bluuebunny/crossref_metadata_2025 huggingface dataset:
State of Health Estimation of Electric Vehicle Batteries Using Transformer-Based Neural Network
Authors: Zhao Yixin , Behdad Sara
Published: November 2023

Electric vehicles (EVs) are considered an environmentally friendly option to conventional vehicles. As the most critical module in EVs, batteries are complex electrochemical components with nonlinear behavior. On-board battery system performance is also affected by complicated operating environments. Real-time EV battery in-service status prediction is tricky but vital to enable fault diagnosis and aid in the prevention of dangerous occurrences. Data-driven models with advantages in time series analysis can be used to capture the degradation pattern from data about certain performance indicators and predict the battery states. The Transformer model is capable of capturing long-range dependencies efficiently using a multi-head attention block mechanism. This paper presents the implementation of a standard Transformer and an encoder-only Transformer neural network to predict EV battery state of health (SOH). Based on the analysis of the lithium-ion battery from NASA Prognostics Center of Excellence website’s publicly accessible dataset, 28 features related to the charge and discharge measurement data are extracted. The features are screened using Pearson correlation coefficients. The results show that the filtered features can effectively improve the accuracy of the model as well as the computational efficiency. The proposed standard Transformer shows good performance in SOH prediction.
A Comparison of CNN Models for Automated Femur Segmentation Based on DICOM Images
Authors: Ghidotti Anna , Vitali Andrea , Regazzoni Daniele , Weiss Cohen Miri , Rizzi Caterina
Published: November 2023

Segmentation of anatomical components is a critical step in creating accurate and realistic 3D models of the human body, which are employed in a wide range of clinical applications, particularly in orthopedics. Recently, many deep learning approaches have been proposed to solve the problem of manual segmentation. Among the available software for automatic segmentation, MONAI Label is a free open-source tool, which allows for the creation of annotated datasets and the development of AI-based annotation models for clinical assessment. In this context, the present study is designed to compare the performance of two well-known neural networks in segmenting knee bones. In spite of the fact that several studies have investigated the use of deep learning techniques for knee reconstruction, there is no consensus regarding the most effective method. In the present study, validation metrics are selected in order to assess the accuracy of the automated segmentation models in comparison with the ground truth data. Magnetic resonance images of 31 patients have been employed for the study. As result, U-Net shows better performance than the SegResNet in the automatic femur segmentation task.
A Flex-Fuel Sensor and Control Strategy for Variable Concentrations of Hydrogen-Natural Gas Blend Fuel in an Internal Combustion Engine
Authors: Dogal Michael , Anstrom Joel , Iyer Suresh S.
Published: November 2023

This paper proposes an ultrasonic sensor methodology to measure concentrations in a binary gaseous alternative fuel system of hydrogen and natural gas for automotive applications. Further, it proposes a sensing and control architecture that enables alternative fuel vehicles, in this case a series hybrid electric vehicle, to fuel and operate their conventional IC engines on variable concentrations of hydrogen and compressed natural gas (HCNG). This represents a new type of flex fueling capability namely Flex-HCNG. The proposed Flex-HCNG system includes the ability to refuel from any hydrogen or natural gas fueling station into a single high-pressure on-board storage tank and then to start and run under changing fuel concentrations. The benefits of Flex-HCNG will be greater demand for both natural gas and hydrogen by effectively increasing available fueling infrastructure and providing security through greater fuel choice. Increasing demand for hydrogen prior to widespread availability of fuel cell vehicles will facilitate faster transition to renewable hydrogen and natural gas economy.
Identification of Design Strategies and Their Effects on Performance Outcomes in Pair Parameter Design Tasks
Authors: Avşar Alkım Z. , Grogan Paul T.
Published: November 2023

Understanding design processes and behaviors are important for building more effective design outcomes. During design tasks, teams exhibit sequences of actions that form strategies. This paper investigates patterns of design actions to identify successful design strategies in paired parameter design tasks. The paper uses secondary data from a design experiment in which each pair completes a series of simplified cooperative parameter design tasks to minimize completion time. Analysis of 192 task observations uses principal component analysis to identify design strategies and regression analysis to evaluate their impacts on performance outcomes. Results show that the design strategy of short average action time, small average action size, and low action variation significantly decreases completion time. Discussion of results suggests smaller and more frequent actions provide more rapid feedback about each action to improve communication and understanding between pairs, leading to more efficient design processes. Results show that task order and the number of variables also significantly contribute to performance outcomes, which aligns with past literature. Results also show a negative relationship between lower English ability, experience level, and team performance outcomes. The discussion suggests that lower English ability can be a barrier to communication between pairs, and a lower experience level can decrease the ability to create effective strategies.
Topology Optimization With Quantum Approximate Bayesian Optimization Algorithm
Authors: Kim Jungin E. , Wang Yan
Published: November 2023

