bluuebunny/crossref_metadata_2025 huggingface dataset:
Adaptive Agent-Based Control for Lithium-Ion Batteries in Naval Microgrids
Authors: Peskar Jarrett , Priddy Braden , Sado Kerry , Downey Austin R. J. , Booth Kristen , Khan Jamil
Published: November 2024

This paper presents an adaptive agent-based control scheme for operating distributed energy storage in naval power systems. The adaptive agent-based control scheme enables batteries distributed throughout an electrical grid to autonomously communicate and align each other to operate in the most effective way possible. To achieve this, each battery is assigned an agent capable of controlling the battery’s output with the goal of maximizing the life of the battery while supplying the required power to the load. Along with the goals of the individual agents, system-level alignment strategies are employed to maximize certain aspects of the battery operations. The employed system-level alignment strategies are duration, ready, and aggressive. The duration strategy maximizes the longevity of grid resources of expected battery degradation and electric generator component wear by utilizing the system in a manner that optimizes system-level longevity. The ready strategy maximizes the amount of power being kept in reserve for use when needed while the Aggressive strategy maximizes power output by allowing the batteries to be used to their limits to deliver as much power as possible. Moreover, an adaptive strategy was also created by mapping the previously mentioned static strategies to the levels of power demand. This adaptive strategy allows the batteries to operate in the most efficient manner while adapting to the power demand from the load. To analyze these strategies, the chosen performance metrics were 1) battery and generator remaining useful life (RUL), 2) average battery temperature, and 3) percent of time the requested load profile was achieved. Results demonstrate that the adaptive strategy operates with better overal efficiency by maintaining the 2nd highest battery RUL, the 3rd highest generator RUL and the 2nd lowest average temperatures. It was also the only strategy to fulfill the maximum pulse seen from the load.
Perception of Automation, AI, and Collaboration in Manufacturing
Authors: Adebayo Oredola , Kumar Pavan , Patel Apurva , Summers Joshua D.
Published: November 2024

This research investigates the perceptions and attitudes towards artificial intelligence (AI) and automation in manufacturing settings. A multimodal data collection approach is used to gather participant responses using surveys, focus group discussions, and interviews. Qualitative and quantitative data collected from six manufacturing companies are analyzed to explore the multifaceted dynamics influencing the adoption and acceptance of AI and automation technologies. The study investigates specific views, concerns, and expectations regarding the implementation of these technologies, shedding light on the nuanced perspectives of participants across different organizational contexts. By examining factors such as personal strategies, sentiment analysis, relevance to roles, and technology acceptance, the research offers insights into the complex interaction between technological advancement, organizational culture, and individual attitudes. Varying levels of understanding regarding technological relevance to roles are observed, alongside concerns about job displacement and decision-making transparency. Participants differ in their belief in AI and automation’s potential to address concerns, highlighting the need for tailored integration strategies. Unique observations, such as machine personalization and cultural influences, are uncovered. Conclusions stress the importance of addressing these complexities in adoption efforts, while future work aims to expand datasets and explore new data collection methods to inform integration strategies. Future work includes analysis of the focus group and interview transcripts for confirmation of these findings.
Refining Engineering Identity Framework for Implementation In Engineering Education – A Literature-Based Analysis
Authors: Kumar Pavan , Summers Joshua D.
Published: November 2024

This paper offers a refined framework for Engineering Identity and its implementation in engineering education. Engineering is portrayed as a multifaceted discipline that leverages scientific, mathematical, and technical knowledge to design, create, and enhance various structures, machines, systems, and devices to address practical challenges and fulfill human needs. It is an action-oriented profession seeking change into a desired future state. The essential skills of critical thinking, problem-solving, creativity, and systematic thinking are those that engineers employ to innovate and implement projects across diverse industries, such as civil, mechanical, electrical, chemical, aerospace, bioengineering and computer engineering. This study emphasizes defining engineering identity within the context of undergraduate engineering education, recognizing this phase as pivotal for the development and evolution of an individual’s identity as an engineer. By scrutinizing definitions of engineering identity from the literature, a refined framework is distilled to support engineering education. The integrated definition for engineering identity is the activity of perception, identification, and corroboration by the student, validated through their interests, opportunities, competence, performance, and recognitions, that is influenced by their prior identity formed by personal, social, educational, and professional experiences. A deeper understanding and cultivation of engineering identity is needed to enrich the educational journey of students and equip them with the necessary skills and mindset for successful careers in the engineering field.
Requirements Mining From Engineering Standards – Development and Evaluation of a Standards Requirements Mining Framework
Authors: Luttmer Janosch , Ehring Dominik , Vinke Kathrin , Alazzazy Mohamed , Nagarajah Arun
Published: November 2024

