bluuebunny/crossref_metadata_2025_split huggingface dataset:
State of Health Estimation of Electric Vehicle Batteries Using Transformer-Based Neural Network
Authors: Zhao Yixin , Behdad Sara 
Published: November 2023

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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.