Despite its wide scope of applications, topology optimization faces a major challenge, which is its high computational expenses caused by the large number of design variables and the underlying physics. Because the number of possible material layouts increases exponentially, the search for the optimum becomes computationally intractable. Recently, quantum computing emerged as an alternative paradigm for solving optimization problems with potentially much higher efficiency than classical computing. The quantum superposition phenomenon allows quantum computer to perform a parallel search of many possible solutions. In this work, a two-mixer quantum approximate Bayesian optimization algorithm (TM-QABOA) is developed as a hybrid quantum-classical optimization algorithm, where Pauli-X mixers and generalized Grover mixers are applied in an alternating fashion. TM-QABOA enables a dynamic exploration-exploitation balance to improve searching efficiency. The exploration is done with the Pauli-X mixers, whereas the exploitation is enhanced by the generalized Grover mixers through amplitude amplification. Surrogate-based Bayesian optimization is used to optimize the hyperparameters of the quantum circuit, i.e., rotation angles. The algorithm can also be used to solve mixed-integer optimization problems. The feasibility of TM-QABOA to solve optimization problems is demonstrated with two simple examples. One is two-dimensional truss design, and the other is metamaterials design.
A Quantitative Comparison of Thermo-Responsive Actuators: Balancing Functionality and Energy Efficiency
Authors: Louis Rafaela , Shea Kristina
Published: November 2023

The term 4D printing is used to describe the 3D printing of systems that through their design and functional materials are responsive to the environment. The goal of 4D printed systems is to establish them as low-energy, lightweight, and functionally integrated alternatives to conventional automation systems. Numerous new materials, fabrication processes and proof-of-concepts for 4D printed actuators and structures have been developed since the emergence of the field in 2014. Many possibilities to combine and leverage different actuation principles have been explored with the vision to create structures that function autonomously in reaction to environmental changes, their reaction embedded into the material and structural design. Systematic design knowledge is needed to consider environment-responsive actuation from an early design stage and integrate it effectively into a system. Since a main goal of this approach is to reduce required energy, an estimation of both functional and environmental performance, in terms of embodied energy and activation energy, needs to be known for different actuation principles and materials to select among them.

While new actuator designs are regularly published, the publications rarely include a performance characterization that considers both functional and environmental performance. This paper provides a first comparison of both functional and energy aspects of environment-responsive actuators. For this purpose, thermo-responsive actuator designs in literature are screened and eight actuators are compared according to their functional material, working principle, actuation force and stroke length. Further design characteristics of weight, materials, embodied and actuation energy are also estimated for the selected actuators. Based on the compiled data, the embodied material energy of the actuators and energy in use are compared and evaluated. The paper concludes with a discussion of the various options and implications for their integration in low-energy design of environment-responsive structures.
Knowledge Management for Data Analytics in Additive Manufacturing
Authors: Park Yeun , Witherell Paul , Jones Albert , Cho Hyunbo
Published: November 2023

As a multi-staged digital manufacturing process, Additive manufacturing (AM) inherently benefits from data analytics (DA) decision-making opportunities. The abundance of data associated with the various observations and measurements taken throughout the design-to-product transformation creates ample opportunities for iterative, process improvements. To best formulate and address these opportunities, knowledge needs to be strategically and deliberately managed for efficient DA development. However, knowledge in AM is broad and comparatively sparse, making it difficult to create robust DA solutions. Also, existing methods for knowledge management in AM are often case-dependent. To address such challenges, this paper proposes a novel framework to manage case-independent knowledge for AM data analytics. The proposed framework consists of two phases: a knowledge-identification phase and a knowledge-representation phase. A knowledge architecture is defined to provide a reference for discovering knowledge that facilitates AM data analytics. In the knowledge identification phase, the architecture is used to facilitate the identification of actionable knowledge relevant to a specific DA use case. In the knowledge representation phase, ontologies are used for representing and linking that identified knowledge. A case study of application scenarios demonstrates how actionable knowledge is identified, represented, and managed by the framework. The framework enhances efficiency of AM data analytics development and enables knowledge sharing, understanding and reuse in AM data analytics activities.
Diffusing the Optimal Topology: A Generative Optimization Approach
Authors: Giannone Giorgio , Ahmed Faez
Published: November 2023