Standards are an important source of knowledge and influence many decisions along the product development process. However, the capture, reuse, and management of standards content is highly inefficient and defined by a lot of manual steps. This paper presents the development of a holistic framework designed to automize the extraction and processing of requirements from engineering standards — one main element of standards. The plug-and-play framework consists of three core clusters, namely extraction of requirements, classification of requirements, as well as transfer of requirements. Due to the high importance of accurate requirements extraction, this research paper focuses on analyzing and optimizing existing requirements extraction techniques. For this, hyperparameter tuning as well as stacking is used. Using these methods, a neural network is developed with an overall recall of 97%. Moreover, different classification methods are implemented to enhance extracted requirements with additional attributes. The requirements are ultimately transferred and integrated into requirements management tools using the interchange format ReqIF. The framework’s functionalities are validated using three exemplary engineering standards. Future work will focus on the further optimization of extraction solutions with emphasis on user interaction. Moreover, user studies will be conducted to measure the efficiency and effectiveness of the proposed framework.
Exploring the Impact on Neurocognitive Process and Motivation Caused by Problem Modality
Authors: Kado Corey , Kames Elisabeth
Published: November 2024

Design based curriculum has been identified as a vital component for engineering education. In the decade since ASME Vision 2030 Project (V2030) strides have been achieved in the implementation of these guidelines into post-secondary institutions. Such is seen in the criteria for accreditation by ABET, which includes an explicit requirement for design to be present in engineering programs. This study is a continuation of previous work examining the effect of design problem modality. Design problem modality describes the method in which design problems are presented to the students. For this study, design problems were presented to the students through either an auditory description or a kinesthetic (hands-on) example. Participants are gathered from an engineering design cornerstone course at Florida Polytechnic University. The tool utilized to examine neural oscillations in this study is the electroencephalograph (EEG). To further examine the effects of these modalities, a modified version of the Motivated Strategies for Learning Questionnaire (MSLQ) and the learning style questionnaire VARK are utilized to extend the comprehension of the impact. Including demographic data of the participants, an encompassing understanding of problem modality on student’s can be achieved.

The results showed differences in neural oscillations in the beta high frequency range with differing design problem modality, however the order in which the design problems were presented showed no such correlation. Further examination found a correlation between the perceptions of a design problem, such as difficulty, and the design problem modality, as well as a participant’s satisfaction with the design problem order.
Dehomogenization for Multiscale Topology Optimization via Triangular Lattice Structures
Authors: Najmon Joel C. , Chaudhari Prathamesh , Tovar Andres
Published: November 2024

Dehomogenization is a post-processing step for generating physically realizable structures from homogenized designs. Typically employed in conjunction with homogenization-based topology optimization (HBTO), dehomogenization is used to derive a binary representation of the initially homogenized (gray) design. Currently, most dehomogenization approaches rely on an implicit representation of microscale topologies using periodic functions, which limits their applicability to specific parameterized microstructures, such as rectangular voids. This work introduces a novel dehomogenization approach that allows the projection of a homogenized mechanical property field onto a discrete lattice structure of arbitrary shape. This results in a well-connected, functionally graded lattice structure featuring control over the length scale, orientation, and complexity of the final design. The proposed approach incorporates an optimization algorithm to find the lattice thickness distribution that minimizes the difference between a local target homogenized property and a corresponding lattice homogenized stiffness tensor. The definition of a signed distance function between connected nodes yields tapered beam designs with smoothly varying thicknesses. The effectiveness of the approach is demonstrated on a half MBB beam, a clamped beam, and an L-bracket. The compatibility of the resulting non-periodic structures indicates that the performance of the dehomogenized design is maintained or improved compared to the homogenized design.
Cooling-Guided Diffusion Model for Battery Cell Arrangement
Authors: Sung Nicholas , Liu Zheng , Wang Pingfeng , Ahmed Faez
Published: November 2024

Our study introduces a Generative AI method that employs a cooling-guided diffusion model to optimize the layout of battery cells, a crucial step for enhancing the cooling performance and efficiency of battery thermal management systems. Traditional design processes, which rely heavily on iterative optimization and extensive guesswork, are notoriously slow and inefficient, often leading to suboptimal solutions. In contrast, our innovative method uses a parametric denoising diffusion probabilistic model (DDPM) with classifier and cooling guidance to generate optimized cell layouts with enhanced cooling paths, significantly lowering the maximum temperature of the cells. By incorporating position-based classifier guidance, we ensure the feasibility of generated layouts. Meanwhile, cooling guidance directly optimizes cooling-efficiency, making our approach uniquely effective. When compared to two advanced models, the Tabular Denoising Diffusion Probabilistic Model (TabDDPM) and the Conditional Tabular GAN (CTGAN), our cooling-guided diffusion model notably outperforms both. It is five times more effective than TabDDPM and sixty-six times better than CTGAN across key metrics such as feasibility, diversity, and cooling efficiency. This research marks a significant leap forward in the field, aiming to optimize battery cell layouts for superior cooling efficiency, thus setting the stage for the development of more effective and dependable battery thermal management systems.
Distributed Multi-Agent Bayesian Optimization for Unknown Design Space Exploration
Authors: Chen Siyu , Bayrak Alparslan Emrah , Sha Zhenghui
Published: November 2024

