Manufacturing Commons^™

Thermal Monitoring Of Solidification In Molded Casting Of Drone Landing Leg, Sadettin Cem Altıparmak, Maher Zayed, Samuel Hawk, Mohamed Baddi, Jesus Degollado, Ahmed Bendaouia, Jianzhi Li

Manufacturing & Industrial Engineering Faculty Publications

This paper presents the design, fabrication, and thermal characterization of an acrylonitrile butadiene styrene (ABS) mold produced by fused deposition modeling (FDM) extrusion-based additive manufacturing used for the wax casting of a drone landing leg. A thermal monitoring framework, consisting of four K-type thermocouples logged at 1 s intervals, was deployed across six casting trials to capture the complete solidification cycle, yielding 84,717 time-stamped measurements. The study focuses on thermal data analysis and materials science interpretation through descriptive statistics, normality diagnostics, inter-trial Kruskal–Wallis tests, phase-duration analysis, rolling-variance monitoring, and inter-channel Pearson correlation. Key findings include a persistent spatial temperature hierarchy …

Optimization Of Gas Consumption, Cost And Production Rate For Computerized Numerical Control Oxy-Acetylene Flame Cutters, Eustace K. William, Simon I. Marandu, Enock W. Nshama

Tanzania Journal of Science

This study examined the impact of flame cutting parameters (i.e., cutting speed, plate thickness and nozzle diameter) on oxy-acetylene gas consumption, cost and production rate. A full factorial design of experiments was used to generate 27 experiments, which were conducted using a CNC flame cutter. The analysis of variance (ANOVA) method was used to determine significant process parameters, followed by regression analysis using the MINITAB ® software. The technique for order preference by similarity to ideal solutions (TOPSIS) was used to determine the optimal cutting parameters for minimizing the consumption and cost estimation of oxy- acetylene gas and maximizing the …

Innovative Controls For Combustible Particulate Dust In Industrial Refineries, Sadie Dickman

Discovery Day - Daytona Beach

This project entails an extensive analysis of the occupational combustible dust hazard that exists in industrial refineries, including food processing, manufacturing, and metalworking facilities. Based on prior accidents like the 2008 Imperial Sugar and 2017 Didion Milling explosions, combustible dust causes fire and explosion risks that can result in damages, injuries, and fatalities. Through comparison of OSHA regulations, NFPA standards, and accident data, a lack of effective standards regarding training, audits, ventilation, fire prevention, and adequate housekeeping measures were highlighted to be an existing safety gap. The purpose of this study is to propose administrative controls to mandate companies to …

A Novel Hexagonal-Zigzag Cellular Infill Structure For Additive Manufacturing, Md. Saidur R Roney, Amm Nazmul Ahsan, Prosenjit Barua

Manufacturing & Industrial Engineering Faculty Publications

The rigidity of the Additively Manufactured objects can be tailored by manipulating the infill lattice type and density. In this research, an island type novel infill structure termed as Hexagonal-Zigzag pattern is introduced, and its mechanical performance is investigated. In this pattern, the zigzag raster reflects the repeating hexagonal shaped cell constituting the parallel-oriented islands and 90° rotation of the pattern in each layer distributes the island span along both transverse and longitudinal directions of the printing contour. A mathematical model is established to illustrate the effect of the infill parameters on hexagon unit cell size and relative infill density. …

Investigation Of Process Parameters To Fabricate Tiwmo Refractory Medium Entropy Alloy Via Laser Powder Bed Fusion, Abdullah Al Masum Jabir, Lindsey A. Salazar, Jianzhi Li

Manufacturing & Industrial Engineering Faculty Publications

This paper presents an experimental study on the fabrication of a TiWMo refractory medium-entropy alloy (RMEA) using laser powder bed fusion (PBF-LB/M, commonly known as selective laser melting) from elemental powders as well as successful alloy formation on titanium substrates. The effects of tungsten particle size and process parameters on successful TiWMo RMEA fabrication have been explored using scanning electron microscopy (SEM), x-ray diffraction, hardness measurement and microstructural analysis. Scanning electron microscope (SEM) analysis revealed that the lowest percentage (0.01%) of unmelted tungsten particles was observed at a laser power of 350 W and scanning speed of 250 mm/s, particularly …

Data Driven Estimation Of Pore Size Using 1d Light Emissions For Laser Powder Bed Fusion Additive Manufacturing, Jose Galarza, Jorge Barron, Luis Jimenez, Tamer Oraby, Jianzhi Li, Farid Ahmed

