Mechanical Engineering Commons^™

Design And Fabrication Of 3d Bioprinted Scaffold: Experimental And Machine Learning Methods, Mohan K. Dey

LSU Doctoral Dissertations

The development of reliable hydrogel-based scaffolds for extrusion bioprinting remains limited by the poor structural fidelity of low-viscosity bioinks and the lack of robust, high-throughput quality evaluation methods. This dissertation can resolve such challenges by developing the combination of optimized hydrogel formulations, cryogenic-assisted bioprinting, and artificial intelligence (AI)-based scaffold evaluation to the use of tissue engineering and preclinical cancer modelling applications. To assess the rheological behavior, printability, mechanical properties, and biocompatibility of the alginate – gelatin (Alg–Gel) system, a novel system of hydrogel was developed and characterized using Alg–Gel hydrogel system. The 7% alginate, 8% gelatin mixture was found to …

Analysis Of Electronic Control Unit Optimization On The Performance Of A 150 Cc Motorcycle Engine Using Ethanol-Blended Fuel, Agus Achmad Syaiful, Annisa Bhikuning, Muhammad Rizal Baihaqi, Muhammad Aulia Rafli

Journal of Mechanical Engineering Science and Technology (JMEST)

The use of ethanol-blended fuel in a standard vehicle can reduce combustion quality, which has an impact on decreasing vehicle performance. At the above 20% blend level, it affects vehicle performance and requires adjustments to engine settings or the Electronic Control Unit (ECU) before use on conventional engines. This study investigates the effect of aftermarket ECU settings on the performance of a 150 cc motorcycle operating on a gasoline-ethanol blend. Four fuel variations were tested: Pertalite (P100), revvo 90 (R100), and 30% ethanol blends (PE30 and RE30). This study analyzes the effects of optimizing injection duration and ignition timing settings …

An Ai And Iot Framework For Dynamic Optimization And Sustainability In Smart Cities, Zeinab E. Ahmed, Rashid A. Saeed, Salah Hagahmoodi, Mamoon M. Saeed, Khalid Hamid, Sally D. Abugasim, Eyman F. A. Elsmany

Al-Esraa University College Journal for Engineering Sciences

Smart cities are emerging as a critical solution for creating more efficient, sustainable, and comfortable urban environments. This transformation is primarily driven by the synergistic integration of the Internet of Things (IoT) and Artificial Intelligence (AI). The IoT provides a pervasive network of connected sensors that collect real-time urban data, while AI serves as the analytical engine that processes this information to optimize city-wide systems. This paper presents a comprehensive framework that leverages this AI-IoT convergence for dynamic optimization to achieve long-term urban sustainability. The framework focuses on enabling intelligent, data-driven decision-making across core urban domains. The discussion and analysis …

Nodal Elimination For E3 Hemp Mitigation: A Physics-Informed Graph Theory Approach, Connor A. Lehman, Rush Robinett, Quinnlin R. Lehman, David G. Wilson, Wayne Weaver

Michigan Tech Publications

This paper presents a comparison of methods to determine the minimum number of placement locations for neutral blocking, global linear quadratic regulator (G-LQR), and local linear quadratic regulator (L-LQR) controllers throughout a power grid to prevent transformer saturation during the onset of an E3 high-altitude electromagnetic pulse (HEMP) disturbance. Different device placement configurations yield different efficacies in E3 HEMP mitigation. The first placement method discussed is a genetic algorithm (GA), which serves as a baseline optimizer test case. The scaling and run time of the GA depend on the complexity of the objective function and often times becomes intractable as …

Me Department Bespoke Coffee Cabinet, Mickie Clyne, Stephen Tamacheepjaroen, Samuel Johnson, Willem Gray

Mechanical Engineering

This senior project involved the design, fabrication, and testing of a custom coffee cabinet for the California Polytechnic State University Mechanical Engineering Department. The project addressed challenges associated with appliance organization, storage efficiency, accessibility, and maintenance within the department’s shared breakroom space. A human-centered design process was used to identify stakeholder needs and develop engineering specifications for the final product.