In multi-agent Bayesian optimization for Design Space Exploration (DSE), identifying a communication network among agents to share useful design information for enhanced cooperation and performance, considering the trade-off between connectivity and cost, poses significant challenges. To address this challenge, we develop a distributed multi-agent Bayesian optimization (DMABO) framework and study how communication network structures/connectivity and the resulting cost would impact the performance of a team of agents when finding the global optimum. Specifically, we utilize Lloyd’s algorithm to partition the design space to assign distinct regions to individual agents for exploration in the distributed multi-agent system (MAS). Based on this partitioning, we generate communication networks among agents using two models: 1) a range-limited model of communication constrained by neighborhood information; and 2) a range-free model without neighborhood constraints. We introduce network density as a metric to quantify communication costs. Then, we generate communication networks by gradually increasing the network density to assess the impact of communication costs on the performance of MAS in DSE. The experimental results show that the communication network based on the range-limited model can significantly improve performance without incurring high communication costs. This indicates that increasing the density of a communication network does not necessarily improve MAS performance in DSE. Furthermore, the results indicate that communication is only beneficial for team performance if it occurs between specific agents whose search regions are critically relevant to the location of the global optimum. The proposed DMABO framework and the insights obtained can help identify the best trade-off between communication structure and cost for MAS in unknown design space exploration.
Advancing Ground Vehicle Dynamics: Dynamic Morphing Technology Integration and Analysis for the Autonomous Utility Truck in High-Crosswind Conditions
Authors: Patel Parth Y. , Koomullil Roy , Zhang Siyuan , Moradi Lee , Vantsevich Vladimir
Published: November 2024

Initially pioneered in aircraft design to optimize maneuverability and aerodynamic efficiency, dynamic morphing technology is now revolutionized in ground vehicles to address the stability and maneuverability challenges. Inspired by the success of morphing solutions in aviation and a few other ground vehicles, this paper dynamically analyzes a novel approach that the authors proposed previously in their published work to advance ground vehicle dynamics by integrating dynamic morphing technology into utility trucks equipped with robotic manipulator boom equipment and to manage their traction capability and the stability during the motion under high-crosswind conditions. This paper aims to optimize the tire-road/terrain contact by dynamically adjusting the truck’s shape through coordinated movements of the boom equipment and mitigate the adverse effects of crosswinds on vehicle dynamics. Using advanced modeling techniques and simulation studies, we analyzed the dynamic morphing process and its impact on the truck’s traction performance in high-crosswind conditions. Utility trucks can achieve traction and stability by optimizing the distribution of the truck’s weight and aerodynamic forces and moments, thus improving safety and performance in challenging environments.
Advanced Tire Testing by an Innovative Moving Laboratory for Automotive Components Safety Assessment (MoLAS)
Authors: Ballo Federico Maria , Biffi Alessio , Gobbi Massimiliano , Mastinu Giampiero
Published: November 2024

The paper presents an innovative moving laboratory (MoLAS) for tire testing. The MoLAS system is mounted on a semitrailer to be pulled for outdoor tire characterization under realistic grip condition. The vertical load is controlled by means of a pneumatic spring actuator mounted on top of a moving platform guided by linear ball sliders. A double-stage suspension system allows to filter out the vertical disturbance induced by road irregularity. Two electric actuators are used to precisely control steering and camber angles. Tire forces and moments are measured by an innovative six-axis force sensor specifically designed for the purpose. The main innovation introduced by the MoLAS with respect to the state-of-art test systems relies in the possibility to test the tire both in traction and braking longitudinal slip conditions. This is achieved by an internal combustion engine and a retarder electromagnetic brake mounted on the driveline. A complete tire characterization has been performed by placing the MoLAS system on the RuotaVia drum of the LaST laboratory at Politecnico di Milano. The main results are presented in the paper.
Physics-Informed Data-Driven Approaches to Electric Vehicle Battery State-of-Health Prediction: Comparison of Parallel and Series Configurations
Authors: Zhao Yixin , Haapala Karl R. , Natarajan Arun , Behdad Sara
Published: November 2024

