bluuebunny/crossref_metadata_2025_split huggingface dataset:
Implementing the Risk Management Framework for Additive Manufacturing Security: A Model-Based Approach
Authors: Gibbons Duncan W. , Lubell Joshua , Witherell Paul 
Published: November 2024

Abstract
Metal additive manufacturing machines are complex and inherently digital and often cyber-physical systems. As the adoption of this manufacturing technology increases and it becomes increasingly industrialized, concerns about security are evermore prevalent. Both cyber and non-cyber related attacks on critical infrastructure such as additive manufacturing production systems are causes for concern for industry and government. Both the public and private sectors need to focus on securing their information systems to reduce the risk of security attacks and their adverse effects. This research aims to apply the National Institute of Standards and Technology’s Risk Management Framework to the metal additive manufacturing production scenario. The Risk Management Framework defines a rigorous, yet flexible and repeatable, process for managing security risk. A model-based assessment approach is proposed to leverage the digital nature of this manufacturing technology. A case study is performed to demonstrate this approach for a commercial laser powder bed fusion machine in its operating environment. This case study focuses on the technological security risks. This study demonstrates how a model-based approach maximizes the benefits of the Risk Management Framework by improving information and decision traceability for addressing metal additive manufacturing security risks.
Exploring the Effects of a Problem Typology Framework on Student Engagement in a Selection Problem
Authors: Retzlaff Lisa , Ghoreyshi Sama , Olewnik Andrew , Ferguson Scott 
Published: November 2024

Abstract
In most engineering curricula, students are often not given opportunities to solve design problems outside their introductory engineering courses and capstone design. This inhibits them from exercising their ability to navigate ill-structured and complex problem elements inherent to design problems. Problem-based learning (PBL) can help provide opportunities to exercise these abilities by allowing students to gain experience in problem framing and decision-making. However, implementing PBL is often challenging because faculty must navigate problem design, facilitation, and assessment. In this study, we investigate the impact of using an explicit problem typology framework in facilitating student progression in a PBL environment where they engage with a selection problem, a common subproblem in design. One student section was exposed to this problem typology framework (intervention group), while another section was not (control). Using retrospective interview data from students, we analyzed how students talked about their engagement with the selection problem. Results of interview analysis were supplemented by analysis of student assignment submissions. We find that students introduced to the problem typology framework focused less on the reporting of equations and calculations, were more articulate in developing and describing the use of a ranking system in support of selection, and focused on aspects of mathematical and procedural reasoning contributing to the confidence in their final solution. Our interpretation of these findings is that students who used the framework began to view design as sociotechnical, not technorational, in nature. These facets may be missed, even in more open pedagogies like PBL, if facilitation strategies are not carefully considered. We believe these findings lend support for continuing efforts to operationalize Jonassen’s design theory of problem solving to develop PBL environments.
Requirements for Designing a Fail-Safe Defense Supply Network
Authors: Carpenter Samantha L. , Klier John , Allen Janet K. , Mistree Farrokh 
Published: November 2024

Abstract
Defense supply networks are critical for national security. These supply networks are currently designed using the cyber-physical construct to be lean. An unintended consequence of a lean supply network is that it is fragile and not fail-safe. We propose to design the defense supply network as a cyber-physical-social system that will result in a resilient fail-safe supply network that accommodates both variations and disruptions thus allowing us to dynamically manage lead times, address a systemic response for variations and disruptions to reduce or eliminate failure. We recognize the Government, Supplier and User to be the stakeholders. To frame the problem in the context of Dilemmas faced by the three stakeholders we use the Dilemma Triangle Method. The Dilemmas are then analyzed and resolved via the development of a Requirements List within the current defense sustainment “Machine” model outlined in the Art of the Possible (AoP). The application and examination of Dilemmas through the lens of the AoP based constraints management system is further specified to include satisficing resolutions to the identified Dilemmas rooted in a preliminary Requirements List. The Requirements List is foundational for designing a defense supply network that is failsafe. In this paper, we propose a method for identifying the requirements for designing fail-safe supply networks to satisfy multiple stakeholders. We illustrate the efficacy of the approach using the defense supply network as an example. Our focus is on the method rather than the results per se.
Material Selection Using Large Language Models
Authors: Patawari Jain Yash , Grandi Daniele , Groom Allin , Cramer Brandon , McComb Christopher 
Published: November 2024

