Industrial Engineering Commons^™

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 …

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. …

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, 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 …

Engineering Design And Analysis Using Creo® Cad, Cae, And Manufacturing Applications, Swapnil Moon

Open and Affordable Textbooks

This textbook presents a structured approach to computer-aided design using Creo, integrating CAD, CAE, and CAM workflows within a unified engineering framework. The material emphasizes parametric modeling, design intent, and feature-based modeling as foundations for creating robust and adaptable engineering models. Through progressively structured tutorials, students develop skills in part modeling, assemblies, engineering drawings, mechanism design, simulation, and manufacturing. The text incorporates real-world engineering components and workflows, including structural and thermal analysis, motion simulation, and toolpath generation, reflecting modern engineering practice. Designed for upper-division undergraduate and graduate students, this open educational resource supports hands-on learning and prepares students for industry-relevant …

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 …

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 …

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 …

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 …

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 …

Performance, Economic And Environmental Evaluation Of A Solar-Assisted Vacuum Deaeration Heating System., Liew Shan Kun

Student Works (2020-2029)

This research evaluates the performance of a solar-assisted heating (SAH) system for makeup water in a vacuum deaeration process. The system integrates solar energy into the heating process, with parametric analysis conducted across varying configuration of total solar radiation and water flow rates. The total solar radiation at the experimental site in Kuala Kangsar, Perak, Malaysia, characterized by tropical weather, fell within the range between 414W/m2 to 568W/m2. Performance analysis showed that a flow rate of 0.4 LPM achieved the target temperature of 61°C required for the deaeration process. Thermal efficiency peaked at 62.60% under a solar radiation of 900 …

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 …

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 …

Lean Service System Optimization In U.S. Automotive Maintenance Centers: A Time Study And Simulation-Based Approach To Reducing Service Cycle Time And Increasing Efficiency, Rakibul Hasan Sarker

All Graduate Theses, Dissertations, and Other Capstone Projects

The primary objective of this study is to measure the current service time at a U.S. automobile service center, with the aim of reducing waste and optimizing service operations through time study and simulation modeling. Inefficiencies in those service centers increase service time and labor costs, reduce service quality, and reduce workshop productivity, thereby increasing customer waiting time. In this study, real-world shop floor data were collected from a single service center, namely Jiffy Lube. Over the course of ten working days, 205 vehicle data points were acquired. Service time, bay time, and overall process time were computed and examined …

A Study Of Perceptions, Readiness, Benefits, And Barriers Related To Exoskeleton Adoption In New Jersey’S Warehousing Sector, Terry Asante

Theses

The warehousing industry in New Jersey remains a vital component of the region's logistics network, employing more than 200,000 workers who routinely engage in lifting, bending, overhead reaching, and other physically demanding activities. These exposures contribute to musculoskeletal disorder (MSD) rates that exceed national averages, particularly affecting the low back and shoulders. Nationally, MSDs account for an estimated $420 billion in combined direct and indirect costs each year, underscoring the need for interventions that can effectively reduce biomechanical strain. Industrial exoskeletons have emerged as a potential solution, with prior research demonstrating reductions in muscle activation, perceived exertion, and fatigue during …

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 …

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 …

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 …

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 …

Advancing Life Cycle Assessment For Environmental Sustainability Of Carbon Fiber-Reinforced Polymer Composites (Cfrps)), Hao Chen

All Dissertations

Carbon fiber-reinforced polymer composites (CFRPs) have emerged as promising materials, particularly for lightweight applications, with the potential to reduce environmental impacts across multiple sectors, including automotive, aerospace, and renewable energy. However, fully realizing their sustainability potential requires a more comprehensive and context-specific understanding of their environmental performance throughout the entire life cycle—from raw material production to end-of-life management.

This dissertation advances life cycle assessment (LCA) practices for CFRPs by addressing key challenges across multiple phases of the CFRP life cycle. First, I conducted a critical review and meta-analysis of carbon fiber manufacturing, revealing substantial variability in reported data on energy …

Research On Technology Opportunity Identification By Integrating Technology Attribute Analysis And Competitive Environment Scanning —— A Case Study Of Industrial Robots, Daobin Gao

Journal of Scientific Information Research

[Purpose/significance] Placing technology opportunity identification in the perspective of technology attribute analysis and competitive environment scanning provides intelligence support for enterprises to select technology opportunities and formulate technology competitive strategies in complex and changing market competition environments.

[Method/process] Using patents as the data source, first, the BERTopic model is used to explore potential technical topics, Secondly, starting from the technical attributes, and from both global and local perspectives, we propose indicators of technical importance and technical attention, and use their cross screening to select candidate sets of technical opportunities, Finally, a market competition intensity index that takes into account market …

Fuzzy-Ahp Based Decision Support System For The Selection Of Optimal Maintenance Strategy For Meter Gauge Railway Infrastructure: A Review, Hamisi J. Maulid

Tanzania Journal of Engineering and Technology (TJET)

There are many uncertainties and complexities associated with maintaining Meter Gauge Railway (MGR) infrastructure, which calls for a methodical approach to decision-making. The development and application of a fuzzy-AHP-based decision support system (DSS) to select the optimal maintenance strategy for the MGR are presented in this study. The review covers research from 2013 to 2023 and focusses on the use of Multi-Criteria Decision Making (MCDM) and Fuzzy Analytic Hierarchy Process (Fuzzy-AHP) techniques in railway infrastructure maintenance. To manage the inherent uncertainties and subjective judgements involved in maintenance decision-making, the Fuzzy-AHP methodology combines fuzzy logic with the Analytic Hierarchy Process (AHP). …