Battery lifetime and reliability depend on accurate state-of-health (SOH) estimation, while complex degradation mechanisms and varying operating conditions strengthen this challenge. This study presents two physics-informed neural network (PINN) configurations, PINN-Parallel, and PINN-Series, designed to improve SOH prediction by combining an equivalent circuit model (ECM) with a long short-term memory (LSTM) network. PINN-Parallel process input data through parallel ECM and LSTM modules and combine their outputs for SOH estimation. On the other hand, the PINN-Series uses a sequential approach that feeds ECM-derived parameters into the LSTM network to supplement temporal data analysis with physics information. Both models utilize easily accessible voltage, current, and temperature data that match realistic battery monitoring constraints. Experimental evaluations show that PINN-Series outperforms the PINN-Parallel and the baseline LSTM model in accuracy and robustness. It also adapts well to different input conditions. This demonstrates that the simulated battery dynamic states from ECM increase the LSTM’s ability to capture degradation patterns and improve the model’s ability to explain complex battery behavior. However, the trade-off between the robustness and training efficiency of PINNs is also discussed. The research findings show the potential of PINN models (particularly the PINN-Series) in advancing battery management systems, but the required computational resources need to be considered.
Multi-Split Configuration Design for Fluid-Based Thermal Management Systems
Authors: Bayat Saeid , Shahmansouri Nastaran , Peddada Satya RT , Tessier Alex , Butscher Adrian , Allison James T.
Published: November 2024

High power density systems require efficient cooling to maintain their thermal performance. Despite this, as systems get larger and more complex, human expertise and insight may not suffice to determine the desired thermal management system designs. To this end, a framework for automatic architecture exploration is presented in this article for a class of single-phase, multi-split cooling systems. For this class of systems, heat generation devices are clustered based on their spatial information, and flow splits are added only when required and at the location of heat devices. To generate different architectures, candidate architectures are represented as graphs. From these graphs, dynamic physics models are created automatically using a graph-based thermal modeling framework. Then, an optimal fluid flow distribution problem is solved by addressing temperature constraints in the presence of exogenous heat loads to achieve optimal performance. The focus in this work is on the design of general multi-split heat management systems. The methods presented here can be used for diverse applications in the domain of configuration design. The multi-split algorithm can produce configurations where splitting can occur at any of the vertices. The results presented include three categories of problems and are discussed in detail.
Economic Impact Considerations for Sustainable Design Engineering as Derived From the Literature
Authors: Kelley K. Joshua , Mattson Christopher A. , Hatch Nile W. , Salmon John L.
Published: November 2024

Sustainable development is frequently discussed in terms of social, environmental, and economic impacts, often referred to as the three pillars of sustainability. Understanding each of these pillars independently, as well as their interdependencies and trade-offs, is essential for effective sustainable development. Thus, as one step in the direction of increased sustainability, this paper explores the economic impact pillar independent from the other two. Specifically, this paper contributes to the archival scholarship on sustainable development by presenting eleven types of economic impacts relevant to sustainable design engineering. These eleven economic impact categories were derived from the literature via a semi-automated review of the observed economic impacts of engineering design decisions and engineered products. This categorization forms a baseline for future research and for the development of economic sustainability models for design engineers. In its current form, this taxonomy may also assist design engineers as they consider the economic impacts of their work, facilitating an increase in both the variety and quantity of impacts considered. Furthermore, we recommend considering these economic impact categories together with the set of social impact categories presented by Rainock, et al. [1]. Considering both sets together can increase one’s understanding of the interplay — including trade-offs — between these two pillars of sustainable development.
A Multi-Fidelity Approach to Testing and Evaluation of AI-Enabled Systems
Authors: Seif Robert J. , Yang Zichong , Wang Ziran , Freeman Laura , Panchal Jitesh H.
Published: November 2024

As Artificial Intelligence (AI) and Machine Learning (ML) technologies advance, the need for methods to test them also increases. Several methods for testing ML and AI systems have recently been developed by the ML/AI and software development communities. These methods assume that the team that is developing the system is also responsible for testing the system, therefore, they have access to the datasets on which the ML models were trained, and the knowledge of the environment in which the system is expected to operate. However, this is not true in certain situations such as in the Department of Defense (DoD) acquisition where the systems are developed by other organizations, due to which, existing methods for Test and Evaluation (T&E) of AI-enabled systems are not adequate for DoD acquisition. To address this gap we propose a multi-fidelity approach for testing and evaluation that consists of (i) a representation of the model space with dimensions along which different fidelities of models can be developed, and (ii) a method to integrate multiple fidelities for continuous T&E of AI-enabled systems. The approach is illustrated using an example of a visual perception system in an autonomous vehicle (AV) use case, where a simulation space across different fidelities is constructed to test how well the system meets the listed requirements. A model space is first identified, in which models are characterized for their cost and performance. A method to generate test plans is then devised to maximize the utility across the span of given system requirements. We illustrate how the proposed approach can be used to develop test combinations that minimize the cost and maximize the utility under a set of system requirements to be tested.
Integrating Machine Learning Into the Design of Green Building Systems
Authors: Payne Emily , Layton Astrid
Published: November 2024

