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Full-Text Articles in Engineering
Machine Learning-Guided Design Of Nanolubricants For Minimizing Energy Loss In Mechanical Systems, Kollol Sarker Jogesh, Md. Aliahsan Bappy
Machine Learning-Guided Design Of Nanolubricants For Minimizing Energy Loss In Mechanical Systems, Kollol Sarker Jogesh, Md. Aliahsan Bappy
Mechanical Engineering Faculty Publications and Presentations
This study explores the significant potential of machine learningguided design in optimizing nanolubricants, focusing on their application in reducing friction and wear in mechanical systems. Utilizing neural networks and genetic algorithms, the research demonstrates how advanced computational techniques can accurately predict and enhance the tribological properties of nanolubricants. The findings reveal that nanolubricants, particularly those containing graphene and carbon nanotubes, exhibit marked improvements in reducing friction coefficients and wear rates compared to traditional mineral oil-based lubricants. Additionally, the enhanced thermal stability and load-carrying capacity of these nanolubricants contribute to substantial energy savings and increased operational efficiency. The study underscores the …
Implementation Of Explainable Ai For Bearing Fault Classification, Mohammad Mundiwala
Implementation Of Explainable Ai For Bearing Fault Classification, Mohammad Mundiwala
Honors Scholar Theses
It is difficult to overstate the impact of artificial intelligence (AI) over the past decade. The rapid expansion of machine learning has stimulated a race to deploy AI in all facets of life, one such domain being machine health monitoring. There is no doubt that machine learning excels in prediction accuracy, but oftentimes, these models are cryptic and fail to provide valuable insight into their decisions. This paper presents an overview of a neural network and what it means to learn. Next, two distinct Explainable AI (XAI) techniques will be presented: Gradient Class Activation Mapping and SimplEx . Finally, these …
Probabilistic Machine Learning For Battery State Of Health Prognostics, Charli Zaretsky
Probabilistic Machine Learning For Battery State Of Health Prognostics, Charli Zaretsky
Honors Scholar Theses
The ability to understand and predict the state of health (SOH) of lithium-ion batteries is an integral component of their widespread commercial use. There are various methods through which SOH can be analyzed and predicted, and this paper discusses these different methods, and the strengths and weaknesses of each. This paper also details an analysis of lithium-ion battery SOH through two data-driven machine learning methods: XGBoost and Gaussian process regression. A comparison is made between each method’s accuracy in predicting next-cycle discharge capacity using electrochemical impedance spectroscopy (EIS) readings and battery charge and discharge rates, from a dataset given in …
Prediction Of Meltpool Depth In Laser Powder Bed Fusion Using In-Process Sensor Data, Part-Level Thermal Simulations, And Machine Learning, Grant King
Department of Mechanical and Materials Engineering: Dissertations, Theses, and Student Research
The goal of this thesis is the prevention of flaw formation in laser powder bed fusion additive manufacturing process. As a step towards this goal, the objective of this work is to predict meltpool depth as a function of in-process sensor data, part-level thermal simulations, and machine learning. As motivated in NASA's Marshall Space Flight Center specification 3716, prediction of meltpool depth is important because: (1) it can serve as a surrogate to estimate process status without the need for expensive post-process characterization, and (2) the meltpool depth provides an avenue for rapid qualification of microstructure evolution. To achieve the …
Physical-Based Training Data Collection Approach For Data-Driven Lithium-Ion Battery State-Of-Charge Prediction, Jie Li, Will Ziehm, Jonathan W. Kimball, Robert Landers, Jonghyun Park
Physical-Based Training Data Collection Approach For Data-Driven Lithium-Ion Battery State-Of-Charge Prediction, Jie Li, Will Ziehm, Jonathan W. Kimball, Robert Landers, Jonghyun Park
Electrical and Computer Engineering Faculty Research & Creative Works
Data-Driven approaches for State of Charge (SOC) prediction have been developed considerably in recent years. However, determining the appropriate training dataset is still a challenge for model development and validation due to the considerably varieties of lithium-ion batteries in terms of material, types of battery cells, and operation conditions. This work focuses on optimization of the training data set by using simple measurable data sets, which is important for the accuracy of predictions, reduction of training time, and application to online estimation. It is found that a randomly generated data set can be effectively used for the training data set, …
Real-Time Monitoring Of Fdm 3d Printer For Fault Detection Using Machine Learning: A Bibliometric Study, Vaibhav Kisan Kadam, Satish Kumar, Arunkumar Bongale
Real-Time Monitoring Of Fdm 3d Printer For Fault Detection Using Machine Learning: A Bibliometric Study, Vaibhav Kisan Kadam, Satish Kumar, Arunkumar Bongale
Library Philosophy and Practice (e-journal)
Additive Manufacturing has wide application range including healthcare, Fashion, Manufacturing, Prototypes, Tooling etc. AM techniques are subjected to various defects that may be printing defects or anomalies in machine. There is gap between current AM techniques and smart manufacturing since current AM lacks in build sensors necessary for process monitoring and fault detection. Both of these issues can be solved by incorporating real-time monitoring into AM. So the study is carried out to identify recent work done in AM to improve current system. For this bibliometric study Scopus database is used, study is kept limited to year 2010-2021 and English …
Insights Into Twinning In Mg Az31: A Combined Ebsd And Machine Learning Study, David T. Fullwood, Andrew Orme, Isaac Chelladurai, Travis Michael Rampton, Ali Khosravani, Michael Miles, Raj K. Mishra
Insights Into Twinning In Mg Az31: A Combined Ebsd And Machine Learning Study, David T. Fullwood, Andrew Orme, Isaac Chelladurai, Travis Michael Rampton, Ali Khosravani, Michael Miles, Raj K. Mishra
Faculty Publications
To explore the driving forces behind deformation twinning in Mg AZ31, a machine learning framework is utilized to mine data obtained from electron backscatter diffraction (EBSD) scans in order to extract correlations in physical characteristics that cause twinning. The results are intended to inform physics-based models of twin nucleation and growth. A decision tree learning environment is selected to capture the relationships between microstructure and twin formation; this type of model effectively highlights the more influential characteristics of the local microstructure. Trees are assembled to analyze both twin nucleation in a given grain, and twin propagation across grain boundaries. Each …