bluuebunny/crossref_metadata_2025_split huggingface dataset:
Recruiting and Training the Future Workforce in Advanced Manufacturing Capabilities: A Case Study From the Tampa Bay
Authors: Amjad Muhammad Saad , Singh Pallavi , Zapata Valerie , Barger Marilyn , Diaz-Elsayed Nancy 
Published: January 2025

Abstract
The manufacturing sector is of paramount importance to a nation’s economy, as it creates employment opportunities, fosters innovation and enhances the competitive advantage in global markets. With the introduction of smart manufacturing technologies, the manufacturing sector has been able to boost its production performance, however, there is a skill gap that the manufacturing sector is facing when it comes to hiring a trained workforce. The US National Strategy for Advanced Manufacturing has underlined the importance of training an advanced manufacturing workforce with an opportunity to support historically underrepresented and underserved communities. We report the outcomes of a manufacturing training certificate program that is designed to provide equitable opportunities for high-quality, industry-relevant training that can lead to sustainable employment and economic growth. A holistic curriculum was developed to teach manufacturing education including CAD, CAM, 3D printing, AR/VR technologies, robotics and automation, assembly and production lines, and measurement systems. We report the outcomes of two cohorts that graduated in Summer 2023 and Fall 2023, in which a total of 86 training sessions were conducted. Twenty-five students enrolled and 17 students (14 males and 3 females) graduated across the two cohorts — among the graduates 47% identified as Hispanic or Latino, 41% identified as African American, and the rest identified as non-white/Hispanic; 16 of the graduates belonged to the low-to-middle income category. In the future, the program can be further expanded to include on the job training opportunities with local employers. We also provide recommendations to improve the learning experience of the students and enhance participation in the program.
Dense Spray-Coating Active Sensing Arrays for Health Monitoring of Composite Structures
Authors: Zhou Shulong , Shen Yanfeng , Wang Chunquan , Tian Yuan 
Published: January 2025

Abstract
This paper proposes a dense spray-coating active sensing arrays for composite structural health monitoring via the guided wave control. The actuation-sensing capability is realized by integrating piezoelectric nano-powder into a hosting matrix, followed by the formation of an active material layer on the composite surface through a spraying process. The actuation and sensing of guided wave mode can be achieved through the optimization of electrode configuration, in conjunction with dispersion effect. This research initiates with the analysis of the dispersion curves of composites to determine electrode parameters. Subsequently, three-dimensional numerical models in several array patterns, such as inter-digital type, comb types and irregular annular array, are meticulously constructed. Single-mode generation at one direction is achieved through the spatial distribution of array electrodes coupled with wavelength modulations in inter-digital array and comb array. At the same time, omnidirectional single-mode generation of irregular annular array is conducted through the electrode spatial distribution coupled with all direction wavelength modulations. Ultimately, the guided wave control performance is numerically investigated through exciting an anti-symmetric and a symmetric type guided wave mode in a multilayer composite plate. The numerical simulation shows that the proposed spray-coating system possesses great potential for future composite damage detection applications. The paper finishes with summary, concluding remarks, and suggestions for future work.
Toward Digital Twin of the Design Process: An Attempt to Capture Design Rationale From Spoken Words
Authors: Oizumi Kazuya , Nakazawa Ryo , Oda Yuya 
Published: January 2025

Abstract
While the drawings and other related data are shared among design teams, design rationale, such as the reasons behind the determination of design, is rarely expressed in these data. Moreover, they are rarely described even in documents such as design reports because it is too time-consuming to describe design rationale while designing a product. However, to reuse the information captured from design processes in the future, design rationale is indispensable. Therefore, a tool to capture design rationales with low workload is required. This paper attempted an automatic extraction of design rationales from conversations while designing a product. This attempt aims to establish a digital twin of a design process. Like other digital twins, sensors are installed to design processes, which are cameras, microphones and smart-glasses to detect when design rationales are spoken. The proposed method to extract design rationale detects where the design rationale is spoken based on multiple clues, which are important words, behaviors and physiological reactions. To verify the proposed method, design experiments were conducted in a controlled environment, which are designed to let designers speak out design rationales. As a result, one of the tests showed that 81% of design rationales could be extracted while 48% of where design rationales were not spoken was also extracted.
Sensitivity Analysis for the Effect of Misalignments on Performance Parameters of Harmonic Reducers
Authors: Guida Roberto , Bertolino Antonio Carlo , De Martin Andrea , Sorli Massimo 
Published: January 2025

