bluuebunny/crossref_metadata_2025_split huggingface dataset:
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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.