Sustainable infrastructure design is a complicated process often requiring detailed estimates specifications and constraints of the project scope to be compiled. Beyond the time-consuming gathering of project data sometimes the availability of completed projects is limited. Therefore, a method to produce similar designs with varied constraints requires a systems engineering perspective. Systems engineering provides a method to evaluate multidisciplinary design development while simultaneously following stakeholder requirements. Ecologically inspired systems have shown the ability to maintain balanced resources and structural relationships even under duress. Driven by the imperative to build sustainable infrastructure, this research explores the utilization of machine learning techniques to generate robust and reliable forecasts of green building specifications, even when design resources are scarce. To demonstrate the effectiveness of this approach, machine learning techniques were performed on a dataset of 93 green educational buildings, and on an oversampled dataset containing synthetically generated data points at the aim of certification level prediction. Both datasets contained metrics quantitatively characterizing cost, energy efficiency, and ecologically sustainable metrics specific to each building. Results indicate that the oversampled dataset allowed for better machine learning prediction among the classification algorithms considered. Oversampled data provided quality information offering cost minimization during initial design stages. This data suggests that oversampling is a reliable technique to amplify the design area of infrastructure projects when applied on data containing strong systemic classification patterns.
SafeZone: A Topologically-Aware Voronoi-Based Framework for Fast Collision-Free Cooperative 3d Printing
Authors: Stone Ronnie F. P. , Ebert Matthew , Zhou Wenchao , Akleman Ergun , Krishnamurthy Vinayak , Sha Zhenghui
Published: November 2024

Swarm manufacturing (SM) is an emerging manufacturing paradigm that employs a heterogeneous swarm of robots to accomplish complex hybrid manufacturing tasks. Cooperative 3D Printing (C3DP), a special form of swarm manufacturing, uses multiple printers to print large-scale parts cooperatively and aims to tackle key challenges in the additive manufacturing industry, such as trade-offs among size, speed, quality, and cost. A fundamental challenge in C3DP is how to achieve collision-free, time-efficient printing when multiple printers operate in a shared workspace. This is a complex problem since the solution may depend on a myriad of factors, such as the number of printers, part geometry, printer positioning, mobility, and kinematics, or whether the printing path pre-determined. In this paper, we present SafeZone, a collision-free and scalable C3DP framework that aims to minimize printing time by considering both the geometry and topology (space-connectivity) of the resulting workspace when segmenting the part layer. To achieve this, we use a guided Voronoi tessellation that can only produce degree-3 partitions, which we show to have optimal scheduling properties based on the chromatic number of the resulting partition graph. The sites of the Voronoi tessellation are constrained to only lie on the boundary of their convex hull, thus facilitating collision-free operation in C3DP systems with robotic arms. We demonstrate through physical testing in a 4-printer scenario with SCARA arms that SafeZone can produce collision-free prints, resulting in a printing time reduction of 44.63% when compared to the single-printer scenario. Finally, we show how the partition created by our methodology has a printing time reduction of 22.83% when compared to a naive choice which does not consider workspace topology.
Effects of Intentional Group Framing on Tradespace Exploration
Authors: Sutton Meredith , Turner Cameron , Mocko Gregory
Published: November 2024

Decision making in engineering design occurs throughout the design process. In early stages, design space exploration reveals potential solutions to designers, while tradespace exploration helps designers choose between solutions based on tradeoffs in utility or performance. The outcomes of these decisions can be influenced by the selective framing of information in the decision scenario. To understand how much influence framing has on engineering decision-making in these scenarios, a study was conducted using a simulated vehicle design tradespace. A team of designers was tasked with exploring possible designs and understanding the tradeoffs in utility between their design options in three differently-framed design scenarios. Explicitly framing the design problem as a group activity was found to reduce the time required to reach a decision without compromising on the utility achievement of the found solution. It also removed the need for initial sensemaking discussions before decisions could be made.
Graded Minimal Surface Periodic Structures for Multiphysics Metamaterial Applications
Authors: Apetre Nicole A. , Michopoulos John G. , Iliopoulos Athanasios P. , Steuben John C. , Rodriguez Steven N. , Graber Benjamin D. , Kittur Madan
Published: November 2024

