Engineering Commons^™

Design And Experimental Evaluation Of A Custom Vision-Guided Approach To 2d Part Localization For Industrial Robots, Faisal Ali

Dissertations, Master's Theses and Master's Reports

Vision-guided robots offer a clear advantage over teach-pendant programming for battery handling, where modern packs hold thousands of cells and teaching each position by hand does not scale. Commercial vision systems address this need, but their calibration, detection, and coordinate-conversion stages are closed to the user, making it hard to incorporate newer learning-based methods. This work presents a modular, custom-built vision-guided system using an Orbbec Gemini 435Le eye-in-hand camera, an NVIDIA Jetson Orin Nano, an Allen-Bradley Micro850 PLC, and a FANUC LR Mate 200iC, communicating over Modbus TCP and EtherNet/IP. A per-hole classification model resolves each known hole into a …

Design And Validation Of A Low-Cost Wearable Electromyography (Emg) System For Monitoring Exercise-Induced Changes In Muscle Activity, Ingrid E. Halverson

Dissertations, Master's Theses and Master's Reports

Wearable technologies have expanded opportunities for monitoring athletic performance, but many existing systems remain costly and confined to laboratory or medical settings. This thesis presents the design, development, and evaluation of a low-cost, wearable EMG platform for monitoring neuromuscular activity during exercise. A wireless device incorporating a surface EMG sensor, an ESP32 microcontroller, and Wi-Fi transmission was developed to acquire muscle activation data. Signal processing techniques, including filtering, root-mean-square (RMS), mean frequency (MNF), and median frequency (MDF) analyses, were used to evaluate changes in muscle activation. Experimental testing demonstrated reliable wireless data acquisition and successful capture of physiological changes before …

High-Performance Circuit Manufacturing And Testing Exercises, Benjamin S. Keppers

Dissertations, Master's Theses and Master's Reports

An advanced demonstrator Printed Circuit Board (PCB) has been designed and implemented providing a framework for advancing students’ knowledge in hands-on PCB design and manufacturing process through industry recognized test coupons, stack ups, and transmission lines. Students are guided through several key aspects of design and simulation relating to manufacturing and qualifications. Manufacturing allows students to refine process development and analyze performance data with respect to qualification tests specified by Global Electronics Association standards. Results are then used to build a stackup model, and complete a design activity for calculating expected test results for a series of controlled impedance electrical …

Robust State Estimation And Scene Understanding For Autonomous Ground Vehicles In Challenging Environments, Nader J. Abu-Alrub

Dissertations, Master's Theses and Master's Reports

Autonomous ground vehicles require reliable state estimation and scene understanding to operate safely in challenging environments. Poor illumination, adverse weather, degraded visibility, snow-covered roads, and sensor noise can reduce the reliability of conventional localization and perception pipelines. Addressing these challenges requires sensing and algorithmic strategies that exploit the complementary strengths of different modalities. Radar is particularly attractive because of its resilience to lighting and weather variations, while inertial, camera, and lidar measurements provide additional motion, appearance, and geometric information. This dissertation investigates compact radar-based and sensor-fusion approaches for improving autonomous ground-vehicle state estimation and scene understanding under degraded sensing conditions. …

Beyond Receptive Measures: Development And Validation Of The Spatial Grid Drawing Assessment (Sgda) As A Productive Measure Of Spatial Skill, Katrina L. Carlson

Dissertations, Master's Theses and Master's Reports

Spatial skills are vital in many STEM disciplines, starting at or prior to university training, and often extending throughout one’s career. The most widely used spatial ability measures (e.g., PSVT: R, MRT) rely on what I refer to as receptive skills: examining an object or design, mentally transforming it, and comparing it to given alternatives. But because work in many STEM disciplines also involves productive spatial skill such as drawing, I hypothesize that a productive measure of spatial skill may predict distinct aspects of spatial ability. This research investigates the relationship between traditional receptive Spatial Visualization (SV) assessments, such as …

Antenna-Based Sensors For Dielectric Characterization Of Materials, Hilary Scott Nkimbeng Cho

Dissertations, Master's Theses and Master's Reports

This research introduces antenna-based sensors for dielectric characterization aimed at overcoming the limitations of conventional microwave sensors. Although traditional microwave sensors are widely used for their noncontact operation, high sensitivity, and ability to penetrate various materials, they often face challenges such as large size and high-power consumption. To address these issues, antenna-based sensors are explored for their compactness, ease of fabrication, and flexible design adaptability across diverse sensing applications. An in-depth analysis of multiple antenna sensor configurations is conducted, followed by the design and development of high-performance sensing structures. A saw-tooth slot antenna sensor is developed for highly sensitive liquid …

Coupled Shrinking Core And Bubble Dynamics Model For Enhancement Of Metal-Water Reactions With Acoustic Cavitation, Troy Metz