Topology Optimization is a widely used method to find optimal designs that meet constraints and maximize system performance. However, traditional iterative optimization methods such as SIMP are computationally expensive and prone to getting stuck in local minima, limiting their effectiveness for complex or large-scale problems. To overcome these challenges, we propose a novel Generative Optimization method that integrates classic optimization (SIMP) as a refining mechanism for the topologies generated by a diffusion model. By introducing a computationally efficient approximation inspired by classic ODE solutions, the need for conditioning on physical fields is eliminated, reducing inference time by half. Our method facilitates the generation of feasible and high-performance topologies while explicitly guiding the process towards regions with superior manufacturability and performance without the need for external auxiliary models or additional labeled data. Notably, our approach overcomes drawbacks encountered by learning-based methods in out-of-distribution constraint configurations, including floating material and high compliance error, as well as the requirement for extensive pre-processing and surrogate models. Experimental results showcase a substantial 20% increase in performance. Overall, the integration of deep generative models, such as Diffusion models and Generative Adversarial Networks, to warm start optimization holds significant promise in advancing the design and optimization of structures in engineering applications, extending its applicability to a broader spectrum of performance-aware engineering design problems.
Ultracapacitors in Light Duty Hybrid Electric Vehicle Energy Storage Systems: Technical Impact and Economic Perspectives
Authors: Pakštys Saulius , Bonfitto Angelo , Hegde Shailesh
Published: November 2023

The current status of the global climate situation brings a great requirement for industries to develop novel solutions for products that minimise their environmental impact. This includes the careful selection of materials and production processes such that the end-of-life product is economically recoverable. These concepts are lately being extended to the automotive industry where critical components such as batteries and electric machines display a negative environmental impact when they reach their end-of-life. This paper analyses battery capacity reduction by implementing an ultracapacitor stack into a hybrid electric vehicle in an attempt to minimise battery material and alleviate its power demand. An environmental and economic consideration are made in the comparison between a hybrid energy storage system and the baseline energy storage system which is composed of a 48 V, 20 Ah capacity battery. Three cases of battery sizes are presented alongside a 21.6 V ultracapacitor to form the hybrid energy storage system, each at 24 V and 20, 15 and 10 Ah of capacity. The results indicate decreased total energy storage cost (up to 25% lower with respect to the baseline) and lower emissions related to total energy storage production (up to 37.4% with respect to the baseline).
How to Encode Microstructure in Machine Learning: A Comparison Study
Authors: Wu Yulun , Li Yumeng
Published: November 2023

Accurately predicting the response of materials under different loading conditions is crucial for designing and developing new materials with desired properties. However, this process can be computationally expensive and challenging, especially for heterogeneous materials with complex microstructures. Recently, machine learning has been widely used to address the challenge for developing predictive models for various material systems with reduced reliance on extensive experimental testings and repetitive expensive physics simulations. The microstructure of a material plays a critical role in determining its properties, making it a key factor that needs to be accounted for in predictive modeling. Heterogeneous materials, specifically, often have complex microstructures with numerous features like pores, inclusions, and grain boundaries, which need to be accurately captured but is hard to be quantified for developing machine learning based predictive models. Therefore, accurate encoding of microstructural features is essential for making reliable predictions. Nevertheless, how to effectively and efficiently capture the complex microstructural features in developing machine learning based predictive models largely remains an open question for researchers in the field of materials science. In this paper, we present a comparison study of different encoding methods for microstructures in machine learning models. Specifically, we investigate pre-defined encoding methods and automatic encoding methods for a synthetic heterogeneous material system. the performance of each machine learning model is evaluated by predicting material responses such as strain energy. Our results show that convolutional neural networks (CNNs) have the ability to auto-encode the microstructure information of material and make promising prediction, especially when good pre-defined descriptors are not available. Overall, this study provides valuable insights into the performance of different encoding methods for microstructures in machine learning models, and can inform the development of more accurate and efficient models for materials science applications.
Numerical Optimization of Underactuated Flexure-Based Grippers
Authors: Okken Boi , Dekker Jan P. , de Jong Jan J. , Brouwer Dannis M.
Published: November 2023

Robotic manipulation in the agri-food industry faces several issues, including object variation, fragility and food safety. Underactuated flexure-based gripper allow passive adaptation to object variation, whilst monolithic flexure joints drive down cost, part-count, hygiene requirements, contamination and wear. However, designing flexure-based grippers presents challenges in achieving sufficient support stiffness, load-bearing capacity and joint deflection. Additionally, modeling the non-linear flexure behavior may become computationally expensive, especially under wide a variety of load cases, limiting the optimization approaches to simple structures and joints. In this work we present an interleaved computational optimization algorithm for underactuated flexure-based grippers, aimed at maximizing the range of graspable circular objects under a given load. This method achieves a superior design faster than state-of-the-art methods that optimize all design parameters simultaneously. A prototype constructed using rapid-prototyping validates the usage of the design method, and experimentally illustrates gripper performance.
The Giving Garden: Realizing Community and Fostering a Connection to the Land Through Co-Design in Duluth, Minnesota
Authors: Konrath Austin , Clarke-Sather Abigail R. , Laroche Regina , Bliss Morgan , Masawi Rumbidzai
Published: November 2023