The continuous progress of additive manufacturing techniques has enabled engineers and designers to build complex geometries at various length scales with minimal setup time, reduced need for skilled labor, and minimal material waste. One important subset of structures endowed with complex geometrical features is formed by periodic and non-periodic metamaterials that enable engineering applications where certain combinations of performance features are desirable. For example, these structures could be used in naval engineering applications where light-weight, large surface area, energy absorption, heat dissipation, and acoustic bandgaps are critical. Nevertheless, to deploy these complex geometry structures, their multiphysics response must be well understood and characterized. The current effort aims to describe an initial approach for designing and deploying graded triply periodic minimal surface (TPMS) architectures. The work focuses on three TPMS systems: (a) Neovius, (b) Schoen’s gyroid, and (c) Schoen’s F-RD made of Ti-6Al-4V alloy. These three geometries are enhanced using variable wall thickness to obtain the so-called graded TPMS. Three problems are explored: (1) evaluation of the elastic equivalent material properties of various unit cells with graded thickness; (2) investigation of acoustics dispersion properties of three cells and (3) a thermo-structural response of a complex geometry made of twelve graded representative volume elements (RVE). Finite element results of the relevant thermo-structural partial differential equations for the case of a gyroid triply periodic cylindrical sandwich structure are presented.
Investigation of Different Snow Material Models and Simulation Techniques for Enhanced Snow-Tire Interaction Study
Authors: Surkutwar Yogesh , Sandu Corina , Untaroiu Costin
Published: November 2024

In regions that experience significant snowfall, the interaction between tires and snow-covered surfaces is critical for vehicle dynamics, safety, and performance. Advanced material models of snow with reliable parameters are required for simulation of this complex interaction.

A comprehensive study on the material characterization of compacted snow and parameter identification using an optimization-based approach are presented in this study. Then, the calibrated snow models are applied in various computational models for simulating rubber block-snow interactions. The characterization process involves Direct Shear Tests (DST) and Confined Compression Tests (CCT) to determine important snow properties. The model parameters are optimized using Multi-Objective Design of Experiments (MODoE) algorithm and experimental data. The study used two advanced computational techniques: Arbitrary Lagrangian-Eulerian (ALE) and Smoothed Particle Hydrodynamics (SPH), evaluating their efficacy in simulating traction in snow-rubber interaction conditions. The work enhances understanding of the complex mechanical behavior of compressed snow that could be used to improve tire design for snow traction.
Building Spaces for Design Justice: A Case Study on the Design Justice Pedagogy Summit
Authors: Roeder Gillian , Das Madhurima , Saadi Jana I. , Harrington Christina , Verma Aditi , Breazeal Cynthia , D’Ignazio Catherine , Yang Maria , Ostrowski Anastasia K.
Published: November 2024

As we promote more engagement with equity, ethics, and justice in design spaces, we must support designers, researchers, and instructors in successfully embedding these considerations in design education. In August 2022, the Design Justice Pedagogy Summit provided a space for instructors to build community, reimagine their courses and/or activities, and envision the future of design education through design justice. This paper explores participants’ experience and the impact of the summit, directly around the summit and four months after the summit through surveys, interviews, and design artifacts. Impact stories told by participants about their experiences and methods to incorporate design justice into their work were also analyzed. Participants gained greater confidence in embedding design justice into their work and/or teaching over the course of the summit and planned to further engage with it. They also valued the community and collaborations that were built at the summit. Moving forward, participants planned to further strengthen their engagement with design justice, share knowledge and continue the conversation around design justice, and build community. We synthesize these learnings to outline two areas of support that the summit provided, including mechanisms for embedding design justice and guidelines for building community spaces for design justice.
Automated Mechanical Multiplexing Manifold: Enabling Precision Aquaculture in Emerging Markets
Authors: Briggi Conor , Costello Jeffrey , Duncan Anna , Herrero-Marques Penelope , Sepulveda Francisco , Liang ZhiYi , Winter Amos G.
Published: November 2024

Aquaculture is a steadily growing segment of food production worldwide, but farmers in both established and emerging markets are facing ever-greater challenges of sustainable intensification. These challenges include, but are not limited to, the implementation of ethical farming practices, minimization of recurring costs, and minimization of environmental impacts. Implementation of precision aquaculture, which often includes automation and monitoring, is one method for achieving sustainable intensification. However, the cost of precision aquaculture is prohibitively high for many farmers; a significant barrier to adoption. This cost barrier is a critical issue in developing markets where the aquaculture industry is expanding rapidly.

