Industrial Engineering Commons^™

Automated Posture Positioning For High Precision 3d Scanning Of A Freeform Design Using Bayesian Optimization, Zhaohui Geng, Bopaya Bidanda

Manufacturing & Industrial Engineering Faculty Publications and Presentations

Three-dimensional scanning is widely used for the dimension measurements of physical objects with freeform designs. The output point cloud is flexible enough to provide a detailed geometric description for these objects. However, geometric accuracy and precision are still debatable for this scanning process. Uncertainties are ubiquitous in geometric measurement due to many physical factors. One potential factor is the object’s posture in the scanning region. The posture of target positioning on the scanning platform could influence the normal of the scanning points, which could further affect the measurement variances. This paper first investigates the geometric and spatial factors that could …

Ultrafast Laser Ablation Of Inconel 718 For Surface Improvement, Sampson Canacoo, Enrique Contreras Lopez, Oscar Coronel, Farid Ahmed, Jianzhi Li, Anil K. Srivastava

Manufacturing & Industrial Engineering Faculty Publications and Presentations

Inconel 718 is considered difficult to machine because of its ability to maintain its properties at high temperatures. The low thermal conductivity of the alloy causes accelerated tool deterioration when machining. Selective laser melting (SLM) additive manufacturing introduces a possibility of eliminating these difficulties, and producing complex shapes with this difficult-to-machine material. However, high surface roughness and porosity usually occur at the surface of components produced through additive manufacturing. In this study, the surfaces of Inconel 718 samples produced through selective laser melting were treated using laser ablation. The process parameters for the laser ablation process were analyzed in …

Minimax Registration For Point Cloud Alignment, Zhaohui Geng, Mauro Garcia, Bopaya Bidanda

Manufacturing & Industrial Engineering Faculty Publications and Presentations

The alignment, or rigid registration, of three-dimensional (3D) point clouds plays an important role in many applications, such as robotics and computer vision. Recently, with the improvement in high precision and automated 3D scanners, the registration algorithm has become critical in a manufacturing setting for tolerance analysis, quality inspection, or reverse engineering purposes. Most of the currently developed registration algorithms focus on aligning the point clouds by minimizing the average squared deviations. However, in manufacturing practices, especially those involving the assembly of multiple parts, an envelope principle is widely used, which is based on minimax criteria. Our present work …

Computation Of Risk Measures In Finance And Parallel Real-Time Scheduling, Yajuan Li

Dissertations

Many application areas employ various risk measures, such as a quantile, to assess risks. For example, in finance, risk managers employ a quantile to help determine appropriate levels of capital needed to be able to absorb (with high probability) large unexpected losses in credit portfolios comprising loans, bonds, and other financial instruments subject to default. This dissertation discusses the computation of risk measures in finance and parallel real-time scheduling.

Firstly, two estimation approaches are compared for one risk measure, a quantile, via randomized quasi-Monte Carlo (RQMC) in an asymptotic setting where the number of randomizations for RQMC grows large, but …

Optimizing Incentives For Systems With Heterogeneous Agents, Chen Chen

Dissertations

This dissertation explores new models and applications based on the game theory of incentives. This exploration starts with controlling an invasive insect problem to address one of the most significant challenges facing our forests, the invasion of the Emerald ash borer (EAB), a non-native, wood-boring insect that threatens to kill most ash trees in North America, through designing two new cost-sharing programs between the landowners and local governments. Ash trees are one of North America’s most widely distributed tree genera and a vital part of the green infrastructure of cities, where they provide residents with numerous social, economic, and ecological …

Evaluating Safety And Productivity Relationship In Human-Robot Collaboration, Aayush Jain, Shakra Mehak, Philip Long, John D. Kelleher, Michael Guilfoyle, Maria Chiara Leva

Conference papers

Collaborative robots can improve ergonomics on factory floors while allowing a higher level of flexibility in production. The evolution of robotics and cyber-physical systems in size and functionality has enabled new applications which were never foreseen in traditional industrial robots. However, the current human-robot collaboration (HRC) technologies are limited in reliability and safety, which are vital in risk-critical scenarios. Certainly, confusion about European safety regulations has led to situations where collaborative robots operate behind security barriers, thus negating their advantages while reducing overall application productivity. Despite recent advances, developing a safe collaborative robotic system for performing complex industrial or daily …

Artificial Neural Networks And Gradient Boosted Machines Used For Regression To Evaluate Gasification Processes: A Review, Owen Sedej, Eric Mbonimpa, Trevor Sleight, Jeremy M. Slagley