Manufacturing & Industrial Engineering Faculty Publications

The quality assurance of the Laser Powder Bed Fusion Process (LPBF) has been extensively investigated over the last decade for in-situ monitoring of metal additive manufacturing. The process inherently generates voids within the bulk of the part, which can detrimentally affect the quality of the printed part. The characterization of these voids by estimating their size and identifying their geometrical features remains a challenge. This study introduces a Machine Learning (ML) based framework for estimating void sizes of varying geometries using layer-wise one-dimensional (1D) average light intensity signal obtained from the optical tomography system during the 3D printing of metallic …

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 …

Development Of A Putting Green Manufacturing Process, Jose Andres Cepeda Santiago

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 controlled documentation …

Introduction To Computer-Aided Design Using Solidworks® : A Structured Approach To Parametric Modeling And Design Intent, Swapnil Moon

Open and Affordable Textbooks

This textbook introduces computer-aided design (CAD) using SOLIDWORKS® through a structured, design-centered approach that emphasizes parametric modeling and engineering reasoning. Rather than focusing solely on software commands, the material develops foundational skills in design intent, constraint-based modeling, and feature relationships to create robust, adaptable models.

The content is organized progressively, beginning with basic sketching and feature creation and advancing to complex part modeling, assemblies, motion studies, and engineering drawings. Each chapter builds upon prior concepts, reinforcing systematic modeling practices and promoting the development of system-level thinking required in real-world engineering design workflows.

Designed for undergraduate students with little or no …

Manufacturing Systems: Characteristics And Dynamics, Alan J. Fitzmorris

Graduate Theses and Dissertations (2019 - present)

This research answers three basic questions. First, what are the behaviors (dynamics) of a manufacturing system in the context of system performance? Are these dynamics best described as linear deterministic, periodic, nonlinear deterministic, or stochastic? Second, what are the complexity (chaotic) characteristics of a manufacturing system, namely the maximal Lyapunov exponent, correlation dimension, and Kolmogorov-Sinai entropy? Third, is there a relationship between complexity characteristics and manufacturing system performance? This research involves four high level steps: data analysis, system dynamics analysis, system complexity analysis, and correlation analysis. The data analyzed consists of concrete plant and shipbuilding shop time series performance data. …

Implementation Of The Fourth Industrial Revolution Technologies In Tanzania’S Downstream Oil And Gas Industries, Vitalis John Mwinyi

Tanzania Journal of Engineering and Technology (TJET)

The advent of the fourth industrial revolution (IR4.0) has resulted in the digital transformation of multiple sectors of the economy due to the disruptive nature of the technologies driving the revolution. The technologies have already found their way into the oil and gas industries across the globe, especially in upstream, midstream, and downstream operations. However, the degree of implementation is not the same for these operations, considering the complexities involved, integrated process, the associated risk and investment cost, among others. This study aimed to establish the implementation level of IR4.0 technologies in the downstream operations. A survey method was employed …

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 …

Layer-Wise Printing Parameter Optimization For Laser Powder Bed Fusion, Chaoran Dou, Rongxuan Wang, Raghav Gnanasambandam, Jianzhi Li, Zhenyu James Kong

Manufacturing & Industrial Engineering Faculty Publications

Additive manufacturing (AM) is a transformative technology that enables the fabrication of complex geometries layer by layer. However, metal parts produced via AM processes such as laser powder bed fusion (LPBF) are prone to various defects, including porosity and deformation. These defects often result from suboptimal printing parameter settings. Traditional approaches typically aim to reduce defects by optimizing a fixed set of parameters for the entire part. However, such methods do not account for layer-wise variations in printing conditions caused by changes in geometry, heat transfer, and re-heating effects. While optimizing parameters for each layer could improve part quality, it …

Reinforcement Learning For Imbalanced Data In Robotic Anomaly Detection Within Autonomous Manufacturing, Salma Messaoudi, Ahmed Bendaouia, El Hassan Abdelwahed, Mohammed Ameksa, Hajar Mousannif, Jianzhi Li