Communities with Healthy Food Priority Areas (HFPA) across the U.S. decreased the quality of life and well-being of individuals. Duluth, Minnesota suffers from several HFPAs. In response, a local organization, “the St. Mark Giving Garden”, promotes healthy living, centering African American resiliency and traditional identity through the communal production of culturally relevant foods. The organization started a community garden in an existing garden site to remedy the food disparities in the Central and East Hillside neighborhoods of Duluth.

After success in the 2022 growing season, the organization looked to expand into a new, larger site. The St. Mark Giving Garden utilized co-design to develop this future community garden site. A leadership committee was organized including members from multiple community stakeholders and partner organizations. This committee defined the goals of the project and then began holding community design sessions to begin the development process. Co-design places the end-user at the foundation of a design project. In this community garden project, the Central and East Hillside neighborhoods became an integral part of the design process; community members presented concerns, desires, and additions to elevate the project and completely benefit the community.

Co-design provides a powerful opportunity for generating an effective product that holistically meets all of the end-users needs. This project compared a community garden design draft produced without co-design and a design produced with co-design. The co-designed product is superior in several ways relative to the preliminary design: it maximized the type of space by decreasing the area of communal space by 30% and increasing the area of exercise and performance space by 8000% and 600% respectively; minimized the number of unnecessary elements, such as eliminating more than 50% of fencing; and increased the number of connections between elements by 200%. These improvements are more reflective of the values and needs of the community and assist in better serving the community.
Gaze-Based Augmented Reality Interfaces for Micro- and Macro-Scale Tasks
Authors: Petlowany Christina , Pryor Mitch
Published: November 2023

Augmented Reality (AR) and gaze-based user interfaces (UI) can reduce a user’s cognitive load in both robotics and manufacturing applications. This paper describes the development and evaluation of a gaze-based AR system to assist users with an assembly task for future use in human-robot interaction (HRI) that is both scalable and benchmarked to determine its efficacy in different scales of environments and with different densities of AR-inclusive items. Testing includes analysis of the effects of a dense environment on the system. The system worked for all initial participants with qualitative positive feedback on the system. Constructive criticism focused on tailoring the dwell time to the individual. The results provide future developers with guidance to determine the practical density of an AR-inclusive environment.
Adapting Digital Twin Frameworks Toward Lean Manufacturing for the Circular Economy
Authors: Ferrero Vincenzo , Alqseer Naser , Reslan Maya , Morris K. C.
Published: November 2023

Lean manufacturing is based on data collection and analysis that is used toward reducing waste and enabling continuous improvement. However, little research has been done bridging emerging topics of a Circular Economy and Digital Twins to lean manufacturing systems. A lean enabled manufacturing digital twin can provide more efficient interactions between these stakeholders and lean activities. Building on previous literature and the ISO 23247 standard — the Digital Twin Framework for Manufacturing — this paper identifies functional requirements for the adaptation of lean manufacturing to a digital twin model. The requirements are defined in terms of a multiple layers of the system: physical space, cyber-physical storage, primary processing, models and algorithms, analysis, feedback, and interfaces. To correlate a lean digital twin framework to the current standard effort, we identify the digitized-lean requirements that can be applied to standards in reference to ISO 23247. In practice, the digitalization of lean manufacturing can improve top-down management recognition, aid decision making, increase cost efficiency, sustain continuous improvement, enhance worker engagement, and support communication with stakeholders. Furthermore, the lean-adapted digital twin framework introduced and subsequent requirements can help interface lean to smart manufacturing systems, and apply standard lean principles to sustainability initiatives such as the Circular Economy.
SEA-Leg: A Wheel-Leg Hybrid Locomotion Platform for Uncontrolled Environments Using Series Elastic Actuators
Authors: Swerczek Nick , Nelson Carl A. , Cerny John , Tannehill Amber
Published: November 2023