Dissertations, Master's Theses and Master's Reports

Hydrogen is a valuable fuel. Hydrogen can be produced in several ways including thermochemical cycles, electrolysis, and metal-water reactions. Thermochemical cycles typically use high temperatures, often with corrosive species. Electrolysis requires high purity water. Metal-water reactions do not have these drawbacks. Activators such as lithium and gallium are often used to enhance metal-water reactions. However, these activators add to the complexity of metal-water reactions and may not work for all metals. A novel method to improve metal-water reactions is cavitation. During bubble collapse, shock waves and microjets are produced that can cause surface erosion. The objective of this work is …

Using Automotive Lidar To Reduce The Energy Consumption Of An Ego Autonomous Vehicle, Logan P. Schexnaydre

Dissertations, Master's Theses and Master's Reports

There is significant potential to reduce the energy consumption of the transportation sector through autonomous vehicles. Prior work on autonomous vehicle energy efficiency focuses on the whole system or the control subsystem. Yet, the sensing and processing components, which have direct and indirect effects on net energy use, are less explored. This dissertation fills this gap by modeling and evaluating these effects for lidar sensors, which provide high-resolution spatial data at the cost of high power and processing demands. I apply lidar to the energy-saving tasks of automated vehicle following and road surface profiling. For automated vehicle following, I model …

Analytical And Data-Driven Modeling And Control Of Nonlinear Point Absorber Wave Energy Converters With Application To Cone–Cone Buoy Geometries, Houssein Yassin

Dissertations, Master's Theses and Master's Reports

This dissertation develops analytical modeling, numerical simulation, experimental validation, and data driven control methods for nonlinear point absorber wave energy converters, with particular emphasis on cone--cone buoy geometries. Geometry dependent nonlinear force models are first derived using pressure field and displaced volume formulations. These models show how buoy geometry produces nonlinear Froude--Krylov and restoring forces, including the dominant cubic behavior of cone--cone geometries.

The derived models are incorporated into a feedback linearization framework that compensates selected nonlinear dynamics while retaining the incident wave terms. An analytical optimal control formulation is also developed to maximize harvested energy in nonlinear, nonautonomous systems …

Methodology Development For Evaluating Relative Heat Checking Resistance Of Open-Die Forge Tooling, Jack F. Schaller

Dissertations, Master's Theses and Master's Reports

Heat checking, characterized by biaxial networks of surface cracks induced by thermomechanical cycling, can cause premature failure of open-die forge tooling. To facilitate the evaluation of heat checking resistance of die steels, a simulation‑informed out-of-phase thermomechanical fatigue (OP-TMF) testing methodology was developed using 4330V steel as a baseline material. Temperature‑dependent material properties and flow stress data were collected and compiled into a material data file for use with finite element analysis software. Forging and cooling scenarios were simulated across a range of die preheat temperatures. Temperature and in-plane strain histories extracted from the die surface provided a foundation for laboratory …

A Novel Deep Transfer Learning-Based Adaptive Cascade Pi Controller Enhanced By Reinforcement Learning Algorithm And Snake Optimization For Robust Speed Regulation Of Brushless Dc Motors, Seyyed Morteza Ghamari, Asma Aziz

Research outputs 2022 to 2026

Brushless DC (BLDC) are common in electric cars, industrial automation, and robotics because of their high efficiency, high torque control, and compact size. Nevertheless, strong speed and current regulation is not easily attained because of system variation, load variations and the shortcomings of traditional fixed-gain proportional-integral (PI) controllers. In this paper, a new snake optimization-assisted deep transfer learning-based reinforcement learning (SOA-DTL-RL)-based adaptive cascade PI controller is proposed that combines transfer learning with fast adaptation, Reinforcement learning with real-time optimization, and snake optimization with optimal initial gain selection to guarantee the robust speed and current regulation in BLDC motors. The proposed …

Experimental Investigation And Performance Optimization Of A Double Slope Solar Still Integrated With Nanoparticles In Phase Change Materials, Pronob Das, Md Shahriar Mohtasim, Utpol K. Paul, Md Sanowar Hossain, Barun K. Das, Anik Saha, Md Golam Kibria

Research outputs 2022 to 2026

By utilizing solar energy for desalination, solar stills offer a sustainable and cost-effective means of providing fresh drinking water in remote and arid regions. Existing studies have primarily focused on improving freshwater productivity while considering economic and environmental feasibility. The present work offers an in-depth assessment of solar still (SS) systems by analyzing energy and exergy performance, exergoeconomic factors (energy-economic factor, exergoeconomic factor, and cost of water), environmental impacts (CO2 emissions, exergo-environmental factor, and carbon credit gained), and sustainability indicators (energy payback time and sustainability index). Five different cases were examined: (I) a conventional solar still (CSS), (II) CSS with …

Numerically Evaluating The Effect Of Extrusion Angle On Material Flow And Thermal Behaviour During Additive Friction Extrusion Deposition (Afed), Numan Habib, Ferdinando Guzzomi, Ana Vafadar

