bluuebunny/crossref_metadata_2025_split huggingface dataset:
Towards Understanding the Relationships Between Personal Motivation and Design Quality
Authors: Blackwell Spencer , Kado Corey , Bohm Matt , Kames Elisabeth 
Published: January 2025

Abstract
This paper presents a case study exploring the effects of individual motivation on design quality. For this study, design quality was measured by industry professionals and engineering faculty using a design rubric comparing factors such as manufacturability, customer satisfaction, and product fit. The Serious Leisure Inventory and Measure (SLIM) survey is used to determine individual motivational factors as well as associated perceived leisure benefits. The purpose of this study is to determine optimal team composition and to create strategies that maximize design quality of a specific product. For this study, eighteen groups of 3–5 senior-level mechanical engineering students were observed. The results of this study indicated that teams containing individuals motivated by singular leisure aspects are more likely to produce lower-quality design outcomes, but initial multivariate analyses indicate the potential for positive correlations between certain motivators.
Model Predictive Optimal Traffic Signal Control Using Neural Network Based on Real-Time Measured Traffic Information
Authors: Kim Minjung , Yoon Hwan-Sik 
Published: January 2025

Abstract
Optimal traffic signal control can improve network-wide fuel efficiency as well as alleviate traffic congestion. Traditionally, traffic signals have been controlled using limited vehicle detection sensors; however, the recent advent of advanced traffic detection technologies, such as the sensor fusion approach that combines multimodal data from cameras and radars, enables the real-time collection of rich traffic information. The real-time traffic data can be used to predict future traffic flow and control traffic signals for optimization targets such as traffic throughput, total travel time, and network-wide fuel consumption. This paper proposes a Model Predictive Control (MPC) algorithm for optimal traffic signal control at real-world traffic intersections. The proposed traffic signal MPC method employs a Neural Network-based future traffic volume prediction model to minimize network-wide fuel consumption and unnecessary vehicle waiting time. Genetic Algorithm (GA) is used as a global optimization method to determine optimal control parameters including the prediction time horizon, optimal weights for road preference and waiting time of vehicles. In order to evaluate the effectiveness of the proposed method, a traffic simulation model is developed in SUMO, a high-fidelity traffic simulation environment. The traffic simulation model is based on a real-world traffic network incorporating the real vehicle detection range of a state-of-the-art sensor technology within the network. The incoming traffic flow in the model is simulated using actually measured traffic data from the traffic network, enabling a comprehensive assessment of the novel optimal traffic signal control method in realistic conditions. The simulation results show that the proposed traffic signal MPC method can significantly reduce network-wide fuel consumption compared to the conventional fixed-time control method. Additionally, incorporating truck priority in the control algorithm leads to further improvements in fuel consumption reduction.
Integrating Motion Intention and Impedance Control in Teleoperated Robotic Rehabilitation for Upper Extremity Disorders
Authors: Banzon Angelo-Jose , Wang Emily , Luo Yue , Jin Li , Jiang Lin 
Published: January 2025

Abstract
Due to the global pandemic in 2020, rehabilitation services had to adapt to virtual care to meet the needs of patients with a diversity of upper extremity disorders and injuries, from simple fingertip injuries to replanted extremities, while mitigating the spread of COVID-19. For patients who require occupational therapy in their rehabilitation, teleoperated robotic care can fulfill this need. Moreover, some clinical studies show that incorporating the active intention of patients into rehabilitation training enhances the effectiveness of therapies. This research aims to develop a bilateral impedance controller with a Franka Emika 7 DOF (degree-of-freedom) articulated robot using MATLAB, facilitating upper limb occupational therapy. Through this setup, the Franka robot will lead patients through various therapeutic exercises based on their motion intention, such as a pick-and-place exercise. The exercises’ efficacy is assessed through evaluations of torque, muscle strength, and jerking performance. During patients’ rehabilitation exercises with the Franka Robot, we anticipate observing decreased muscle activity alongside a smooth, assist-as-needed trajectory. This method will allow a patient to perform complex movements essential for daily activities quicker than traditional occupational therapy.
Vision-Guided End Effector Trajectory Planning and Workspace Edge Drifting for Enhanced Real-Time Robot Teleoperation
Authors: Saeed Salman , Sun Katie , Duong Han Xuyen , Jiang Lin 
Published: January 2025