In this paper, we present the SEA-Leg robotic mobility platform: a wheel-leg hybrid locomotion system which uses a 2 degree-of-freedom linkage driven by two series-elastic actuators to create a passive suspension system featuring the reconfigurability that makes legged systems advantageous. A method for simplifying the wheel-leg’s nonlinear dynamics to a model analogous to the traditional suspension system model is presented along with methods for its validation. A mechanical design which meets constraints outlined for SEA-Leg’s intended application as a platform for off-world exploration is presented, and a prototype was constructed. Initial testing of the SEA-Leg’s performance confirms its ability to meet the constraints laid out in this paper and implies validation of the lumped-stiffness approximation model.
Design Process for Dimensioning Elastomer Bearings for Winch Based Point Absorbers
Authors: Andersson Kjell , Duraisamy Rimmie
Published: November 2023

Wave energy is a huge power source that can make a significant contribution to the production of renewable energy in the future. It can generate as much as 10% of the world’s present energy consumption [1].

However, Wave Energy Converters (WECs) is a relatively new field, which requires new technology development, and testing before efficient WECs can give any significant contribution to the energy production. The main difficulties lie in the harsh marine environment and the fact that wave power is delivered with high forces and low speeds. The high forces necessitates strong and durable mechanical structures and the slow speed makes efficient energy conversion challenging. Further, there is a very large variation in wave height over time, and during storms, the largest waves can be 10–25 times higher than the average wave height at the site. The maximum forces and motions can thereby be tremendous if not handled properly, and makes the development of efficient Wave Energy Converters (WEC) challenging. The dimensioning forces strongly depend on the wave power concept, the WEC implementation and the actual Power Take-Off (PTO) system. Of the many different types of WECs being researched and developed, this paper focuses on the development of a winch based point absorber wave energy converters (WBPA-WECs).

This paper presents the development of a solution for a flexible chain where an elastomeric bearing is used as a means to achieve the relative motion between the links in a chain. With this solution no sliding is present and the motion is achieved as a deformation in the elastomeric bearing.

A design process is formulated for determining a suitable configuration of layers of steel shims and elastomers. A test rig for bearing tests has been developed and results from these experimental tests in combination with material tests and FE analysis are presented in the paper. These are presented in the context of illustrating the proposed design process for designing these type of laminated elastomer bearings.

The different steps in this design process have been illustrated for the defined test bearing and we can conclude that our findings indicate that the proposed process can be an important support method for further development of this approach with elastomer bearings.
DC Motor Velocity Control With Integral Retarded Controller Under Unintentional Delay
Authors: Fan Haonan , Ramírez Adrián , Mondié Sabine , Sipahi Rifat
Published: November 2023

PID controllers are an industry standard. These controllers are not only effective in many applications, but also intuitive for practitioners to utilize them under a variety of conditions. However, balancing multiple control objectives is still not a trivial task, especially one has to deal with noisy measurements and a relatively high bandwidth of operation is expected from the closed-loop system. An alternative to PIDs has been recently revisited with promising, analytical tuning formula and capabilities to effectively control plants with noisy measurement even without low-pass filters. These controllers are called delay-based controllers whereby an intentional delay is introduced into the controller to improve speed of response, reduce noise, and even improve set point regulation. One such controller is the focus of this manuscript, namely, an Integral Retarded (IR) controller. This controller utilizes an intentional delay to reduce actuator chattering against noisy measurements, while its integral part achieves zero steady-state error for set-point regulation of Type-0 open loop systems. This controller can be tuned analytically to achieve, at the same time, rightmost root placement to render certain speed of response in closed loop. However, measurements can be unintentionally delayed in many applications. IR framework for such cases has not been studied in the literature. Here, we provide new tuning rules for such cases, as well as validations through simulations and a DC motor speed control hardware experiment.
A Framework Establishing the Bounds of Small Angle Assumptions in Multi-Material Additively Manufactured Compliant Mechanisms
Authors: Thomas Evelyn , Meisel Nicholas , Butler Jared
Published: November 2023

Additively-manufactured multi-material compliant mechanisms are an emergent class of mechanisms; these mechanisms have a higher reliance on material properties to drive deflection when compared to other compliant mechanisms. As a result, material-specific properties such as Young’s modulus, strain response, and Poisson’s ratio must be analyzed to properly predict the performance of multi-material additively manufactured compliant mechanisms. This paper creates a framework for additive manufacturing users to characterize acceptable force-deflection relationship predictors using strain data associated with a generalized multi-material cantilever beam. This allows designers to have more confidence in the performance of their designs prior to production. When applied, the framework allows the user to identify the applied deflections where Hooke’s Law and small angle assumptions are, or are not, appropriate. This process will be useful for multi-material additive manufacturing processes capable of producing multi-material prints: directed energy deposition (metals) and material extrusion (thermoplastics). This process will serve as the basis for encouraging the widespread adoption of additively manufactured multi-material compliant mechanisms.
Computational Modal Analysis of Half Scale Business Jet Fuselage Tail Section
Authors: Gunawardana Rochana , Mechefske Christopher K.
Published: November 2023