The completed cabinet integrates custom woodworking, steel pipe frame construction, dedicated K-Cup storage, organized appliance placement, and a rack-and-pinion sliding platform that improves access to the Keurig coffee maker for refilling and maintenance. Design decisions were informed through stakeholder feedback, …

Development Of A Putting Green Manufacturing Process, Tabitha R. Webster

Honors Theses

Our Capstone project investigates the end to end design, development, and production of a 6‑foot long portable putting green marketed for individuals seeking a high quality, competitively priced golf product for home or office use. The capstone project examines the full lifecycle of product creation applying manufacturing principles learned through the center of manufacturing’s coursework. From the initial concept through engineering design, market research, prototyping, manufacturing optimization, and final production the project emphasizes cross‑functional collaboration across engineering, business, and accountancy majors. Methods used to gather data included marketing surveys, CAD drawings, time studies during production runs, value stream mapping, and …

Optimizing Warranty Policies For Remanufactured Products: When Should They Be Longer, Shorter, Or Identical To New Product Warranties?, Kunpeng Li, Jun-Yeon Lee

Faculty Publications

Manufacturers adopt different warranty strategies for remanufactured products, offering shorter, identical, or longer warranty periods compared to new products. However, prior research has only focused on manufacturers offering either shorter or identical warranties. In addition, the existing literature has not captured the diminishing returns of warranties, i.e., as the warranty coverage increases, its incremental benefits begin to decrease but the costs continue to increase. To address these gaps, we develop an optimization model that jointly considers pricing and warranty decisions while accounting for warranty’s diminishing effect on consumer’s willingness to pay for remanufactured products. We show that all three observed …

Reinforced Scan: A Reinforcement Learning Enabled Optimal Laser Scan Path Planning In Laser Powder Bed Fusion Additive Manufacturing, Chaoran Dou, Jihoon Chung, Raghav Gnanasambandam, Yuhao Wu, Jianzhi Li, Zhenyu James Kong

Manufacturing & Industrial Engineering Faculty Publications

Additive Manufacturing is an innovative technology that fabricates parts layer by layer. However, in Laser Powder Bed Fusion (LPBF), printed metal parts often exhibit residual stresses, deformations, and other defects due to non-uniform temperature distribution during the printing process. To mitigate these issues, an optimized scan sequence within each layer can improve thermal uniformity. Traditional optimization methods, which rely on domain knowledge and employ trial-and-error or heuristic approaches, often fail to achieve optimal solutions due to the complex nature of the problem. One major challenge in improving scan strategies lies in the vast search space required to optimize the scan …

Detecting And Repairing Conflicting Constraints In Co-Trained Physics-Informed Neural Networks For Composite Curing Processes, Cooper J. Evans

Dissertations, Master's Theses and Master's Reports

Composite materials have become a critical component of modern manufacturing, especially in the automotive and aerospace industries. The curing process for these composites has been modeled using a variety of partial differential equations representing the heat transfer and composite curing kinetics. Optimizing the applied temperature profile is critical for maximizing the efficiency and capacity of composite part manufacturers. Constraints must be placed on the inputs and outputs of the model, including but not limited to, the applied temperature profile, part temperature, and final degree of cure. Conflicting sets of constraints are easy to unknowingly impose due to the highly coupled …

Adaptive Machine Learning Framework For Microstructural Optimization And Mechanical Performance Prediction In Steels, Henry Adekola Haffner

Doctoral Dissertations

Evolution of microstructures, such as polygonal ferrite, acicular ferrite, bainite, and martensite, plays a pivotal role in determining the final microstructural and mechanical properties of steel products. Given the established inter-relationship between processing parameters, microstructure, properties, and performance, precise control of phase transformation is essential to achieve pre-determined properties. To understand transformation routes in different steel grades, time-temperature-transformation (TTT) and continuous-cooling-transformation (CCT) diagrams are necessary and can be described using the Johnson-Mehl-Avrami-Kolmogorov equation and Scheil’s additivity rule. This study presents a comprehensive computational framework for predicting and optimizing microstructure and mechanical properties in advanced high-strength steels (AHSS) using adaptive machine …