This article proposes a scheme for the automation of one aspect of fish food distribution, the selection and routing of fish food to each of the fish pens. Using principles of mechanical multiplexing, a full-scale prototype of an automated pen selector manifold was designed and constructed. The manifold selector system featured a modular design to make adding feed pens easier for developing markets, by making expansion costs more accessible. Functional tests demonstrated equivalent or better performance and a cost reduction of 78% compared to existing market solutions. Additional steps are outlined to fully productize and implement the proposed solution in emerging markets and, through reverse innovation, in existing world markets.
Research2Market Connect: Cloud-Based Platform to Connect Academic Research With SMEs for Accelerated Innovations
Authors: Vaishnav Shubham , Agarwal Ankit , Sorathiya Pratik C. , Desai K. A. , Mears Laine
Published: November 2024

Small and Medium-sized Enterprises (SMEs) can benefit significantly by exploiting and successfully commercializing technological innovations in academic institutions. The primary challenge in realizing this requirement is the non-availability of systematic repositories and lower Technology Readiness Levels (TRL) with systems or products available at academic institutions. This paper presents Research2Market Connect to aggregate innovations within academic institutions and a listing of the repository on a cloud-based platform for offering shared access to SMEs. As academic research may not be readily suited to market needs, the platform facilitates enhancing the TRL level (from TRL 4 to TRL 7) through online collaborations and resource provisioning using the service-oriented model. The platform elements — an academic profile, an industry profile, and a mechanism for improving the TRL level using the Design-as-a-Service (DaaS) framework, are conceived and discussed in the paper. An illustrative case study of the rapid development and deployment of a face shield during the COVID-19 pandemic is presented to showcase the importance of collaborative innovation using a cloud-based platform. It has been shown that cloud-based platforms similar to Research2Market Connect can enable SMEs to leverage research in academic institutions with human expertise and computational resources to overcome resource barriers and swiftly respond to changing market conditions.
A Geometrico-Statistical Representation for Off-Road Environments Captured by LiDAR
Authors: Khan Taimoor Daud , Vyas Shantanu , Krishnamurthy Vinayak R.
Published: November 2024

In this paper, we present a hierarchical representation of LiDAR point cloud data in off-road environments with the aim of generating 3D scene abstractions for autonomous vehicles. Different objects in the environment are better represented at different levels of detail based on the type of object, the application context and the information available about the object. Despite this, prior works on point cloud scene processing focus on detailed scene rendering and scene reconstruction which are computationally expensive and often require prior training. In addition, these works are centered on structured on-road environments. Unlike on-road environments, off-road scenes consist of highly unstructured and irregularly shaped objects such as rough terrains, trees, and shrubs. As a result, most algorithms for segmenting, reconstructing, and consequently navigating such environments are inherently challenging. Furthermore, the transmission of off road scenes over networks to other vehicles operating in coordination or to remote sites is expensive when working with high fidelity scene representations. In contrast we present a geometric-statistical algorithm that is computationally inexpensive and provides a multi-level of detail scene abstraction for robot decision making and task execution. The low footprint of the abstraction also allows for lossless scene transmission over the network. We present qualitative results for scene abstractions for various outdoor scenes in the RELLIS-3D dataset and also present quantitative assessment of the abstractions compared to segmented objects in the scenes.
Prospective Life Cycle Assessment of Nonwoven Mat Manufacturing: Comparing Pre-Consumer Wood Waste Fibers to Post-Consumer Recycled Apparel Fibers
Authors: Durrani Hira , Clarke-Sather Abigail R. , Alves Paulo H. T. F.
Published: November 2024

The apparel industry’s substantial contribution to increasing Municipal Solid Waste (MSW) from discarded apparel through fast fashion approaches necessitates exploring sustainable alternatives. Over the past fifty years, throwing away textiles has risen four times as fast as MSW generation rates in the U.S. This study examines the environmental impact of an existing nonwoven mat manufacturer that uses pre-consumer wood waste to make fiber for nonwoven mats with a post-consumer recycled apparel alternative. This alternative, the Fiber Shredder, a lab-scale innovative mechanical textile recycling technology, takes discarded apparel and turns it into valuable fiber. The recycled cotton fiber produced by the Fiber Shredder from discarded apparel is long enough to manufacture new nonwoven textiles. The final products, nonwoven mats, are intended for landscaping purposes and thus use biodegradable fibers. The novel mechanical textile recycling technology to create fibers from discarded apparel, the Fiber Shredder, developed at the University of Minnesota Duluth, is scaled up theoretically to compare its potential impact upon commercialization to an existing industrial process that creates wood fibers from waste wood. A prospective Life Cycle Assessment (LCA) compared the environmental impacts of three options for manufacturing nonwoven mats; (A) pre-consumer waste wood fibers, (B) current lab scale post-consumer recycled cotton fibers from the Fiber Shredder and (C) post-consumer recycled cotton fibers from the scaled up Fiber Shredder. This LCA encompasses emissions, energy usage, and materials from the acquisition of raw materials through the production processes or cradle-to-gate. The results show that although the lab-scale Fiber Shredder has higher environmental impacts than the commercial nonwoven manufacturer, scaling up the Fiber Shredder will lead to reduced impacts. However, the scaled-up Fiber Shredder still needs improvement in electricity consumption and speed of production to be able to match the current commercially available technology. The limitations of this study involve simplifying assumptions such as neglecting packaging during shipping, energy consumption for apparel sorting and notion removal, and potential microplastics generation. In conclusion, transitioning to post-consumer apparel recycling holds promise but requires careful consideration of energy usage for scalability of the Fiber Shredder. This study underscores the importance of prospective LCA to optimize the design of emerging recycling technologies for sustainable production.
Current State and Benchmarking of Generative Artificial Intelligence for Additive Manufacturing
Authors: Surovi Nowrin Akter , Witherell Paul , Mathew Vinay Saji , Kumara Soundar
Published: November 2024