Acoustic noise caused by structural vibrations directly affect the comfort of passengers on aircrafts throughout the duration of their flight., The dynamic characteristics of a system that dictate the behavior of such vibrations is critical to the improvement of flight conditions. Modal analysis is the practice of determining these dynamic characteristics of a structure under loading [1]. A computational modal analysis was conducted in this study with the objective of validating the previous experimental modal analysis, and providing a reliable model that can predict accurate frequency responses based on future modifications to the structure. This paper focuses on the development, validation and preliminary comparisons of the computational modal analysis. The mode shapes and natural frequencies were analyzed and validated for connectivity. The Frequency response curves for all measured points showed repeatability for the tests. Modal Assurance Criterion was measured against the data that demonstrated good similarity in mode shapes. A preliminary comparison was done between the experimental and computational data that compared differences between natural frequencies in correlated mode pairs, which were minimal. It was found that the model is underdamped in comparison to the experimental model. The next stage of this work is determined to be iteratively finding these damping values, then doing a comparison with the experimental data using a pseudo orthogonality check.
Gaussian Process Regression for Sim-to-Real Transfer of Hopping Gaits
Authors: Krause Jeremy , Alaeddini Adel , Bhounsule Pranav A.
Published: November 2023

Simulation-based controllers are relatively easy to build and evaluate, but rarely transfer seamlessly to hardware. This is because of the reality-gap which is the discrepancy between simulations and hardware. Narrowing the reality-gap can speed up the deployment of simulated controllers to hardware, also known as sim-to-real. This paper presents sim-to-real transfer of controllers on a single leg hopping robot, a system that cycles between under-actuation during the stance phase to no-actuation during flight phase. Using simulations, we design a controller to achieve speed and height regulation once-per-step, but the controller cannot achieve accurate control on hardware. Using data from hardware, we model the mis-match between simulation and hardware using Gaussian Process Regression (GPR), recompute the controller, and redeploy it in hardware. It takes about 4 iterations to achieve accurate tracking. The results show that when GPR is used to model the step-to-step level model inaccuracy, it can lead to high accuracy sim-to-real transfer while maintaining sample efficiency. A video is here: tiny.cc/idetc2023
AI-Accelerated Design of Evidence Synthesis for Global Development
Authors: Edwards Kristen M. , Song Binyang , Porciello Jaron , Engelbert Mark , Huang Carolyn , Ahmed Faez
Published: November 2023

When designing evidence-based policies and programs, decision-makers must distill key information from a vast and rapidly growing literature base. Identifying relevant literature from raw search results is time and resource intensive, and is often done by manual screening. In this study, we develop an AI agent based on a bidirectional encoder representations from transformers (BERT) model and incorporate it into a human team designing an evidence synthesis product for global development. We explore the effectiveness of the human-AI hybrid team in accelerating the evidence synthesis process. To further improve team efficiency, we enhance the human-AI hybrid team through active learning (AL). Specifically, we explore different sampling strategies, including random sampling, least confidence (LC) sampling, and highest priority (HP) sampling, to study their influence on the collaborative screening process. Results show that incorporating the BERT-based AI agent into the human team can reduce the human screening effort by 68.5% compared to the case of no AI assistance and by 16.8% compared to the industry standard of using an n-gram language model to encode texts and a support vector machine (SVM)-based classifier for identifying 80% of all relevant documents. When we apply the HP sampling strategy for AL, the human screening effort can be reduced even more to 78.3% for identifying 80% of all relevant documents compared to no AI assistance. We apply the AL-enhanced human-AI hybrid teaming workflow in the design process of three evidence gap maps (EGMs) for USAID and find it to be highly effective. These findings demonstrate how AI can accelerate the development of evidence synthesis products and promote timely evidence-based decision making in global development in a human-AI hybrid teaming context.
Physics-Informed Multi-Output Surrogate Modeling of Fusion Simulations
Authors: Maupin Kathryn A. , Tran Anh , Lewis William E. , Glinsky Michael E.
Published: November 2023