Faculty Publications

Waste-to-Energy technologies have the potential to dramatically improve both the natural and human environment. One type of waste-to-energy technology that has been successful is gasification. There are numerous types of gasification processes and in order to drive understanding and the optimization of these systems, traditional approaches like computational fluid dynamics software have been utilized to model these systems. The modern advent of machine learning models has allowed for accurate and computationally efficient predictions for gasification systems that are informed by numerous experimental and numerical solutions. Two types of machine learning models that have been widely used to solve for quantitative …

Developing Novel Optimization And Machine Learning Frameworks To Improve And Assess The Safety Of Workplaces, Amin Aghalari

Theses and Dissertations

This study proposes several decision-making tools utilizing optimization and machine learning frameworks to assess and improve the safety of the workplaces. The first chapter of this study presents a novel mathematical model to optimally locate a set of detectors to minimize the expected number of casualties in a given threat area. The problem is formulated as a nonlinear binary integer programming model and then solved as a linearized branch-and-bound algorithm. Several sensitivity analyses illustrate the model's robustness and draw key managerial insights. One of the prevailing threats in the last decades, Active Shooting (AS) violence, poses a serious threat to …

Assessing Readiness For Implementation Of Prognostics And Health Management In Small And Medium Enterprises, Sara C. Fuller

Theses and Dissertations

Prognostics and Health Management (PHM) refers to using robust sensing, monitoring, and control to detect, assess, and track system health degradation and failure modes, allowing for enhanced management and operational decisions. The need for PHM within a manufacturing facility has increased due to a variety of reasons, such as the increasing complexity of manufacturing equipment.

A lack of readiness for digital implementations is linked to failure. The literature highlights certain barriers and enablers that can signal whether a technology implementation will be successful, such as management and maintenance employees’ desire to change the existing process, an understanding and willingness to …

A Biomechanical Approach To Prevent Falls In Ergonomic Settings, Sachini Kodithuwakku Arachchige

Theses and Dissertations

Introduction: Fall-related injuries are exceptionally prevalent in occupational settings. While endangering the workers’ health, falls cause poor productivity and increased economic burden in the workplace. Hence, identifying these threats and training workers to achieve proper postural control is crucial. Purpose: Study 1: To investigate the ankle joint kinematics in unexpected and expected trip responses during single-tasking (ST), dual-tasking (DT), and triple-tasking (TT), before and after a physically fatiguing exercise. Study 2: To investigate the impact of virtual heights, DT, and training on static postural stability and cognitive processing. Methods: Study 1: Twenty collegiate volunteers (10 males and females, one left …

Bayesian Network Development For Depots Location Selection With Biomass Supply System Excellence, Alaa Ashraf Abulhamail

Theses and Dissertations

The renewable energy of the wood pellet market has taken great attention over the last few periods. However, the returns from the pellet business depend largely on how well the quality of biomass. The objective is to economically harvest pellets matching pellet standards set forward by the U.S. markets. The single-mindedness of this study is to develop a Bayesian network model to ensure a high-quality flow through the supply chain of the pallet industry in the top ten counties in Mississippi state. Multiple critical decisions (harvesting, storage, transportation, and quality control) of a biomass-to-pellet supply system could potentially affect the …

Optimizing Strategic Planning With Long-Term Sequential Decision Making Under Uncertainty: A Decomposition Approach, Zeyu Liu

Doctoral Dissertations

The operations research literature has seen decision-making methods at both strategic and operational levels, where high-level strategic plans are first devised, followed by long-term policies that guide future day-to-day operations under uncertainties. Current literature studies such problems on a case-by-case basis, without a unified approach. In this study, we investigate the joint optimization of strategic and operational decisions from a methodological perspective, by proposing a generic two-stage long-term strategic stochastic decision-making (LSSD) framework, in which the first stage models strategic decisions with linear programming (LP), and the second stage models operational decisions with Markov decision processes (MDP). The joint optimization …

Carbon Footprint And Cost Minimization For Grid Systems Through Day-Ahead Order And Battery Size Optimization, Omid Pourkhalili

Doctoral Dissertations

We modeled the problem of peak hours day-ahead order for smart grid companies integrating renewable energy and power storage systems. This results in optimizing day-ahead order, battery storage size, and consequently lowering the use of fossil fuels and emissions. The utility-scale power storage can balance the difference between the day-ahead forecasts and real-time consumer demand through energy arbitrage and transmission deferral for peaking capacity. We define system parameters and their associated costs and run a suggested algorithm to minimize the grid operating cost by optimizing day-ahead order amount and battery storage capacity. The model is designed to prioritize and take …