Abstract
The rise of Urban Air Mobility (UAM) heralds a new era in transportation, offering efficient and sustainable aerial solutions tailored for urban settings. Crucial to this evolution are compact electromechanical actuators (EMA), facilitating precise control of electric Vertical Take-Off and Landing (eVTOL) aircrafts components with agility and responsiveness.
Compared to traditional hydraulic or pneumatic systems, EMA offer heightened efficiency, faster response times, and reduced maintenance needs, ideal for the UAM.
At the core of rotary compact EMA lies the harmonic reducer, ensuring high reduction ratios with minimal backlash and robust torque transmission. Its role in maintaining precision and minimizing losses is vital for system efficiency and reliability.
Reliability is paramount in UAM, necessitating redundancy measures and the adoption of Prognostics and Health Management (PHM) strategies. PHM integrates data-driven algorithms to predict component health, enabling proactive maintenance and mitigating failures.
This paper delves into the impact of misalignment on harmonic reducer performance, aiming to enhance the reliability and efficiency of EMA systems in UAM. By analyzing reducer dynamics, it aims to contribute to the advancement of UAM technologies, fostering safer and more dependable urban air mobility systems.
A Power Spectrum Bayesian Surrogate Model for a Geared Pair With a Cracked Tooth
Authors: Belli Olympio , Bittencourt Visnadi Laís , Fiori de Castro Helio 
Published: January 2025

Abstract
Even with the evolution of computation, the increased demand for high-quality models in engineering compromises its cost-effectiveness for use in several applications such as optimization, fault parameters, and uncertainty qualification. An example of this model is the dynamic simulation of gear trains and planetary. The computational cost is very high, and applications that require real-time results are impossible to obtain. To maintain the data quality, a solution to tackle this problem is to use surrogate data-driven models that can reproduce interpolated prediction. Artificial Intelligence is prominent in this area due to its unique ability to handle multi-dimensional data. However, the lack of explainability and interpretation breaks the advancements of this type of application in systems requiring high-reliability levels. In this sense, this work presents a reliable and interpretable surrogate model for a geared pair with a cracked tooth based on the combination of uncertainty quantification of Bayesian neural Networks with the interpretability of a Deep Operator Network.
Proposal for a Context-Aware Decision Support System for Waste Disposal Processes: Case Study Recycling at the Office
Authors: Ríos-Zapata David , Rendón-Vélez Elizabeth , Ruiz-Arenas Santiago , Vallejo Paola , Arbeláez-Estrada Juan Carlos , Villa-Espinal Jesus Hernando 
Published: January 2025

Abstract
Recycling processes are complex for people, and elements such as regulation variations or waste conditions affect the decision. In the business context, companies take responsibility for the final waste management. Therefore, they invest resources in training their employees in recycling and implementing manual waste separation at their internal collection centres. A context-aware decision support system that aids the waste-disposal decision-making process at companies can support people’s recycling learning process. However, a deep knowledge of the variables involved in the waste disposal process is needed, including understanding the most common mistakes, the recycling training processes, among others. This research includes the analysis of the waste-separation processes at six Colombian companies, including interviews with managers, waste-separation operators, and employees. Grounded theory was implemented to identify and select the variables affecting the recycling process, allowing the definition of the common requirements. These are the inputs for developing an intelligent system that aims to support decision-making. In this article, a solution concept is proposed. It can be argued that implementing these solutions can help companies achieve their sustainability goals, influencing people’s environmental behaviour and enhancing a circular economy.
Preliminary Design of an Additively Manufactured Bio-Inspired Thermosyphon for Thermal Energy Storage
Authors: Spence Ryan , Hazlett Gavin , Nuszkowski John , Stagon Stephen , Oshman Christopher 
Published: January 2025

