Engineering Commons^™

Characterization Of Electrophoretic Deposited Zinc Oxide Nanopartices For The Fabrication Of Next-Generation Nanoscale Electronic Applications, Fawwaz Abduh A. Hazzazi

LSU Doctoral Dissertations

Several reports state that it is crucial to analyze nanoscale semiconductor materials and devices with potential benefits to meet the need for next-generation nanoelectronics, bio, and nanosensors. The progress in the electronics field is as significant now, with modern technology constantly evolving and a greater focus on more efficient robust optoelectronic applications. This dissertation focuses on the study and examination of the practicality of Electrophoretic Deposition (EPD) of zinc oxide (ZnO) nanoparticles (NPs) for use in semiconductor applications.

The feasibility of several synthesized electrolytes, with and without surfactants and APTES surface functionalization, is discussed. The primary objective of this study …

A Deep Reinforcement Learning Approach With Prioritized Experience Replay And Importance Factor For Makespan Minimization In Manufacturing, Jose Napoleon Martinez

LSU Doctoral Dissertations

In this research, we investigated the application of deep reinforcement learning (DRL) to a common manufacturing scheduling optimization problem, max makespan minimization. In this application, tasks are scheduled to undergo processing in identical processing units (for instance, identical machines, machining centers, or cells). The optimization goal is to assign the jobs to be scheduled to units to minimize the maximum processing time (i.e., makespan) on any unit.

Machine learning methods have the potential to "learn" structures in the distribution of job times that could lead to improved optimization performance and time over traditional optimization methods, as well as to adapt …

Machine Learning Assisted Discovery Of Shape Memory Polymers And Their Thermomechanical Modeling, Cheng Yan

LSU Doctoral Dissertations

As a new class of smart materials, shape memory polymer (SMP) is gaining great attention in both academia and industry. One challenge is that the chemical space is huge, while the human intelligence is limited, so that discovery of new SMPs becomes more and more difficult. In this dissertation, by adopting a series of machine learning (ML) methods, two frameworks are established for discovering new thermoset shape memory polymers (TSMPs). Specifically, one of them is performed by a combination of four methods, i.e., the most recently proposed linear notation BigSMILES, supplementing existing dataset by reasonable approximation, a mixed dimension (1D …