Computational simulation allows scientists to explore, observe, and test physical regimes thought to be unattainable. Validation and uncertainty quantification play crucial roles in extrapolating the use of physics-based models. Bayesian analysis provides a natural framework for incorporating the uncertainties that undeniably exist in computational modeling. However, the ability to perform quality Bayesian and uncertainty analyses is often limited by the computational expense of first-principles physics models. In the absence of a reliable low-fidelity physics model, phenomenological surrogate or machine learned models can be used to mitigate this expense; however, these data-driven models may not adhere to known physics or properties. Furthermore, the interactions of complex physics in high-fidelity codes lead to dependencies between quantities of interest (QoIs) that are difficult to quantify and capture when individual surrogates are used for each observable. Although this is not always problematic, predicting multiple QoIs with a single surrogate preserves valuable insights regarding the correlated behavior of the target observables and maximizes the information gained from available data. A method of constructing a Gaussian Process (GP) that emulates multiple QoIs simultaneously is presented. As an exemplar, we consider Magnetized Liner Inertial Fusion, a fusion concept that relies on the direct compression of magnetized, laser-heated fuel by a metal liner to achieve thermonuclear ignition. Magneto-hydrodynamics (MHD) codes calculate diagnostics to infer the state of the fuel during experiments, which cannot be measured directly. The calibration of these diagnostic metrics is complicated by sparse experimental data and the expense of high-fidelity neutron transport models. The development of an appropriate surrogate raises long-standing issues in modeling and simulation, including calibration, validation, and uncertainty quantification. The performance of the proposed multi-output GP surrogate model, which preserves correlations between QoIs, is compared to the standard single-output GP for a 1D realization of the MagLIF experiment.
An LSTM-Driven Simulation Approach for SOC and SOH Estimation of a Lithium-Ion Cell
Authors: Dhanagare Tejas , Singh Shikha , Pandey Vijitashwa
Published: November 2023

Electric vehicles are poised to replace diesel and gasoline-based vehicles all over the developed world. The benefits are many, from reducing greenhouse gas emissions to reducing overall design complexity leading to lower repair costs. One central area of investigation in this regard is battery design and efficient operation. Estimating the battery’s health is essential in successfully adapting electric vehicles. Two critical parameters of interest in the battery management system are the state of charge (SOC) and a cell’s state of health (SOH). The state of charge depicts the battery capacity percentage. It informs the end user about the remaining performance in the current cycle. The state of health suggests the battery’s remaining life before it must be replaced. In this paper, a third-order resistor-capacitor equivalent circuit model is used to model a Lithium-ion battery’s dynamic behavior. This circuit model has been shown to accurately describe the behavior of the lithium-ion battery, with high concordance between simulation results and reality. The parameters of the model are estimated using an extended Kalman filter. Subsequently, utilizing the simulation thus created, a method is introduced to perform the estimation of state of charge and state of health using a recurrent neural network model with long short-term memory architecture. The paper then presents comparison results and identifies avenues for future work.
Generative Design of Statistically Self-Similar Mechanical Structures
Authors: Hill Noah , Ebert Matthew , Maurice Mena , Krishnamurthy Vinayak
Published: November 2023

We present a novel methodology to generate mechanical structures based on the idea of fractal geometry as described by the chaos game. Chaos game is an iterative method that generates self-similar point-sets in the limiting case within a polygonal domain. By computing Voronoi tessellations on these point-sets, our method generates mechanical structures that adopts the self-similarity of the point-sets resulting in fractal distribution of local stiffness. The motivation behind our approach comes from the observation that a typical generative structural design workflow requires the ability to generate families of structures that possess shared behavioral (e.g. thermal, mechanical, etc.) characteristics making each structure distinct but feasible. However, the generation of the alternatives, almost always, requires solving an inverse structural problem which is both conceptually and computationally challenging. The objective of our work is to develop and investigate a forward-design methodology for generating families of structures that, while not identical, exhibit similar mechanical behavior in a statistical sense. To this end, the central hypothesis of our work is that structures generated using the chaos game can generate families of self-similar structures that, while not identical, exhibit similar mechanical behavior in a statistical sense. Furthermore, each family is uniquely identifiable from the parameters of the chaos game, namely, the polygonal domain, fractional distance, and number of samples. We present a systematic study of these self-similar structures through modal analysis and demonstrate a preliminary confirmation of our hypothesis.
The Design of a Low-Cost Voluntary Closing Finger Prosthetic for Developing Countries
Authors: Loutan Kelvin , Persad Umesh
Published: November 2023