Abstract
Teleoperated robotic systems that have computer vision technology are expanding across different industries such as surgical, industrial, and space or ocean exploration applications. While the benefits of vision-guided robot control are evident, challenges arise due to limitations in traditional workspace Cartesian mapping methods, particularly in avoiding singularity points. These limitations can result in jerking movements or system shutdowns at the remote end, presenting significant hurdles to seamless operation. This work integrates two algorithms in the robot teleoperation: 1) the edge drifting algorithm for the teleoperation workspace mapping, and 2) the deep learning algorithm for obstacle recognition and collision avoidance. By combining these two approaches in robot control, we aim to cover the robot’s workspace during task operation with improved accuracy and efficiency, ensuring both safety and seamless motion planning for robot teleoperation.
Understanding Cranial Biomechanics and Preventing Brain Injury
Authors: Brady Owen , Saboori Parisa 
Published: January 2025

Abstract
The subarachnoid space is located around the brain, between the arachnoid and pia maters. Within the subarachnoid space are supports called trabeculae. The subarachnoid trabeculae’s primary function is to dampen brain movement. Thus, they are integral in the protection of the brain when there is an impact on the head. There are five known structures of trabeculae found in the subarachnoid space.
Using ABAQUS, two-dimensional models were generated for each of the five structures. They were then run in a finite element analysis with a load to the skull to compare the strain on the brain. Then, using SOLIDWORKS, three-dimensional models were created to complete a finite element analysis with the inclusion of a cerebrospinal fluid equivalent.
The most protective structure of trabeculae results in the least amount of strain on the brain. This allows for a known hierarchy of protectiveness regarding the five main structures.
Economical Catalyst for Fuel Cell Applications: Cobalt-Based Layered Double Hydroxides (LDHS)
Authors: Olukeye Tiwaloluwa , Alshweiki Ali , Udeochu Uche , Bari Bushra , Tyagi Pawan 
Published: January 2025

Abstract
In fuel cell technology, researchers are fervently looking for alternatives to expensive and rare materials like platinum as the demand for sustainable energy solutions continues to rise. Due to their remarkable electrochemical properties, layered double hydroxides (LDHs) based on cobalt have emerged as intriguing potential candidates. In this study, we investigate the possibility of using LDHs instead of platinum in fuel cells. We aim to reveal the potential of LDHs to revolutionize the landscape of fuel cell applications by providing a sustainable and financially feasible solution for the production of clean energy. This is accomplished by conducting a comprehensive analysis of their structural characteristics, catalytic activity, and stability. A two-step process was used to synthesize the Co-LDH catalyst. Deionized water, isopropanol, and polytetrafluoroethylene (5% by weight) were first combined in a ratio of 4:1:0.05 to create a catalyst ink. This ink was used to make a homogeneous mixture of Co-LDH (5 mg/mL), which was then applied to a glassy carbon electrode (GCE) by ultrasonication. Using a BASi Epsilon Eclipse Electrochemical Analyzer with a three-way electrode setup of a Pt wire counter electrode, an Ag/AgCl reference electrode, and the Co-LDH-modified GCE as the working electrode, electrochemical studies of the GCE were carried out. After applying 15μl of cobalt ink to the surface of the glassy carbon electrode, the ink was air-dried for 24 hours at room temperature. The electrolyte for the cyclic voltammetry (CV) experiment was 0.1M HClO4. According to these findings, the presence of the catalyst enhances both the anodic and cathodic peaks. The operating temperature was also adjusted to show which electrolyte exhibited better selectivity. This experiment’s findings will serve as a benchmark for a fuel cell system with built-up working electrodes to compare region-specific characteristics.
Patterned Electrodes for Hydrogen Production in Alkaline-Water Electrolysis
Authors: Shahreza Mahmoud Saleh , Albayati Ibrahim M. , Aliyu Aliyu 
Published: January 2025