Manufacturing & Industrial Engineering Faculty Publications

Ensuring reliable anomaly detection in industrial robots is critical for safe and autonomous manufacturing operations. However, it remains challenging due to temporal dependencies and class imbalance in sensor data. This study presents a reinforcement learning approach using Deep Q-Network (DQN) enhanced with Long Short-Term Memory (LSTM) and Gradient Boosting Machine (GBM) for robust anomaly detection in robotic systems. The proposed framework integrates an LSTM into the DQN policy to capture temporal patterns. It also introduces a novel GBM-based reward mechanism that mitigates class imbalance by applying SMOTE (Synthetic Minority Over-sampling Technique) after removing temporal dependencies. Experimental results demonstrate that this …

Enhancement Of Plastic Deformation In Ultrasound-Assisted Cold Spray Of Tungsten: A Molecular Dynamics Study, Md Tusher Ahmed, Farid Ahmed, Jianzhi Li

Manufacturing & Industrial Engineering Faculty Publications

Tungsten (W) is widely valued for its exceptional thermal stability, mechanical strength, and corrosion resistance, making it an ideal candidate for high-performance military and aerospace applications. However, its high melting point and limited room-temperature plasticity pose significant challenges for processing W using additive manufacturing (AM). Cold spray (CS), a solid-state AM process that relies on high-velocity particle impact and plastic deformation, offers a promising route for additive manufacturing of W, yet conventional CS fails to induce sufficient plastic deformation for effective bonding. In this study, we employ atomistic simulations to investigate the effect of ultrasonic perturbation in …

Artificial Intelligence And Additive Manufacturing As A Coupled Design System: Rethinking Inference, Manufacturability, And Design Education, Charul Chadha, Garth Crosby, Sabit Ekin, Mohamed Gharib, Eman Hammad, Congrui Jin, Ali Ahmad Malik, Noemi Mendoza Diaz, Calahan Mollan, Monsuru Ramoni

Manufacturing & Industrial Engineering Faculty Publications

Artificial intelligence (AI) is becoming deeply integrated into additive manufacturing (AM) workflows, reshaping how designers approach geometry, materials, and process constraints. AI holds significant potential by accelerating design exploration, revealing complex patterns in AM behavior, and supporting earlier assessment of manufacturability. At the same time, it introduces new risks related to model transparency, data quality, physical validity, and the potential for overreliance by students and practitioners. This perspective examines these issues through four guiding questions that address the role of AI in AM-enabled design, the gaps that limit or enable AI contribution, the implications for engineering education, and the responsibilities …

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 …

Harnessing Ml And Iiot For Traceability In Continuous Production Systems: A Conceptual Framework, Kholoud M. Abdelaal

Theses and Dissertations

In the era of rapid technological advancement, the manufacturing sector faces increasing pressure to leverage emerging technologies to enhance operational efficiency and minimize waste. In this context, traceability plays a pivotal role, as it provides complete visibility of processes and products throughout manufacturing systems, enabling them to identify areas for improvement and take corrective actions accordingly. Additionally, traceability ensures compliance, supports product recalls, provides a clear understanding of the system’s performance, and enables fact-driven decision-making in multiple aspects of the manufacturing system. Although the broad spectrum of traceability applications in batch production-based plants, traceability remains challenging to achieve in continuous …

Direct Ink Writing Of Shear Exfoliated Two-Dimensional Nanomaterial- Elastomeric Multifunctional Nanocomposite, Md Abdur Rahman Bin Abdus Salam, Asif Hasan Ridoy, A K M Abirul Haque, Muhammad Shahbaz Rafique, Md Arafat Hossain, Md Shahriar Forhad, Matthew G. Boebinger, Farid Ahmed, Karen Lozano, Ali Ashraf

Manufacturing & Industrial Engineering Faculty Publications

Direct ink writing (DIW) of polymer nanocomposites with high loadings of two-dimensional (2D) nanofillers (graphene and hexagonal boron nitride (hBN)) is challenging because of potential clogging, use of hazardous solvents, and agglomeration. Here, in this work, a shear exfoliation and sieving method to prepare DIW ink with high loading of nanofillers produced from low-cost bulk layered materials such as graphite and bulk hBN powder for successful DIW printing without the use of any solvents, binders, or plasticizers. The single-step exfoliation technique resulted in a composite with substantial layer reduction along the c-axis, as confirmed by SEM, TEM, XRD, and Raman …

Ai-Based Porosity Detection In Babbitt Bore Turning, Kaylee Leggett, Hannah Adams Gemmell, Kaisa Shingleton