Additive Manufacturing (AM) is gaining popularity in the industry for its cost-effectiveness and time-saving benefits. However, AM encounters challenges that need to be addressed to enhance its efficiency. While Machine Learning (ML) can tackle various AM challenges, it is often limited to specific issues, necessitating multiple models. In contrast, Generative Artificial Intelligence (GenAI) has the potential to mitigate instance-specific bias due to its broader training. This paper presents a comprehensive methodology for evaluating the capabilities of various existing GenAI tools in addressing diverse AM-related tasks. We propose three categories of metrics, totaling 35 metrics, namely agnostic, domain task, and problem task metrics. Additionally, we introduce a scoring matrix, a practical tool that can be used to assess the responses of different GenAI tools. The study involves data collection from diverse published papers, which are used to create inquiries for GenAI tools. The results demonstrate that transformer-based models, such as multi-modal GPT-4 and Gemini (prev. BARD), can handle both AM image and text data. In contrast, uni-modals such as GPT-3 and Llama 2 are proficient in processing AM text data. Furthermore, image-based models such as DALL·E 3 and Stable Diffusion can accept AM text data and generate images. It is also observed that the performance of these models varies across different AM-related tasks. The variation in their performance may be due to their underlying architecture and the training dataset.
Comparative Analysis of Non-Pneumatic Tire Spoke Designs for On-Road Applications: A Traction Force Perspective
Authors: Sidhu Charanpreet , El-Sayegh Zeinab
Published: November 2024

This study explores the traction performance and structural integrity of Non-pneumatic (NP) tires, which are being explored as alternatives to traditional air-filled tires, offering enhanced durability, reduced maintenance, and improved eco-friendliness. This study investigates the influence of four spoke shapes (honeycomb, modified honeycomb, re-entrant honeycomb, and straight spokes) on NP tire traction performance for on-road applications. Using Finite Element Analysis (FEA), the study considers the influence of longitudinal speed and vertical load on the traction performance and stress distribution of the NP tire designs. This assessment of traction coefficient under the constant torque will reveal distinct variations in performance across the four-spoke designs. By systematically varying these parameters, the study aims to provide a detailed understanding of how different design factors influence the overall performance of NP tires. Furthermore, the traction coefficient measurements helped to identify spoke configurations that optimize tire-road interaction performance. Comprehensive comparison and evaluation of the four NP tire spoke designs highlight their respective advantages and limitations for on-road applications. By optimizing spoke shapes and considering the effects of speed and load, manufacturers can develop NP tire designs that offer improved traction, durability, and efficiency for a wide range of automotive applications. These findings provide valuable insights for optimizing NP tire designs, aiding decision-making for manufacturers in the automotive industry.
Towards Novel Standard-Based Approach to Flexible Supply Chain Integration
Authors: Jelisic Elena , Kulvatunyou Boonserm , Ivezic Nenad , Oh Hakju
Published: November 2024

Global supply chain digitalization must support diverse and incompatible formats, protocols, and structures of the Data Exchange Standards (DESes). The complexity of the global supply chain network has led to the need for increased supply chain flexibility, agility, and resilience. This paper reviews different approaches to resolving the DES heterogeneity between business systems and their trade-offs in supporting flexible supply chain integration. A novel approach is then proposed to better manage and reuse mapping specifications and enable more efficient translation of messages from one DES to another and, consequently, more flexibility in supply chain networking. The approach represents an extension of the existing platform for DES management. It is based on previously documented federated DES development and management, which adopts the federated learning approach for building the DES library. Central to the proposed approach is the concept of a super data model as an “interoperable” layer that enables semantics match between different DESes. Another important concept is Business Context, which is envisioned as a basis for the identification and reuse of existing mapping specifications. Our initial evaluation shows the feasibility and benefit of the proposed solution. The paper identifies a list of research directions that will be addressed in the future.