A Multiobjective Framework For Joint Coverage And Motion Planning In Uav Inspection Tasks, Luis Fernando Escobar Carvajal

Graduate Theses, Dissertations, and Problem Reports (ETD)

Unmanned Aerial Vehicles (UAVs) have become essential for data acquisition in complex 3D environments. However, traditional Coverage Path Planning (CPP) methodologies often rely on a sequential pipeline that isolates viewpoint generation from flight path routing. This decoupling fails to account for the interdependence between viewpoint distribution and minimum flight paths, effectively restricting the search space and preventing the identification of a global optimum. This dissertation proposes a unified multi-objective framework for joint coverage and motion planning. The primary contribution of this work is the transition from isolated, sequential steps to a simultaneous optimization of the number and position of viewpoints …

Multi-Objective Optimization Strategy For Component Sizing In Solar-Hydrogen Microgrids Using An Advanced Hybrid Genetic Algorithm, Dylan Jones

UNF Graduate Theses and Dissertations

This thesis presents the development of a genetic algorithm (GA) optimization framework for the design and component sizing of hybrid solar-hydrogen microgrids. The framework addresses a critical gap in research and existing commercial tools by unifying performance maximization and cost minimization objectives across both grid-tied and islanded configurations. Integrating solar photovoltaics, electrolyzers, hydrogen storage, fuel cells, and batteries, the GA employs adaptive weighting and dynamic boundary constraints to balance technical feasibility with economic efficiency. To ensure real-world viability, the algorithm relies on a novel Daylight Sun Factor (DSF) for localized solar assessment and was rigorously validated against multi-year, high-fidelity irradiance …

Mission-Focused Multidisciplinary Design Optimization Of Tilt-Rotor Evtol Propulsion System, Tyler Critchfield, Andrew Ning

Faculty Publications

Tilt-rotor propulsion system design requires a multidisciplinary approach to tackle important challenges and competing tradeoffs between disciplines. In this paper, we model rotor aerodynamics, blade structures, vehicle drag, electric propulsion, and tonal/broadband acoustics for a tilt-rotor, electric vertical takeoff and landing aircraft using low-to-mid fidelity tools. We use gradient-based design optimization with automatic differentiation and parameter sensitivity analyses to explore the design space and complex tradeoffs of tilt-rotor distributed electric propulsion systems, exploring effects of variations in payload/empty weight, battery specific energy, and blade tip speed. This framework models multiple operating points with a mission-focused objective to account for the …

Machine Learning And Multi-Scale Optimization For Control And Energy Management In Connected And Automated Vehicle Propulsion Systems, Joshua D. Orlando

Dissertations, Master's Theses and Master's Reports

This dissertation presents a multi-scale optimization framework leveraging machine learning (ML) to enhance energy efficiency in connected and automated vehicle (CAV) propulsion systems. As transportation transitions toward hybridization and automation, the integration of vehicle-to-everything (V2X) connectivity and advanced control algorithms offers unprecedented opportunities for energy reduction. This research addresses three critical scales of vehicle energy management: multiple vehicle-level coordination, component-level powertrain dynamics, and real-time vehicle parameter estimation.

First, the research investigates the energy consumption characteristics of heterogeneous propulsion systems—ranging from internal combustion engines to battery electric vehicles across light- and heavy-duty sectors—on arterial roadways. Utilizing Particle Swarm Optimization (PSO) and …

Graph Based Planning With Guarantees, Trazon Tyrik Jimerson

Mechanical Engineering ETDs

This thesis presents two graph-based methods with formal guarantees for motion planning and routing. First, the invariant-set motion planner (ISMP), which uses constraint admissible positive invariant (CAPI) sets of closed-loop dynamics, is adapted for spacecraft attitude planning to avoid moving keep-out zones. Contributions include time bounds for maneuvers via exponential stability, a single-stage reachability graph from one-step backward reachable CAPI sets, and its multi-stage expansion to certify node safety over time. Simulations verify safe attitude control with moving obstacles. Second, we formulate a convex optimization problem over a network for evacuation planning with operational constraints such as helicopter capacity and …