Williams Honors College, Honors Research Projects

For this project, an external company reached out to the University of Akron requesting assistance with defect detection during their vertical turning operations. As babbitt is removed in a vertical turning process, it occasionally reveals defects, mainly porosity, which can lead to costly downstream failures of the part. Current inspection techniques involve use of dye penetrant, which is time consuming, labor intensive, unergonomic, and a source of human error. The goal of the project is to create an alternative inspection method using an AI-based machine-learning model. After the turning operation, a camera is deployed to perform an in-place inspection, taking …

Detecting Non-Axisymmetric Instabilities In Fluid-Based Manufacturing Via Multi-View Tensor Analysis., Bidusi Khadka

Electronic Theses and Dissertations

Fluid-based manufacturing processes, such as inkjet printing and electrospinning, fabricate micro- and nano-scale structures with high precision, but are prone to complex fluid dynamics exhibiting axisymmetric and non-axisymmetric instabilities. Conventional monitoring often relies on single-camera inputs and symmetry assumptions, limiting the detection of three-dimensional anomalies like jet deflection. This study presents a novel multi-view streaming video-based anomaly detection framework to address this gap. The framework employs a modified Tensor Sequential Sampling (TSS) algorithm with edge-based sampling to capture geometric spatiotemporal features of each camera view using the videos obtained from an orthogonally positioned dual-camera setup. These features are fused with …

Full Factorial Design Of Carbon Content And Heating Temperature On Height Reduction In Bonnell Springs, Yurida Ekawati, Cendana Anggun Sasmitha, Mochamad Syamsul Ma’Arif

Journal of Mechanical Engineering Science and Technology (JMEST)

The durability of Bonnell springs, which are widely used in the manufacture of spring beds, is often compromised by height reduction during use, which negatively impacts product comfort and quality. This study aims to minimize spring height reduction by investigating the effects of heating temperature and carbon content in the spring steel. A full factorial experimental design was applied using two factors: heating temperature (250°C, 260°C, and 270°C) and carbon content (0.72%, 0.73%, and 0.74%). Nine treatment combinations were tested, with five replicates each, and the height reduction values were measured after 100 compression cycles. The data were analyzed using …

Experiential Learning And The Revitalization Of Manufacturing Education At The University Of Dayton, Sean Cahill

Research and Reflection on Learning and Teaching in Higher Education

This perspective paper explores the role of experiential learning in preparing future-ready manufacturing engineers at the University of Dayton, set against the backdrop of Dayton’s legacy as an industrial innovator and the broader national movement to revitalize domestic manufacturing. As automation, cyber-physical systems, and Industry 4.0 technologies reshape the manufacturing landscape, there is a growing need for engineers who possess both technical fluency and systems-thinking capabilities. To meet this need, the manufacturing engineering technology department implemented hands-on, integrated lab-and-lecture modules through support from the Experiential Learning Innovation Fund for Faculty (ELIFF). Grounded in Kolb’s Experiential Learning Theory and constructivist learning …

Intelligent Real-Time Flotation Froth Stability Monitoring Using Embedded Computer Vision: Lead (Pb) Processing Case Study, Ahmed Bendaouia, Taha Ismaili, Ismail Najib, Oussama Hasidi, Intissar Benzakour, Jianzhi Li, El Hassan Abdelwahed

Manufacturing & Industrial Engineering Faculty Publications

Froth stability in flotation refers to the ability of the froth layer to remain intact and avoid collapsing during the flotation process. A stable froth layer is crucial to maximizing mineral recovery and ensuring the efficient operation of flotation cells. Several factors influence the stability of the froth, including the depth of the froth, the crowding, the size of the bubbles, and the rate of movement of the froth. In this study, our main objective is to analyze and evaluate froth stability in real time, since it plays a critical role in process optimization for flotation-based mineral processing. We propose …

Process-Driven Manufacturability Constraints In Design For Wire Arc Additive Manufacturing, Monsuru Ramoni, Sampson Gholston, Albert E. Patterson

Manufacturing & Industrial Engineering Faculty Publications

Wire arc additive manufacturing (WAAM) has emerged as a useful option for large-scale metal additive manufacturing. It has gained widespread use in the aerospace industry and other applications that require large and complex custom parts. The process is a combination of a precise control system and a welding process, typically GTAW or MIG welding. It is a member of the directed energy deposition (DED) family of AM processes. Compared with many metal AM processes, it is relatively simple and cost-effective but almost always requires a significant amount of postprocessing before parts can be used. The AM-based nature of the process …

Transparent Eeg Analysis: Leveraging Autoencoders, Bi-Lstms, And Shap For Improved Neurodegenerative Diseases Detection, Badr Mouazen, Ahmed Bendaouia, Omaima Bellakhdar, Khaoula Laghdaf, Aya Ennair, El Hassan Abdelwahed, Giovanni De Marco

Manufacturing & Industrial Engineering Faculty Publications

Highlights

• Novel hybrid architecture: Combined autoencoders with bidirectional LSTM networks for enhanced EEG signal classification, achieving 98% accuracy in distinguishing AD, FTD, and healthy controls.