Democratization of prosthetic devices has been made possible by the availability of desktop 3D printers. Low-cost body-powered open-sourced prosthetic devices are a first choice for many patients with upper limb amputations. However, the design of these devices often stems from a ‘one shape fits all’ archetype and therefore user acceptance varies. Little or no consideration is given to kinematics, size, weight or aesthetics. Novel devices reported in the literature rely on mathematical optimization of kinematic parameters to achieve human-like finger trajectories and forces. However, these devices are not open-source and lack user testing and feedback. This paper presents the design process for a new open-source low-cost 3D printed prosthetic finger, customized for an individual amputee. Parametric optimization was used to determine key dimensional parameters for simultaneous actuation of the prosthetic medial and distal phalanges. The amputee user participated in the conceptualization and testing phases of the design process. Two design iterations are presented with user feedback on fit, comfort and functionality to validate the design. The most recent iteration included three user adjustments for fine-tuning the degree of actuation. This iteration permitted desirable actuation and grasping. Future iterations should be able to facilitate diametral changes to the user’s finger due to swelling and ageing.
A Categorical Approach to Define Makerspaces
Authors: Kumar Pavan , Summers Joshua D.
Published: November 2023

This paper presents an approach to identify potential categories and benchmark makerspaces based on their narrative or illustrative definitions, intent, operational aspects captured over time and their asserted impact as per existing literature. The identified categories are further mapped and classified based on an established culture-based framework. This categorization is defined at three levels. The first two levels capture and provide the macro information that is orthogonal to each other. The third level leaf node details (presented in the appendix) are codified based on a cultural framework but are excluded from the analysis presented here. However, the leaf node is an essential listing of the various terms used in the domain of makerspaces as identified in the literature and it is observed that these terms are generally used by the various stakeholders to define their makerspaces. The discussions provide context for the first two levels and its validity through the application of DSM (Design Structure Matrix) techniques to prove its orthogonality. Effective categorization of makerspaces will support the following: (1) accurate and consistent descriptions of makerspaces, (2) clear mapping and association of the values of the makerspaces to their individual characteristics, and (3) provide a framework to design makerspaces for specific uses. The literature reviewed also provides a basis for another categorization to be developed in parallel using the factors or characteristics that define the value proposition of makerspaces. Finally, this paper presents examples of using the categorization on a sample list of makerspaces based on direct observations and literature surveys. This work aims to guide efforts towards building a framework to design makerspaces.
Dealing With Loss: How Do Designers’ Resilience and Perceptions of Scarcity Affect Perceptions of Design Performance?
Authors: Sibley Grace , Letting Cynthia , Menold Jessica
Published: November 2023

Resources are an essential aspect of individual problem solving and their availability is critical for success. Resource scarcity is common in many industries and can affect the resources available to prototype, design creatively, and work freely. We argue that designers face scarcity and short timelines on engineering teams, and it is uncertain how these constraints may affect design outcomes and strategies. We also argue that designers must be resilient to overcome challenges faced during problem-solving. In this work, we study the interactions between resilience and perceived resource scarcity and how they affect perceptions of design performance. A controlled study was conducted to understand the linkages between resource scarcity, resilience, and scarcity mindset. We found that a perceived general scarcity of time in day-to-day life significantly predicted perceptions of design performance. Further, we found that participants with higher resilience scores were less likely to have their design idea affected by the induced resource scarcity. Our work builds foundational knowledge regarding the interactions between individual resilience, general perceptions of scarcity, and resource constraints in the context of engineering design. Further work may require more longitudinal-based studies to better understand resilience and resource scarcity over longer periods of time.
Complex Terramechanics for Mobility Assessment Part 2: Efficiency Through a Simple Terramechanics Representation
Authors: Little Joseph , Coker Chris , Thornton Ben , Pesheck Eric , Jayakumar Paramsothy
Published: November 2023

The trade-off between computational efficiency and accuracy of various terramechanics simulation methods is a challenge that many engineers face when conducting analysis of off-road vehicle mobility. The Discrete Element Method (DEM) can capture complex phenomena within the soil, but requires substantial time and computing resources in comparison to the lower fidelity simple terramechanics methods such as the Bekker-Wong model. In this study, we propose a novel approach that seeks to strike a balance between efficiency and fidelity in simulating tire tractive capability in soft-soils. Our approach leverages DEM to determine Bekker-Wong parameters which encapsulate the bulk response of the DEM model and can predict accurate overall tractive performance. The process involves using DEM to generate soft-soil tire performance curves and using an optimization framework to determine simple terramechanics parameters, such as Bekker-Wong parameters, that best fit the DEM response. These parameters can then be used in other simulations for quantifying vehicle performance on soft-soil without the need for cumbersome DEM simulations. The results of the study demonstrate that the approach is promising with further refinement. Future work should focus on starting with more accurate DEM soil properties and scaling the method up to the full-vehicle level. If successful this work could ultimately provide a model with good predictive capability, while still allowing fast simulation times for agile design iteration and high run-count analyses.