Abstract
Thermal Energy Storage materials, integrated with phase-change materials (PCMs), enable near-isothermal heat extraction through thermosyphons, reducing production costs and carbon emissions. Thermosyphons, with high thermal conductivities, efficiently transfer heat through evaporation, convection, and condensation, are widely utilized for various applications. Conventional PCM thermosyphons often lack sufficient contact surface areas for optimized PCM solidification. This paper proposes a thermosyphon design inspired by fractal geometry found in biological structures, specifically the corn plant, suitable for additive manufacturing. By utilizing fractal geometry, these thermosyphons promise enhanced heat exchange through increased exterior surface area and internal liquid wicking structures. A prototype, designed for household use, has been modeled with empirical and analytical methods, focusing on capillary pressure and effective thermal conductivity. Operating at 1 kW while submerged in a low-temperature wax-based PCM (melting temperature 40°C), it comprises one main vertical tube and six base tubes with leaves. Full-scale production will utilize stainless steel powder additive manufacturing, incorporating radially and axially patterned rectangular channel cubes for the wicking structure. Analytical modeling confirms the system’s viability with an excess pressure of 2353 Pa, showing that capillary pressure will efficiently drive liquid flow despite liquid viscous pressure losses. This innovation holds promise for significant contributions to industry and building heating system decarbonization efforts.
Full Thermal Runaway Simulation of 18650 NCA Li-ion Cell With Venting and Combustion in a Large Enclosure
Authors: Ostanek Jason K. , Parhizi Mohammad , Kwon Byoungchul , Jeevarajan Judith A. 
Published: January 2025

Abstract
Thermal runaway in lithium-ion (Li-ion) cells in a module or battery pack features a wide range of length and time scales as well as many complex physical phenomena, presenting significant challenges for numerical simulation. Including fluid domain effects, such as venting and combustion, increases computation time significantly beyond the transient heat conduction problem. There exists a need to develop and validate new, efficient computational strategies for these multiphysics problems. The present work examines thermal runaway of a single 18650 cell placed in a large open container and subjected to thermal abuse via an external heater. The model simulates the complete thermal runaway process and includes decomposition reactions, internal heat and gas generation, vent gas flow, and combustion of the vent gas. Using strategic modeling assumptions, such as switching to a lumped body formulation during the sharpest heat release, allowed an economical modeling approach. Using detailed cell geometric modeling and detailed thermophysical parameter identification resulted in good agreement with experiments. The distribution of heat through the cell body and into the surrounding space was captured, as well as the spatial distribution of the vented gases. The model approach may be extended to modules or battery packs to investigate the risk of thermal propagation, including effects of venting and combustion.
Kinetic Insights and Morphological Transformations in Coal Gasification: An In-Depth Analysis With Model Validation
Authors: Khan Haider , Savvopoulos Symeon , Janajreh Isam 
Published: January 2025

Abstract
Understanding the reactivity and kinetics of feedstock particles is critical for process optimization, reactor design, and industrial gasifier scaling. This study investigates the impact of particle size on the gasification behavior of coal, focusing on the temperature and gas species concentration profiles during the process. Coal particles of varying sizes (1 mm, 4 mm, 6 mm, and 8 mm) were subjected to high-temperature gasification in a furnace maintained at 1073 K and under limited air supply. Temperature profiles are analyzed. The results indicate that smaller particles (4 mm) achieved higher peak temperatures suggesting faster reaction rates, while larger particles (6mm and 8mm) exhibited slower and lower peak temperatures. We also employed a sophisticated mathematical model integrating key equations for energy balance, heat flux, and char mass balance to simulate the residence time, core temperature and gas concentrations of O2, CO, and CO2 of the coal. Experimental data and model estimates indicate that thermal conductivity increases with particle size, while activation energy decreases at lower temperatures, suggesting higher reaction rates for smaller particles. Based on the validated numerical model, the sharper and faster reactions observed in the gas species concentration profiles for smaller particles indicate quicker gasification. The results show good alignment between experimental data and model predictions. These findings underscore the critical influence of particle size on thermal dynamics and reaction kinetics during coal gasification, highlighting the need for optimized particle size distribution to maximize gasification efficiency.
A Decarbonization Strategy for Preserving the Galapagos Islands With Green Hydrogen in Maritime Transportation: Empowering a Sustainable and Inclusive Green Tourism
Authors: Valarezo Alfredo , Ramos Emilia , Castro Westly , León Marco , Yépez Krutskaya , Bejarano M. Lorena , Escudero David , Thomson Camilla , Ingram David , Mignard Dimitri 
Published: January 2025