Characterizing Surface Waviness Of Aluminum Alloy: An Approach To Minimize Post-Processing In Wire Arc Additive Manufacturing (Waam) Production, Shammas Mahmood Shafi, Anis Fatima, Nicholas V. Hendrickson

Michigan Tech Publications

Wire Arc Additive Manufacturing (WAAM) offers high deposition rates and cost-effective production of large metal components but suffers from poor surface quality, particularly surface waviness, which increases post-processing requirements and limits industrial adoption. Since waviness directly impacts structural integrity, resource efficiency, and industrial applicability, understanding how process parameters govern this feature is critical for reducing post-processing requirement. This study systematically investigated the influence of voltage, travel speed, and wire feed speed on surface waviness in aluminum alloy walls fabricated by WAAM. A two-level factorial design with 16 experiments was conducted, and surface waviness was quantified using height gauge measurements relative …

Pressure And Force Dynamics In Artificial Muscle Actuators: A State-Space And Optimization-Based Approach, Mohammad Elzein

Dissertations and Theses

This two-part investigation explores the dynamic behavior of braided pneumatic actuators (BPAs) under bio-inspired pulse modulation, with the aim of improving their biomimetic force output and control. The first study examines the effect of pulse length and inter-pulse timing on BPA performance, revealing that force output is highly sensitive to the temporal structure of input pulses mirroring biological muscle behavior. Using dual-pulse actuation schemes, the results demonstrate that force responses exceed the additive contributions of individual pulses, with peak amplification occurring consistently at a 27 ms inter-pulse gap. Shorter pulse lengths (10–20 ms) yielded the highest normalized force increases, up …

Genetic Algorithm Optimization To Maximize Sensitivity Of Triboelectric Mems Accelerometers, Yu Tian, Benyamin Davaji, Shahrzad Towfighian

Mechanical Engineering Faculty Scholarship

A genetic algorithm is used to optimize the sensitivity of a triboelectric MEMS accelerometer fabricated using CMOS-compatible processes. The optimization focuses on the structural parameters of an awl-shaped serpentine microspring (ASSM) connected to the proof mass. Constraints are defined by microfabrication standards and the available chip area. Three different constraint problems are considered, and the algo- rithm yields designs with maximized sensitivity in each case. Simulations under various sinusoidal vibration scenarios show that the optimized designs significantly outperform the previous version, achieving a 2.72× improvement at 1000 Hz and up to 20× at 500 Hz.

A Provable Semi-Infinite Programming Approach For Solving Dynamic Nash Games, Tyler C. Gardner

All Graduate Theses and Dissertations, Fall 2023 to Present

Many engineering problems must account for the non-cooperative decisions and actions of multiple players. These problems can be modeled within a game-theoretic framework. The approach herein is to model problems as Nash games, convert them to semi-infinite programs, and leverage provable semi-infinite algorithms to solve the original problem. A particular algorithm that leverages off-the-shelf solvers is used to solve four low-dimensional benchmark problems successfully. Two types of linear quadratic dynamic games are then investigated: ones where each player’s problem is convex and ones where at least one player’s problem is nonconvex. Within each type, variations based on information structure, communication …

Impact Of Print Speed And Nozzle Temperature On Tensile Strength Of 3d Printed Abs For Permanent Magnet Turbine Systems, Wirawan Wirawan, Hilmi Iman Firmansyah, Satworo Adiwidodo, Mohammad Sukri Mustapa

Journal of Mechanical Engineering Science and Technology (JMEST)