Abstract
Over the last decade, there has been a re-emergence of hydrogen as a clean fuel for the energy transition towards net-zero carbon emissions. Hence, significant interest has been shown in low-carbon and green hydrogen to power this transition. Water electrolysis has been identified as the most common method for green hydrogen production. However, increasing cell faradaic efficiencies for deployment at scale involves improving every part of the system, e.g., the electrodes. Recent investigations have suggested that the surface structure of electrodes can affect hydrogen evolution by increasing the active surface area of reaction. Yet, this has been explored with micro-pillars and micro-pits which have complex manufacturing processes. Hence, this study investigates surface macro-pits and patterns as more suitable replacements for industrial application. Steady-state, multi-phase computational simulations were carried out to investigate the characteristics hydrogen evolution over flat and macro-dimpled electrodes. Two current densities in alkaline-water electrolysis were explored. The results show that macro-dimples significantly eliminate static gas pockets when compared to flat electrodes suggesting more dynamic bubble detachment. Furthermore, dimple size also affects the HER process. In summary, the results hint at macro-patterns being promising in controlling hydrogen bubble evolution for better electrolytic cell performance for green hydrogen production.
The Design and Characterization of a Variable-Stiffness Compliant Knee Joint for Enhanced Mobility
Authors: Young Vanessa , Talley Connor , Schwenck Lucas , Lozano Raul , Sawicki Gregory , Tekes Ayse 
Published: January 2025

Abstract
The knee joint, serving as a critical nexus in human locomotion, presents a unique set of challenges and opportunities for enhancing mobility through assistive technologies. Assistance with knee function is frequently required due to conditions like arthritis, injuries, or age-related changes, affecting one’s ability to walk and carry out daily tasks. Compliant mechanisms integrate flexible members that go under large deformation when loaded which is very similar to the nature as opposed to relative motion generation between adjunct links as in rigid mechanisms. Their ability to be manufactured in a single piece, coupled with enhanced performance due to the absence of friction loss, positions them as an excellent choice for a range of applications, particularly in the development of robotic and biomedical devices. In this study, we designed a compliant knee joint featuring variable stiffness, characterized by initially pre-buckled beams, to mimic the nonlinear stiffness behavior seen in the human gait cycle. We designed various combinations of pre-buckled beams having different geometries to analyze the torque and angular deformation as well as force and displacement relations. The knee joints are built as a single piece using a dual extruder 3D printer.
Substract Surface Quality Influence on LP-DED Process: Exploring Effects on Bead Characteristics and Microstructure
Authors: Grass Nuñez Johan , Pereira Juan I. , Rodrigues Alessandro , Coelho Reginaldo T. 
Published: January 2025

Abstract
Laser Powder – Directed Energy Deposition (LP-DED) is an Additive Manufacturing (AM) technology that uses metal powder to create structures and repair worn components. Many published works have explored the operational parameters, such as laser power (P), mass flow rate (ṁ) and travel speed (Vf), relating them with the resultant mechanical properties. Increasingly, studies have extended into material selection and modifications of it. The LP-DED process can have some notable applications in mold repair using AISI H13 steel and previous investigations have assessed heat treatments and microstructural characteristics when building and post-processing those repairs. However, the aspect of different substrate surface conditions remains unexplored. This research examines the effects of surface conditions on deposition outcomes using AISI H13 steel for substrate and deposition material. Substrate surface machined, sand blasted and polished before deposition. SEM was used to analyze the cross-section and determine how surface quality influences the bead integrity. The results reveal that as machined surfaces show a dilution variability of 28% on the same bead, while as blasted surface provide more stable bead areas. Additionally, the Heat-Affected Zone (HAZ) in the polished surface deposition was 18% smaller than observed in as machined surface. The study further includes evaluations of depositions area, dilution percentages, and micro-hardness, offering insights into the optimal substrate conditions for L-DED applications.
Development of Open Multi-Processing Based Parallel Multiphysics Topology Optimization Software for DC Bus Bar
Authors: Sinha Hitesh Kumar , Singh Krishna Mohan 
Published: January 2025