Efficient Approaches To Steady State Detection In Multivariate Systems, Honglun Xu

Open Access Theses & Dissertations

Steady state detection is critically important in many engineering fields such as fault detection and diagnosis, process monitoring and control. However, most of the existing methods are designed for univariate signals. In this dissertation, we proposed an efficient online steady state detection method for multivariate systems through a sequential Bayesian partitioning approach. The signal is modeled by a Bayesian piecewise constant mean and covariance model, and a recursive updating method is developed to calculate the posterior distributions analytically. The duration of the current segment is utilized to test the steady state. Insightful guidance is provided for hyperparameter selection. The effectiveness …

Deep Learning Applications In Industrial And Systems Engineering, Winthrop Harvey

Graduate Theses and Dissertations

Deep learning - the use of large neural networks to perform machine learning - has transformed the world. As the capabilities of deep models continue to grow, deep learning is becoming an increasingly valuable and practical tool for industrial engineering. With its wide applicability, deep learning can be turned to many industrial engineering tasks, including optimization, heuristic search, and functional approximation. In this dissertation, the major concepts and paradigms of deep learning are reviewed, and three industrial engineering projects applying these methods are described. The first applies a deep convolutional network to the task of absolute aerial geolocalization - the …

Scheduling, Complexity, And Solution Methods For Space Robot On-Orbit Servicing, Susan E. Sorenson

Graduate Theses and Dissertations

This research proposes problems, models, and solutions for the scheduling of space robot on-orbit servicing. We present the Multi-Orbit Routing and Scheduling of Refuellable On-Orbit Servicing Space Robots problem which considers on-orbit servicing across multiple orbits with moving tasks and moving refuelling depots. We formulate a mixed integer linear program model to optimize the routing and scheduling of robot servicers to accomplish on-orbit servicing tasks. We develop and demonstrate flexible algorithms for the creation of the model parameters and associated data sets. Our first algorithm creates the network arcs using orbital mechanics. We have also created a novel way to …

Optimal Global Supply Chain And Warehouse Planning Under Uncertainty, Avnish Kishor Malde

All Dissertations

A manufacturing company's inbound supply chain consists of various processes such as procurement, consolidation, and warehousing. Each of these processes is the focus of a different chapter in this dissertation.

The manufacturer depends on its suppliers to provide the raw materials and parts required to manufacture a finished product. These suppliers can be located locally or overseas with respect to the manufacturer's geographic location. The ordering and transportation lead times are shorter if the supplier is located locally. Just In Time (JIT) or Just In Sequence (JIS) inventory management methods could be practiced by the manufacturer to procure the raw …

Adaptive Design And Flexible Approval Of Clinical Trials, Saeid Delshad Sisi

All Dissertations

Dose-finding clinical trials are among the most critical cornerstones of the healthcare system. In this broad research area, there are many decision making problems that are extremely challenging to address. However, a small improvement may result in significant benefits to the society. Dose-finding clinical trials are extremely expensive and require multiple time-consuming and complicated R&D phases. Despite all the costs and the long time these trials need to conclude (on average over ten years for each new drug/technology), only less than 15\% of these trials successfully end up in a new approved drug entering the market. This problem is even …

Ensemble Tree-Based Machine Learning For Imaging Data, Reza Iranzad

Graduate Theses and Dissertations

In particular medical imaging data, such as positron emission tomography (PET), computed tomography (CT), and fluorescence intravital microscopy (IVM), have become prevalent for use in a wide variety of applications, from diagnostic purposes, tracking diseases' progress, and monitoring the effectiveness of treatments to decision-making processes. The detailed information generated by medical imaging has enabled physicians to provide more comprehensive care. Although numerous machine learning algorithms, especially those used for imaging data, have been developed, dealing with unique structures in imaging data remained a big challenge. In this dissertation, we are proposing novel statistical tree-based methods with more efficient and more …

Park Equity Modeling: A Case Study Of Asheville, North Carolina, Anisa Young

All Theses

Parks and greenspaces are publicly available entities that serve the vital purpose of promoting multiple aspects of human welfare. Unfortunately, the existence of park disparities is commonplace within the park setting. Specifically, marginalized individuals encounter limited park access, insufficient amenity provision, and poor maintenance. To remedy these disparities, we propose a process in which we select candidate park facilities and utilize facility location models to determine the optimal primary parks from both existing and candidate sites.

We note that platforms currently exist to identify the geographical areas where residents lack sufficient access to parks. However, these platforms do not yet …