Operational parameters must be integrated into turbine systems' main components, which are determined by turbine systems' functional requirements. The need for producing component designs more effectively raises the possibility of using additive manufacturing. The study focuses on the optimization of the mechanical properties of the principal components of magnetic turbines manufactured with 3D printers using Acrylonitrile Butadiene Styrene (ABS), by changing the temperature and speed of the nozzle. The approach consisted of modeling a standard test piece in CAD software and producing ABS-based test pieces using a 3D printer with print speeds of 50, 70, 90, and 110 mm/s and …

Addressing Urban Traffic Congestion: A Deep Reinforcement Learning-Based Approach, Tairan Liu

Mineta Transportation Institute

In an innovative venture, the research team embarked on a mission to redefine urban traffic flow by introducing an automated way to manage traffic light timings. This project integrates two critical technologies, Deep Q-Networks (DQN) and Auto-encoders, into reinforcement learning, with the goal of making traffic smoother and reducing the all-too-common road congestion in simulated city environments. Deep Q-Networks (DQN) are a form of reinforcement learning algorithms that learns the best actions to take in various situations through trial and error. Auto-encoders, on the other hand, are tools that help simplify complex data, making it easier for the DQN to …

Lifting-Line Predictions For The Ideal Twist Effectiveness Of Spanwise Continuous And Discrete Control Surfaces, Zachary S. Montgomery, Douglas F. Hunsaker, James J. Joo

Mechanical and Aerospace Engineering Faculty Publications

Modern materials and manufacturing technologies have allowed the construction of morphing wings that are able to continuously vary certain airfoil parameters such as twist, camber, or control surface deflection as a function of span. This work presents a twist effectiveness parameter as a means of comparing the ideal aerodynamic efficiency of spanwise continuous control surfaces (morphing wings) and spanwise discrete control surfaces (standard wings). A numerical algorithm is used to compute the twist effectiveness of both continuous and discrete control-surface designs over a wide range of planform shapes with evenly spaced actuation for inviscid, incompressible flow. Results included here show …

Multi-Objective Design Optimization Of Hypoid Geared Rotor Systems, Xinqi Wei

Mechanical and Aerospace Engineering Dissertations - Archive

Hypoid gears represent one of the most generalized and complex forms of gearing, widely used for power transmission of skew shafts in vehicles, aviation, and marine transmission applications. Optimizing their performance remains challenging due to the complex tooth surface and contact behavior. Specifically, the design parameters of the tooth surface are multi-scale, interdependent, and subject to strong constraints, leading to strong nonlinearity and an ill-conditioned Jacobian matrix in the parameter identification model. Moreover, feasible and insensitive contact conditions are difficult to constrain due to the inherent complexity of local conjugate contact between the meshing surfaces. These challenges significantly increase optimization …

Utilizing Optimization Tools For Passive Flow Control Passage Loss Reduction In Low-Pressure Turbines, Bryant Robert Duane Burton

Browse all Theses and Dissertations

Low-pressure turbines (LPTs) play a crucial role in fuel efficiency and thrust generation of aero-engines. Traditional LPT designs, however, involve multiple stages and numerous blades, resulting in increased weight and manufacturing costs. The challenge of modern- day researchers is to reduce the weight and cost but maintain the high efficiency of LPTs. To address this, one approach is to increase the aerodynamic loading of individual blades, reducing the blade count. However, this can lead to increased secondary losses caused by flow separation, particularly in the endwall regions. This research focuses on optimizing the blade profile at the junction with the …

Application Of Genetic Algorithm In Container Vessel Stowage Planning With Carbon Tax Considerations, Ming-Feng Yang, Wei-Hao Su, Ko-Meng Hu, Yu-Hsuan Li

Journal of Marine Science and Technology–Taiwan

This study developed a method for optimizing stowage planning for container vessels, a crucial aspect of international trade logistics. Over 80% of global trade depends on containerized transportation; thus, effective stowage planning is essential for minimizing transportation costs and enhancing operational efficiency. In the proposed hybrid optimization approach, integer programming is combined with a genetic algorithm to generate optimal stowage plans. The key factors considered in this method include load capacity limits, stacking constraints, and carbon tax regulations. The proposed method involves maximizing space utilization while minimizing logistics costs, with particular emphasis on reducing port dwell times. The findings of …