• Explainable AI integration: Implemented SHAP (SHapley Additive exPlanations) framework to enhance model transparency and identify entropy as the most influential feature for neurodegenerative disease detection.

• Optimal temporal segmentation: Demonstrated that 5-s EEG windows with 50% overlap provide the best balance between classification accuracy and computational efficiency.

• Comprehensive feature extraction: Utilized Power Spectral Density (PSD) analysis across standard frequency bands (Delta, Theta, Alpha, Beta, Gamma) following autoencoder-based dimensionality reduction.

• Superior performance validation: Outperformed traditional machine learning …

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Using Statistical Clustering Of Trajectory Data To Support Analysis Of Subject Movement In A Virtual Environment, Martin Galicia Avila, Douglas Timmer, Alley C. Butler

Manufacturing & Industrial Engineering Faculty Publications

Gracia de Luna conducted experiments with an HMD virtual environment in which human subjects were presented with surprise distractions. His collected data for head, dominant hand, and non-dominant hand included 6 DOF human subject trajectories. This paper examines this data from 57 human subject responses to those surprise virtual environment distractions using statistical trajectory clustering algorithms. The data is organized and processed with a Dynamic Time Warping (DTW) algorithm and then analyzed using the Density Based Spatial Clustering (DBSCAN) algorithm. The K-means method was used to determine the appropriate number of clusters. Chi Squared goodness of fit was used to …

Human Comfort Modeling, Measurement, And Improvement In Human–Robot Collaboration, Yuchen Yan

All Dissertations

A dissertation is proposed to explore human comfort in human-robot collaboration (HRC) through modeling, prediction, and enhancement methodologies. Human comfort is a crucial yet underexplored factor in HRC, directly influencing task efficiency, trust, and overall collaboration effectiveness. Understanding the influential factors, developing computational models, and refining methods to improve human comfort in HRC are essential steps toward advancing the field of collaborative robotics. To address these challenges, multiple studies have been conducted. A series of experimental studies were performed to investigate how robot motion-based parameters affect human comfort in HRC. These studies examined both analytical comfort modeling approaches and physiological …

Machine Learning And Clinical Eeg Data For Multiple Sclerosis: A Systematic Review, Badr Mouazen, Ahmed Bendaouia, El Hassan Abdelwahed, Giovanni De Marco

Manufacturing & Industrial Engineering Faculty Publications

Multiple Sclerosis (MS) is a chronic neuroinflammatory disease of the Central Nervous System (CNS) in which the body’s immune system attacks and destroys the myelin sheath that protects nerve fibers, leading to a wide range of debilitating symptoms and causing disruption of axonal signal transmission. Accurate prediction, diagnosis, monitoring and treatment (PDMT) of MS are essential to improve patient outcomes. Recent advances in neuroimaging technologies, particularly electroencephalography (EEG), combined with machine learning (ML) techniques — including Deep Learning (DL) models — offer promising avenues for enhancing MS management. This systematic review synthesizes existing research on the application of ML and …

A Machine Learning Approach To Detect Pores In Laser Powder Bed Fusion Additive Manufacturing, Jose Galarza, Jose Barron Jr., Luis Jimenez, Tamer Oraby, Jianzhi Li, Farid Ahmed

Manufacturing & Industrial Engineering Faculty Publications

Real-time detection of pores in the Laser Powder Bed Fusion (LPBF) metal Additive Manufacturing (AM) process is proposed in this study and can be utilized for in-situ process monitoring and quality control. The average light emission data from the process captured by an optical tomography camera can be integrated into a defect detection module to characterize defects after the deposition of a layer. The light emission contains information on the process zone which could be extracted with the appropriate data techniques. In this paper, we proposed a machine-learning approach that utilizes the mean light intensity data from the melt-pool monitoring …