Abstract
The task of material selection plays a pivotal role in a large number of industries, from manufacturing to construction. Material selection is usually carried out after several cycles of conceptual design, during which designers iteratively refine the design solution and the intended manufacturing approach. In design research, material selection is typically treated as an optimization problem with one correct answer, and restricted to specific types of objects or design functions. In this work, we instead treat materials selection as a problem with multiple answers with varying feasibility. We collect a dataset of experts’ material preferences for nine material categories across 16 diverse design scenarios, and report some trends in the data.
Recent advancements in the natural language processing field around transformer-based architectures, leading to the development of a multitude of pre-trained Large Language Models (LLMs) capable of understanding human language and exhibiting reasoning behaviors, raise some questions about the potential use of this technology for automating the material selection, especially during early the early conceptual design phase. In this work, we identify the degree to which material recommendations provided by an LLM align with those of expert human designers. The results underscore the potential of LLMs to inform material selection.
Multi-Task Learning for Intention and Trajectory Prediction in Human-Robot Collaborative Disassembly Tasks
Authors: Zhang Xinyao , Tian Sibo , Liang Xiao , Zheng Minghui , Behdad Sara 
Published: November 2024

Abstract
Human-robot collaboration (HRC) has become an integral element of many industries, including manufacturing. A fundamental requirement for safe HRC is to understand and predict human intentions and trajectories, especially when humans and robots operate in close proximity. However, predicting both human intention and trajectory components simultaneously remains a research gap. In this paper, we have developed a multi-task learning (MTL) framework designed for HRC, which processes motion data from both human and robot trajectories. The first task predicts human trajectories, focusing on reconstructing the motion sequences. The second task employs supervised learning, specifically a Support Vector Machine (SVM), to predict human intention based on the latent representation. In addition, an unsupervised learning method, Hidden Markov Model (HMM), is utilized for human intention prediction that offers a different approach to decoding the latent features. The proposed framework uses MTL to understand human behavior in complex manufacturing environments. The novelty of the work includes the use of a latent representation to capture temporal dynamics in human motion sequences and a comparative analysis of various encoder architectures. We validate our framework through a case study focused on a HRC disassembly desktop task. The findings confirm the system’s capability to accurately predict both human intentions and trajectories.
Predictive Modeling for Public Policy Design: The Impact of Artificial Lights at Night (ALAN) on Bird Strikes
Authors: Haynes Joey Paul E. , Bhalerao Mayank J. , Honeycutt Wesley T. , Allen Janet K. , Mistree Farrokh 
Published: November 2024

Abstract
Bird strikes present a growing concern to wildlife preservation efforts and pose safety and economic challenges in aviation and urban design that amalgamates a sociotechnical system. Our review of recent evidence suggests that artificial light at night (ALAN) is a contributing factor to bird strikes, especially during migration seasons, when birds are more likely to be disoriented by bright urban lighting.
In this paper, we explore the relationship between light pollution and bird strikes in aviation, with a focus on the application of predictive modeling to inform public policy. Central to our exploration lies is the foundational paradigm of Cyber-Physical-Social Systems (CPSS) that underscores the interaction between the cyber, physical, and social spaces, ensuring effective model-based system design. Multiple criteria are used to perform exploratory data analysis and train a predictive model, including the FAA wildlife strike database, a light pollution GIS map, and BirdCast migration forecasts.
Through exploratory data analysis and predictive modeling techniques, we identify critical correlations between light pollution levels and bird strike incidents. These findings are used to reveal significant trends and offer a model capable of predicting bird strike occurrences with implications for urban planning, lighting design, and aviation safety. We emphasize the potential of these predictive models in informing public policy decisions, aiming to mitigate bird strikes while considering ecological and industrial factors through model-based systems design. Moreover, we acknowledge our method’s limitations and provide recommendations for future research to refine the model’s accuracy and applicability in a policy-making context.
Spatially-Aware Milling Surface Flatness Prediction Through Physics-Based Graph Neural Network
Authors: Zhou Qianyu , Kim Jeongho , Tang Jiong 
Published: November 2024

Abstract
Milling operations are critical in the manufacturing sector, pivotal for achieving desired product specifications and surface quality. However, post-milling deformations, primarily induced by residual stress, can significantly affect the final flatness and surface integrity of workpieces, potentially deviating from manufacturing tolerances and standards. This issue is pronounced in the machining of A2024 aluminum, a material favored for its strength and lightweight properties but susceptible to such stress-related deformations. Finite element Method (FEM) can offer valuable insights but at the cost of extensive computational resources, making them less viable for real-time applications and rapid iteration across large parameter spaces. Addressing this challenge, our study introduces a spatially-aware predictive framework utilizing the physics-based Graph Neural Network (GNN). This framework leverages the spatial relationships between nodes on the milling surface and incorporates physics-guided insights from Jonson-Cook model to forecast post-milling surface flatness with crucial process inputs. A meticulously crafted FEM model drives our automated data generation pipeline, ensuring the creation of high-quality data. The fusion of FEM-derived data with specially tailored GNN modeling represents a paradigm shift in predictive machining results, enabling rapid and reliable flatness prediction to support adaptive manufacturing strategies. This framework is generic, and has the potential of being extended to other manufacturing processes.
Examining the Longitudinal Psychological Safety of Mentored Senior Capstone Design Teams
Authors: Arpini-Lorenzoni Ana , Hart Robert , Polk Todd W. , Murphy Alexander R. 
Published: November 2024