Abstract
This paper presents a proposal for implementing green hydrogen as a key component in decarbonizing maritime transportation in the Galapagos Islands. The objective is to transition interisland transportation for tourists using fuel-cell-powered ferries. Leveraging renewable energy sources, particularly wind and solar, as well as a mix of them, an integrated techno-economic analysis of the system is presented for producing, storing, and utilizing green hydrogen fuel. The approach addresses the unique challenges of the remote islands, focusing on the environmental impact and local sources of solar radiation and wind speeds in the vicinity of Baltra Island. The paper discusses a construction plan for a hydrogen plant capable of producing about 100 kg of hydrogen per day, utilizing AEM (anion-exchange membrane) modular electrolyzers for production and batteries and hydrogen tanks for storage. The feasibility and costs of three project scenarios (wind, solar, and hybrid) regarding energy coverage throughout the year, excess energy production fed to the grid, and cost sensitivity analyses of the energy market are thoroughly examined. This work contributes to the broader goal of achieving NetZero carbon for the Galapagos and preserving the pristine ecosystems of this UNESCO World Heritage site.
Investigating the Impact of Deposition Pressure on CRN Coating Properties and Machining Performance With Build Up Edge Formation
Authors: Chowdhury Mohammad Shariful Islam , Bose Bipasha , Arif Abul Fazal M. , Veldhuis Stephen 
Published: January 2025

Abstract
Coating properties, including hardness, elastic modulus, roughness, adhesion, and residual stress, significantly impact tool performance and wear patterns in machining. Physical vapor deposition (PVD) coatings offer tailored property combinations for specific applications, adjustable through deposition conditions like N2 gas pressure. This study focuses on customizing CrN coatings to enhance machining, particularly in reducing built-up edge (BUE) formation. Three CrN coatings with varying N2 gas pressures were deposited and characterized using X-ray diffraction (XRD) and nanoindentation testing. These techniques provided insights into the coatings’ structural and mechanical properties. The wear performance of these coatings was evaluated through a series of machining tests involving the finish turning of TiAl6V4 titanium alloy. Tool life studies were conducted to assess the coatings’ performance under machining conditions, while 3D wear volume measurements were executed to quantify the degree of tool wear and observe its progression. The results demonstrated that tailored CrN coatings with specific properties, such as a high elastic modulus, low H/E ratio and high plasticity index, were highly effective in reducing problems associated with sticking and built-up edge (BUE) formation.
Disruption Management of Interdependent Power Networks Using a Data-Driven Co-Design Approach for Enhanced System Resilience
Authors: Chung In-Bum , Luo Yi , Wang Pingfeng 
Published: January 2025