Abstract
This paper deals with developing a parallel multiphysics topology optimization methodology for a DC bus bar. The developed algorithms have been implemented in an object-oriented C+ + program. While testing the effect of grid size on the optimal area, the optimal area converges for a medium grid size. However, the decrease in the grid size prevents the checkerboard pattern and grayscale region (the area that contains solid and void regions) and shows the finer distribution of optimized material. For finer grids, computational time increases. From the time analysis of serial code, we observe that multiphysics finite element analysis (FEA), optimization algorithm method of moving asymptotes (MMA), and filter process take up significant computation time. In the present work, we aim to reduce the computational time and efficiently utilize CPU resources using parallel computing. We have created a parallel program using an application programming interface (API) based on the OpenMP multithread parallel tool. We tested our parallel program with different grid sizes for a sample test case. The result shows that speed-up increases with an increase in threads. However, after eight threads, speed up, and parallel efficiency decreases for coarser grid size.
Computational Fluid Dynamics (CFD) Modeling of Flow Boiling Heat Transfer in Microchannels: Modeling Approaches Using ANSYS Fluent and Validation Studies
Authors: Viyyuri Sreenivas 
Published: January 2025

Abstract
Microchannel heat exchangers have become increasingly important for dissipating high heat fluxes in various applications, including electronics cooling, power generation, and refrigeration systems. Among the heat transfer mechanisms in microchannels, flow boiling offers significant advantages due to its efficient heat removal capabilities and compact design. Computational Fluid Dynamics (CFD) plays a crucial role in understanding and optimizing flow boiling heat transfer in microchannels. This paper reviews two modeling approaches, one employing the Volume of Fluid (VOF) method and the other utilizing a semi-mechanistic boiling model under the Eulerian multiphase framework, implemented in ANSYS Fluent. While the VOF method does not rely on empirical models, it may entail long simulation times, whereas the semi-mechanistic boiling model allows for quicker turnaround. Two validation studies are presented to assess the accuracy of the CFD models implemented in ANSYS Fluent. The first validation study involves water as the working fluid, mimicking typical coolant applications in electronics cooling. The second validation study focuses on R134a as the working fluid, representing refrigerant applications in microchannel heat exchangers. These validation studies demonstrate the utility of CFD simulations using ANSYS Fluent in elucidating heat transfer mechanisms and optimizing microchannel geometries for diverse applications.
Design and Validation of an Origami Paper-Based Gripper
Authors: Nunez-Quispe Johan J. , Liu Fan , Liu Vanessa , Gonzales-Martell Adriel A. , Ming Isaac , Xu Rong-Guang , Jiang Litong , Xiu Haning , Chen Zi 
Published: January 2025