Abstract
The psychological safety of members on a team can impact outcomes during the engineering design process, where psychological safety describes to what extent a team member feels accepted and respected by the team. Engineering capstone courses (or senior design) provide an opportunity for students to interface with industry sponsors in a culminating engineering experience. This study measures team member’s psychological safety at three points during a two-semester senior design sequence (at the start, after the first semester, and after the second semester). Both individual and team psychological safety are considered. In this study, each design team is assigned a project mentor from academic and industry backgrounds to guide them throughout the process. Results show that the psychological safety of individuals does not significantly change over time. This result is consistent when comparing the type of mentor (industry-based or academia-based) assigned to each capstone team. In addition, no significant changes are observed for aggregate team psychological safety during the two-semester capstone design experience. This work contributes to an understanding of the factors that influence team members’ psychological safety during senior design courses to ultimately improve educational outcomes for undergraduate engineering students. In addition, this work provides insight into how engineering educators can promote positive collaboration between undergraduates and industry professionals through team mentorship sourced from both industry and academia.
Design for an Additively Manufactured Hollow Glass Microsphere Lattice Support Structure Using Topology Optimization
Authors: Persia Jude Thaddeus , Lee Soobum , Yun Victor 
Published: November 2024

Abstract
For future military mission using high-quality optics to develop remote sensing instruments, it is critical to design a lightweight substrate structure for reliable support to mirrors. This paper proposes a conceptual design methodology for generating a stable and lightweight supporting structure for a satellite mirror substrate using a 3D topology optimized TPMS lattice structure. This lattice would be constructed using 3D printed glass filled with hollow microspheres for further reduced weight while maintaining high rigidity. An optimal 3D topology is generated in MATLAB for maximum stiffness. Using various boundary conditions, multiple TPMS unit cell patterns are produced for comparison. Each unit cell design is used to construct a full assembly and postprocess is followed to eliminate staircases out of topology optimization and smooth the surface profile. Each of assembly is tested in FEM using ANSYS Workbench for stress analysis from vibration and 3-point bending test. A pareto frontier is produced to compare the designs with respect to their loading responses from two tests. This study identifies Schwarz P and N14 as best designs among others. A more realistic assembly with a larger number of unit cells and the corresponding experimental verification is required for detailed performance evaluation for lightweight mirror substrate application.
Pressure-Correction Based Transient Modeling of Wave Energy Powered Reverse Osmosis (RO) Desalination
Authors: Wu Xian , Li Xiaofan , Zuo Lei 
Published: November 2024

Abstract
Ocean wave-powered reverse osmosis (RO) desalination is an emerging field of study that combines the utilization of ocean energy and RO desalination techniques. However, due to the significant fluctuations in pressure and flow rate within the hydraulic system, an accurate transient model is necessary to estimate its performance accurately and effectively. This paper presents a two-dimensional transient model based on the pressure-correction algorithm to simulate the channel flow with porous walls and time-dependent inlet conditions. The coupled pressure, velocity, and salt concentration problem is solved iteratively by decoupling each term and updating them separately. The model is validated by comparing the results with analytical film theory which estimates the formation of the concentration polarization layer under constant inlet conditions. The performance of the RO systems, especially the concentration polarization phenomenon at the member surface, is investigated using different input conditions, including constant flow condition and sinusoidal flow condition. The salt concentration and permeate flux at the membrane boundary are studied to understand the effect of the dynamic inputs. Results show that the system can reach a higher maximum wall concentration and higher average recovery ratio in sinusoidal signal compared with the constant input. The model’s adaptability to different flow regimes, from steady to sinusoidal, underscores its potential as a valuable tool in optimizing RO desalination powered by ocean wave energy.
Increasing Accessibility of 3D-Printed Customized Prosthetics in Resource-Constrained Communities
Authors: Lee Junghun , Nkama Chukwuemeka , Yusuf Hadiza , Maina Joseph , Ikuzwe Jean , Byiringiro Jean , Busogi Moise , Tucker Conrad 
Published: November 2024