Research outputs 2022 to 2026

Additive Friction Extrusion Deposition (AFED), also known as “SoftTouch”, is an emerging friction-based Additive Manufacturing (AM) technology allowing material to soften before the deposition, increasing the printing speed and reducing cost [1]. However, high power is required during the process, as excessive force is needed to extrude enough material through the printing head. This study investigates how extrusion angle and tool rotational speed affect thermal distribution and material flow in AFED to minimise power consumption. A three-dimensional computational fluid dynamics (CFD) model is proposed, and ANSYS ^® Workbench CFD code (Fluent) is used to discretise the CFD model. A User-Defined …

Super-Resolution Learning Inspired Spectral-Spatial Correlation Network For Hyperspectral Target Detection, Jiaping Zhong, Yunsong Li, Jianxin Li, Yanzi Shi, Weiying Xie, Paolo Gamba

Research outputs 2022 to 2026

Hyperspectral target detection (HTD) aims at extracting targets from complex backgrounds while overcoming noise interference. Existing deep learning models for HTD usually suffer from low spatial resolution and unitary representation, especially in space-borne platforms. Super-resolution, as a critical technology to enhance the spatial details, could effectively address the aforementioned issue. To make super-resolution absolutely pose positive effects on target detection, this paper proposes an end-to-end novel super-resolution learning inspired spectral-spatial correlation network for hyperspectral target detection (SR-HTD) from the perspective of spatial and spectral regularization to achieve high-precision detection. Specifically, we designed a Spatial Correlation Aggregation (SCA) module inspired by …

Additive Manufacturing And Heat Treatment Of Zero Poisson’S Ratio Self-Expanding Nitinol Stents, Farhana Yasmin, Vafadar, Majid Tolouei-Rad

Research outputs 2022 to 2026

Additive manufacturing (AM) has recently gained attention as an effective approach for printing patient-specific, self-expanding Nitinol (NiTi alloy) stents with complex structural designs for the treatment of peripheral arterial disease (PAD). However, achieving the desired phase transformation temperature and superelastic performance remains challenging due to compositional variations, phase imbalance and microstructural inhomogeneities introduced during the printing process. In this study, self-expanding Nitinol stents with a zero Poisson’s ratio (ZPR) structural design were fabricated via laser powder bed fusion (PBF-LB). The printed stents showed no evidence of cracks or structural defects, confirming PBF-LB’s capability to produce mechanically sound stent geometries. However, …

High Temperature Creep Deformation Mechanism Of Fe28.2ni18.8mn32.9al14.1cr6 High Entropy Alloy And Its Modified Alloys, Edwin S. Jiang, I. Baker

Dartmouth College Ph.D Dissertations

Fe28.2Ni18.8Mn32.9Al14.1Cr6 eutectic high entropy alloy exhibits a good combination of room-temperature and high temperature properties, including tensile strength, ductility, and corrosion resistance, making it a promising candidate for structural applications in extreme environments. However, the creep deformation behavior of this alloy— and high-entropy alloys (HEAs) in general—remains insufficiently understood. This dissertation systematically investigates the high-temperature creep deformation mechanisms of Fe28.2Ni18.8Mn32.9Al14.1Cr6 and its derivatives.

Creep mechanisms and associated microstructural changes were examined across a wide range of strain rates using strain-rate jump and constant-stress tests. Two dominant regimes were identified: dislocation glide/solute-drag at low strain rates, and dislocation climb at high …

Early-Time/High-Frequency Electromagnetic Induction Sensing For Minimal-Metal And None-Metallic Subsurface Targets, Michele Louise Maxson

Dartmouth College Ph.D Dissertations

Conventional electromagnetic induction (EMI) systems operate within the quasi-static regime, where measurements are dominated by conduction currents and are primarily sensitive to highly conductive targets and bulk soil properties. As a result, these systems exhibit limited sensitivity to low-conductivity and layered media, such as permafrost, composite materials, and minimum-metal landmines, where diagnostically relevant information resides in early-time/high-frequency electromagnetic responses, generally above 100 kHz. This limitation reflects a mismatch between conventional EMI system design and the underlying target physics, restricting the ability of standard EMI approaches to resolve fine-scale non-metallic subsurface structure.

In this thesis, I investigate early-time/high-frequency sensitivity as a …

Designing Narrative-Based Ai Assistance For Sensemaking In Collaborative Environments: Case Studies In Education And Dementia Care, Dylan Edward Moore

Dartmouth College Ph.D Dissertations

This thesis addresses a gap in the human-computer interaction literature regarding the design, development, and evaluation of narrative-based AI assistance for collaborative, complex problem solving. I explore this design space through three case studies across the domains of education and dementia care. This work encompasses multi-year industry partnerships and longitudinal fieldwork, user-centered design, dataset curation, model training, and system evaluation.

Specifically, the first case study considers a story-based web platform for teaching AI literacy through peer-generated, personalized narrative scaffolding. Learners on the platform showed significant knowledge gains and other learning-related outcomes. To describe the novel design of this system, I …