Abstract
The present research introduces an innovative, cost-effective approach to robotic gripper design, utilizing origami-based engineering to overcome the complexities and high costs associated with conventional grippers. We developed a lightweight origami gripper that transitions from a square to a rhombus shape through simple manual folding. This type of device has certain features depending on how folding lines are placed, design parameters, folding orientation, and material thickness. This transformation enables efficient grasping and gap bridging, a significant advancement over the limited capabilities of traditional paper-based origami grippers. The proposed gripper was tested in two different materials, namely, cardstock paper and plastic sheet, both of which were able to hold most common objects properly such as a stuffed toy, phone stand, bottle cap, wine cork, magic cube, and similar-sized items. However, in terms of lifted weight, the plastic gripper performed better during the lifting tests which were performed using a bottle of water, a scale, and identifying the maximum weight it can hold. A key aspect of our research is the geometric analysis of the gripper’s transformation, focusing on how the control angle influences the gripper’s final shape and functionality. An analytical model was developed to figure out how manual folding, specifically the angular displacement input, moves each folding line in the origami and establishes three types of folding lines in the presented origami which are valley, mountain, and contour lines. After this process, some equations were calculated to characterize this motion working with three major angle parameters alpha, beta, and gamma. The present research validates the proposed origami gripper in three major aspects; analytical modeling using angular parametric equations, computational simulation performed using Rigid Body Dynamics (RBD) as well as the lifting weight experiment to evaluate its limitations. The experimental test confirms what the theoretical analysis and computational simulation outlined regarding the predicted motion and maximum angle it can reach to close the gap. Moreover, the simulation showed and quantified critical stress zones across the origami, which are the side internal corners for the lifting experiment modeled as a sliding force. Through this investigation, we encapsulated the interplay between these parameters mathematically, enhancing our understanding of the gripper’s mechanics. Our theoretical and simulation results closely align with mathematical predictions, confirming the practicality of this design. The proposed origami gripper, with potential enhancements and an appropriate actuation mechanism, presents a promising solution for efficient, lightweight robotic grippers in various applications.
Low-Speed Impact Performance of Surface-Based Aluminum Lattices
Authors: Yassine Mohamad , Almaskari Fahad , Zaki Wael 
Published: January 2025

Abstract
This paper presents the low-speed penetration impact testing experimental study performed on triply periodic minimal surface sheet-based structures. It is concerned specifically with the dynamic response of additively manufactured AlSi10Mg alloy samples towards a moderate strain rate impact loading. The low-speed impact response and penetration resistance can be crucial in the assessment of protective gear and aerospace structural components performance. The laser bed powder fusion (LBPF) additive manufacturing technique was employed to fabricate the samples into 40 × 40 × 20 mm3 specimens, and the localized penetration testing was performed on the samples through the usage of a weighted drop impact tower. This analysis compares and assesses the penetration resistance of three different TPMS topologies, namely, the gyroid, diamond and IWP, having a constant relative density of 28%. The penetration performance of these topologies was evaluated based on the peak force and maximum penetration depth results obtained from the impact penetration testing. The results acquired confirmed the full perforation of the IWP structure under the supplied kinetic energy of the impactor, while the gyroids and diamond specimen were almost entirely penetrated. The peak force values were marginally greater in the diamond structures than the other two topologies.
Multi-Class Entity Classification on Rasterized Engineering Drawings Using Graph Learning
Authors: Joseph Joe , Regmi Amit , Burak Kara Levent , Shimada Kenji 
Published: January 2025

Abstract
In design and manufacturing engineering, the manual interpretation of 2D technical drawings remains a significant bottleneck, impeding efficiency in tasks such as part quotation and process manufacturing. While computer vision advancements have shown promise in interpreting natural images, the translation of engineering drawings into actionable data poses unique challenges. Our research proposes a novel data-driven framework that leverages computational vision and graph learning to automate the vectorization and low-level interpretation of raster engineering drawings, bringing us closer to a CAD format. Our approach involves the vectorization of rasterized engineering drawings into a collection of lines and arcs. We construct a connectivity graph from these lines and arcs and train a graph convolutional neural network to classify entity types accurately. We concentrate on 9 distinct classes: Lines, Arrowheads, Text, Dimension Lines, Center Lines, Arcs, Hidden Lines and Contours, and Misc. We achieved a notably high performance of 98.2% validation accuracy, a 20.66% improvement compared to prior methodologies, and a maximum of 46.48% increase compared to baseline learning methods. By streamlining the interpretation of technical drawings, our framework offers a pathway to enhance efficiency in modern manufacturing processes, reducing reliance on manual labor and boosting overall productivity.