Abstract
Customized prosthetics are often inaccessible in resource-constrained communities. The authors of this work identify two significant barriers. First, obtaining a digital representation of the residual body is inaccessible. The proposed methods overcome this issue by achieving digital representation via smartphones. Second, determining the design and 3D printing process is challenging. Although additive manufacturing technology is widely adopted to fabricate customized prosthetics because of its high accessibility, it has a drawback in that the printing conditions significantly influence mechanical functionality. This issue can be magnified in resource-constrained communities with limited access to high-end 3D-printing technology and filaments. To overcome this issue, we propose a method to predict the mechanical functionality of the prosthetic printed in various printing processes. Finally, a case study demonstrating the implementation of the proposed methods is presented. The case study represents designing customized prosthetic sockets for patients with below-knee amputation on the right leg with a low to mid-end 3D printer. The authors aim to (1) increase accessibility in achieving an accurate digital model of the residual body and (2) reduce the effort of professionals in the 3D printing process, encouraging the implementation of 3D printing, especially in fused filament fabrication/Fused deposition modeling.
Designing Fictional Worlds for Play Through Large Language Models
Authors: Short Ada-Rhodes 
Published: November 2024

Abstract
This paper explores the application of Large Language Models (LLMs) in addressing the challenges of procedural generation in video game design, specifically tackling the Oatmeal Problem and the Bach Faucet Problem. These issues refer to the generation of endlessly similar, unengaging content and the devaluation of content due to its infinite producibility, respectively. By integrating design thinking, computational-cognitive heuristics, and prompt engineering with modern machine intelligence tools, we propose a novel approach termed Procedural Woodworking (PWw). This methodology aims to generate unique worlds and narratives through LLMs while enhancing player engagement by embedding a player’s character into the game’s narrative in a meaningful way. We utilize OpenAI’s ChatGPT-4 to create custom GPTs for world-building and narrative generation, evaluating their novelty and logical consistency through comparative rating scales. Our results indicate that both Top-Down and Bottom-Up world-building methods can produce logically consistent and structurally coherent outputs. The Procedural Woodworking approach, in particular, shows promise in generating novel narrative content that aligns with pre-made human-designed fictional worlds, potentially mitigating the identified procedural generation problems. This study lays the groundwork for further investigation into how LLMs can enrich game design and player experience by offering a more personalized and value-driven narrative generation process.
Fuel Consumption Reductions in Heavy-Duty Vehicles With Crank Pulley Decoupler Mounted Engines
Authors: Pakštys Saulius , Hegde Shailesh , Bonfitto Angelo 
Published: November 2024

Abstract
An increasing amount of effort has been directed to ensure the minimization of carbon dioxide emissions within the transport industry, particularly in the light passenger vehicle segments. This has been achieved through many advancements such as mass and aerodynamic drag reduction as well as the electrification of powertrains. The heavy-duty vehicle segment contributes significantly to the total emissions of the transport sector, however fuel consumption through electrification poses some limitations. Fuel efficiency has been achieved by adopting higher fuel injection pressures generating an increase of the crankshaft nose oscillation making Front-End Accessory Drive (FEAD) design more challenging. This paper provides a solution, with the incorporation of a crank pulley decoupler that isolates the irregularities from the FEAD that arise at the crank of the internal combustion engine. The analysis of a 12.7 L engine tested with a steel pulley and with the decoupler is presented. The brake-specific fuel consumption map is obtained for the engine mounted with the steel pulley and with the decoupler through a dynamometer test. The engine maps are incorporated into a tractor trailer with a 4 × 2 configuration in the Vehicle Energy consumption Tool (VECTO), evaluated over three driving cycles: long-haul delivery, regional delivery, and urban delivery. The outcome indicates a maximum relative fuel saving of 0.63% for a vehicle mounted with a decoupler compared to one mounted with a solid steel pulley.
Data-Driven Modeling Adaptive Aerostructures
Authors: Roetzer James , Boik Hunter , Janke Ben , Li Xingjie , Hall John 
Published: November 2024

Abstract
In the growing domain of wind energy, morphing aerostructures provide an avenue of research to improve aerodynamic performance and turbine control. Branching off previous work by the authors, several data-driven models which relate the actuation of a linearly segmented active morphing blade to an arbitrary performance parameter, aerodynamic thrust load in the case examined, are developed, presented, and compared to evaluate accuracy. Models are developed for the Danish Technical University (DTU) 10 MW and National Renewable Laboratory (NERL) 15 MW reference turbines to ensure generalizability. The models are trained using OpenFAST simulations to generate training and test data. State vector machine (SVM), neural network (NN), decision tree, and gaussian process regression (GPR) models are developed. Comparing root-mean-square error (RMSE), the GPR method produces the least error, in most cases by an order of magnitude or more. The developed model is then used in a demonstrative application, an optimization of the target parameter. Particle swarm optimization is used to find the global minimum input space of the GPR model at discrete wind speeds. The references turbines show a respective maximum improvement in the target performance parameter of 3.15% and 2.66%. Optimization results indicate that refinement of the developed model may improve results.