Abstract
With growing complexities and interdependencies, critical infrastructure systems such as power grids are likely to suffer from external disturbances and be vulnerable to disruptions. Hence, it is crucial for these systems to be designed and maintained with high resilience against disruptive events. A typical system undergoes three stages of operation: the normal stage, the degradation stage, and the recovery stage. To enhance the system’s resilience, the system can be designed so that the degree of degradation is minimal or the recovery strategy is operated effectively and efficiently to restore the nominal performance. In this paper, an integrated co-design framework is presented considering interactions between system design and recovery, where the post-disruption management scheme is considered in the pre-disruption design stage. This could result in a design that can easily be subjected to better restoration. For the optimization framework, a graph convolutional network was employed to reduce the computational burden in evaluating the performance of a power system during optimization. To show the efficacy of the co-design framework, it was applied and compared to a conventional sequential approach. For the case study, a distribution network of 37 buses based on an IEEE benchmark test network was employed. Both methods were able to create better designs compared to the original. While the sequential approach reduced the total length of connection lines by 0.11 percentage points more than the co-design approach, the latter performed approximately twice as well in terms of resilience. Although there was evidence of a trade-off between the network design and resilience, the case study results have proven that the developed co-design framework can enhance the network system’s resilience while improving the design.
Applying Binary Stiffness Mechanism to an Exoskeleton Arm for Casualty Evacuation
Authors: Chen Puning , Xie Yangming , Liu Chang , Wang Dahai , Dong Janet 
Published: January 2025

Abstract
A new binary structure is designed and summarized, which can be applied to the auxiliary wearable robotic exoskeleton design for casualty transfer. This binary structure, substituting for a conventional spring, utilizes the material’s elastic deformation characteristics to produce a significant increase in resistive force abruptly once a certain displacement threshold is exceeded, creating a spring-like effect. The binary structure provides flexibility for arm motion and substantial pulling force for maintaining the arm in position, transitioning smoothly from high flexibility to supportive rigidity as needed. Human elbow joints achieve a maximum range of motion within a ±20 mm displacement when the upper and lower arms are at a 90-degree angle, which also corresponds to the position where the device is connected to the forearm and the binary structure is not under load. The exoskeleton system, integrated with this mechanism, assists soldiers in transporting casualties with an additional support force of up to 320N while reducing arm injuries and fatigue. An approach is summarized, combining simulation-based theoretical analysis with experimental validations, demonstrating the potential of this binary structure to change casualty evacuation efforts in military operations.
Damage Mechanics–Based Simulations of Cutter-Rock Interaction With an Integrated Cloud CAE Platform
Authors: Gan Xiaoge , Song Fei , Zhao Shun , Panayirci Huseyin Murat , Gu Xuan , Villareal Steven , Li Ke , Alteirac Laurent , Zhang Youhe , Gjertsen Ole 
Published: January 2025

Abstract
Enhancing bit rock-breaking efficiency is the prerequisite for improving the rate of penetration (ROP) in drilling. The investigation of the rock-breaking mechanism in cutting is essential to evaluate and design the drill bit. Over the years, the oil and gas industry has developed a deep knowledge of cutter-rock interaction (CRI) that is vital for optimizing drilling and excavation processes. Drill bit design and drilling optimization requires a labor-intensive process to study CRI, which usually requires significant effort and resources based on experimental characterization. Therefore, the design and implementation of a high performing platform to enable efficient prediction of cutter-rock interaction is highly desired.
In this study, a new virtual CRI platform-digital rock laboratory (DRL) has been developed for accelerating accurate simulation of cutter-rock interaction at scale. The nonlinear finite element analysis (FEA) models that can reasonably describe the cutter-rock interaction mechanism with damage mechanics are first developed and serve as the compute engine for the computer aided engineering (CAE) platform. It has been shown that the developed FEA models agree favorably with experimental results in terms of cutting forces for various rocks, which has validated the modeling approach. We then introduce the process of developing a cloud-based computation platform for virtual cutter-rock interaction and showcase its advantages over traditional on-premises computing solutions. We highlight how the cloud-based platform replaces a years-long laborious laboratory testing process that is necessary to drill bit and cutting element development. This study aims to demonstrate the benefits of our cloud-based virtual CRI solution, including its novelty, efficiency, and sustainability advantages. Furthermore, the use of the platform can significantly reduce the cost and time for physical verification and validation (V&V) process that is necessary to minimum new product risks in the development, making it a more sustainable solution.