Machine Learning-Driven Process Analysis And Optimization In Solid-State Welding And Fusion-Based Additive Manufacturing, Radif Uddin Ahmed

Master's Theses

In the modern era of advanced manufacturing, optimizing process parameters is pivotal in ensuring the quality and reliability of sophisticated component fabrication. This study presents a novel, data-driven approach to parameter optimization in two cutting-edge manufacturing techniques: Friction Stir Welding (FSW) and Laser Powder Bed Fusion (LPBF). By leveraging machine learning methodologies, this research addresses the critical challenge of efficiently determining optimal process parameters, a task traditionally relying on time-consuming and resource-intensive trial-and-error methods. This study will lead to a robust data-driven framework for process analysis of more advanced manufacturing techniques like the Additive Friction Stir Deposition (AFSD) process. Friction …

Robust Data-Driven Run-To-Run Control Via One-Step Constrained Optimization For Automated Serial Sectioning, Rhianna M. Oakley

Mechanical Engineering ETDs

This Thesis develops a one-step predictive run-to-run controller (R2R-MPC) for automating a mechanical serial sectioning (MSS) system. MSS is a destructive material analysis process that iteratively removes slices of material and captures 2D images, reconstructing them into 3D representations. Commonly used in material science for characterizing materials and failure analysis, MSS typically operates in an open-loop fashion, which experiences high variability in material removal due to system disturbances. To address this, a robust closed-loop R2R-MPC is presented, modeling MSS process uncertainty using a linear differential inclusion identified from operational historical data. The R2R-MPC is posed as an optimization problem that …

Optimal Control Of Nonlinear, Nonautonomous, Energy Harvesting Systems Applied To Point Absorber Wave Energy Converters, Houssein Yassin, Tania Demonte Gonzalez, Kevin Nelson, Gordon Parker, Wayne Weaver

Michigan Tech Publications

Pursuing sustainable energy solutions has prompted researchers to focus on optimizing energy extraction from renewable sources. Control laws that optimize energy extraction require accurate modeling, often resulting in time-varying, nonlinear differential equations. An energy-maximizing optimal control law is derived for time-varying, nonlinear, second-order, energy harvesting systems. We demonstrate that sustaining periodic motion under this control law when subjected to periodic disturbances necessitates identifying appropriate initial conditions, inducing the system to follow a limit cycle. The general optimal solution is applied to two point absorber wave energy converter models: a linear model where the analytical derivation of initial conditions suffices and …

Nonlinear Model Predictive Control Of Heaving Wave Energy Converter With Nonlinear Froude–Krylov Forces, Tania Demonte Gonzalez, Enrico Anderlini, Houssein Yassin, Gordon Parker

Michigan Tech Publications

Wave energy holds significant promise as a renewable energy source due to the consistent and predictable nature of ocean waves. However, optimizing wave energy devices is essential for achieving competitive viability in the energy market. This paper presents the application of a nonlinear model predictive controller (MPC) to enhance the energy extraction of a heaving point absorber. The wave energy converter (WEC) model accounts for the nonlinear dynamics and static Froude–Krylov forces, which are essential in accurately representing the system’s behavior. The nonlinear MPC is tested under irregular wave conditions within the power production region, where constraints on displacement and …

Utilizing Bayesian Optimization In Technoeconomic Analyses For Integrated Energy Systems, Anthoney Griffith

All Graduate Theses and Dissertations, Fall 2023 to Present

Technoeconomic analysis is a key element in the study of integrated energy systems. The goal of this analysis is the sizing of technologies resulting in the best economic outcome for the system. The evaluation of this system involves sizing the components and simulating the resulting market to determine an outcome. This simulation incorporates multiple possible values of uncertain parameters like grid price and wind generation. This problem is currently approached with the gradient descent optimization method. An alternative approach, Bayesian optimization, sees success on simple problems of a similar nature to technoeconomic analyses. These results motivate applying Bayesian optimization as …