miller, Matthew L. Jones, Mike M. Henry, Bryan Stanfill, Eric Jankowski

Materials Science and Engineering Faculty Publications and Presentations

The purpose of this work is to lower the computational cost of predicting charge mobilities in organic semiconductors, which will benefit the screening of candidates for inexpensive solar power generation. We characterize efforts to minimize the number of expensive quantum chemical calculations we perform by training machines to predict electronic couplings between monomers of poly-(3-hexylthiophene). We test five machine learning techniques and identify random forests as the most accurate, information-dense, and easy-to-implement approach for this problem, achieving mean-absolute-error of 0.02 [× 1.6 × 10−19 J], R2 = 0.986, predicting electronic couplings 390 times faster than quantum chemical calculations, …

Go to article

Demonstration Of Visible And Near Infrared Raman Spectrometers And Improved Matched Filter Model For Analysis Of Combined Raman Signals, Alexander Matthew Atkinson Oct 2019

Demonstration Of Visible And Near Infrared Raman Spectrometers And Improved Matched Filter Model For Analysis Of Combined Raman Signals, Alexander Matthew Atkinson

Electrical & Computer Engineering Theses & Dissertations

Raman spectroscopy is a powerful analysis technique that has found applications in fields such as analytical chemistry, planetary sciences, and medical diagnostics. Recent studies have shown that analysis of Raman spectral profiles can be greatly assisted by use of computational models with achievements including high accuracy pure sample classification with imbalanced data sets and detection of ideal sample deviations for pharmaceutical quality control. The adoption of automated methods is a necessary step in streamlining the analysis process as Raman hardware becomes more advanced. Due to limits in the architectures of current machine learning based Raman classification models, transfer from pure …

Go to article

Understanding Self-Assembly And Charge Transport In Organic Solar Cells Through Efficient Computation, Evan Miller Aug 2019

Understanding Self-Assembly And Charge Transport In Organic Solar Cells Through Efficient Computation, Evan Miller

Boise State University Theses and Dissertations

Organic solar cells capable of sustainably generating electricity are possible if: (1) The structures assembled by photoactive molecules can be controlled, and (2) The structures favorable for charge transport can be determined. In this dissertation we conduct computational studies to understand relationships between organic solar cell compounds, processing, structure and charge transport. We advance tools for encapsulating computational workflows so that simulations are more reproducible and transferable. We find that molecular dynamic simulations using simplified models efficiently predict experimental structures. We find that the mobilities of charges through these structures—as determined by kinetic Monte Carlo simulations—match qualitative trends expected with …

Go to article

Implementation Of Multivariate Artificial Neural Networks Coupled With Genetic Algorithms For The Multi-Objective Property Prediction And Optimization Of Emulsion Polymers, David Chisholm Jun 2019

Implementation Of Multivariate Artificial Neural Networks Coupled With Genetic Algorithms For The Multi-Objective Property Prediction And Optimization Of Emulsion Polymers, David Chisholm

Master's Theses

Machine learning has been gaining popularity over the past few decades as computers have become more advanced. On a fundamental level, machine learning consists of the use of computerized statistical methods to analyze data and discover trends that may not have been obvious or otherwise observable previously. These trends can then be used to make predictions on new data and explore entirely new design spaces. Methods vary from simple linear regression to highly complex neural networks, but the end goal is similar. The application of these methods to material property prediction and new material discovery has been of high interest …

Go to article

Non-Destructive Evaluation For Composite Material, Desalegn Temesgen Delelegn Jul 2018

Non-Destructive Evaluation For Composite Material, Desalegn Temesgen Delelegn

Electrical & Computer Engineering Theses & Dissertations

The Nondestructive Evaluation Sciences Branch (NESB) at the National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC) has conducted impact damage experiments over the past few years with the goal of understanding structural defects in composite materials. The Data Science Team within the NASA LaRC Office of the Chief Information Officer (OCIO) has been working with the Non-Destructive Evaluation (NDE) subject matter experts (SMEs), Dr. Cheryl Rose, from the Structural Mechanics & Concepts Branch and Dr. William Winfree, from the Research Directorate, to develop computer vision solutions using digital image processing and machine learning techniques that can help identify …

Go to article

Real-Time Object Detection And Tracking On Drones, Tu Le May 2018

Real-Time Object Detection And Tracking On Drones, Tu Le

Maseeh Summer Undergraduate Research Experience

Unmanned aerial vehicles, also known as drones, have been more and more widely used in recent decades because of their mobility. They appear in many applications such as farming, search and rescue, entertainment, military, and so on. Such high demands for drones lead to the need of developments in drone technologies. Next generations of commercial and military drones are expected to be aware of surrounding objects while flying autonomously in different terrains and conditions. One of the biggest challenges to drone automation is the ability to detect and track objects of interest in real-time. While there are many robust machine …

Go to article

Data Driven Discovery Of Materials Properties., Fadoua Khmaissia May 2017

Data Driven Discovery Of Materials Properties., Fadoua Khmaissia

Electronic Theses and Dissertations

The high pace of nowadays industrial evolution is creating an urgent need to design new cost efficient materials that can satisfy both current and future demands. However, with the increase of structural and functional complexity of materials, the ability to rationally design new materials with a precise set of properties has become increasingly challenging. This basic observation has triggered the idea of applying machine learning techniques in the field, which was further encouraged by the launch of the Materials Genome Initiative (MGI) by the US government since 2011. In this work, we present a novel approach to apply machine learning …

Go to article

First Prev