bluuebunny/crossref_metadata_2025 huggingface dataset:
Unveiling the Voltage-Current Characteristics of Dielectric Barrier Discharge Plasma in Interaction With Water Droplets at Varied Temperatures
Authors: Sarwar MD Sohaib Bin , Ahumada Lazo Jorge , Lelikov Petr , Liu Yang
Published: January 2025

Plasma-droplet interaction is a widely observed phenomenon in various applications, such as environmental cleanup, additive manufacturing, aviation, and fusion energy. The characteristics of plasma discharge can be greatly altered during these complex multiphase interactions. In this paper, we aim to investigate the voltage-current (V-I) properties of dielectric barrier discharge (DBD) plasma actuator when interacting with a droplet impacting on the plasma surface. Specifically, this study explores the complex interactions between DBD plasma and water droplets at ambient temperature and in a 0°C temperature condition. The V-I profiles of the DBD plasma actuator are recorded throughout the entire process of droplet-plasma interaction, i.e., before, during, and after droplet impact. Lissajous graphs are also plotted to reveal the distinct discharge profiles of the DBD plasma actuator during droplet-plasma interactions. Our findings show that the impact of water droplets led to an increase in current magnitude while voltage magnitude remained the same. The results highlight that the interactions between liquid droplets and DBD plasma bring changes in the current amplitude before and after impact, and this fluctuation in current amplitude becomes more evident at higher input voltages. The Lissajous figures and time-dependent current profiles revealed that the plasma’s reaction to water droplets is significantly different depending on the temperature of the droplets. Specifically, droplets at room temperature resulted in more noticeable alterations in the behavior of the plasma discharge.
Creating Equitable Spaces: Harnessing Dynamic Modeling for Gender Balance in Engineering Education
Authors: Gonzalez-Mendivil Jorge A. , Rodriguez-Paz Miguel X. , Zamora-Hernandez Israel , Caballero-Montes Eduardo
Published: January 2025

Gender equality in engineering is crucial for several reasons: The equal participation of men and women in engineering ensures a greater diversity of perspectives, ideas, and approaches. This enriches the design, development, and problem-solving process by considering a variety of experiences and knowledge. Gender diversity in engineering encourages innovation and creativity, as different points of view can lead to more complete and effective solutions. The lack of gender diversity in engineering can lead to the creation of products and services that do not meet the needs of all people. By having an equal representation of men and women in the field of engineering, more inclusive and appropriate solutions can be designed for all users, regardless of gender. This applies to fields like technology, infrastructure, healthcare, energy, and many others.

This paper aims to explore the use of dynamic modeling as a pathway to achieving gender equality in engineering education. By addressing the gaps in current approaches, it seeks to provide insights and guidance for educational institutions, policymakers, and stakeholders to effectively advance gender equality and create a more inclusive and diverse engineering education ecosystem. A dynamic model that describes the enrollment of women in an engineering academic program can have several uses and applications. This would provide an understanding of historical trends and patterns and allow estimates of the number of women who will enroll in a specific program in the future. Early results of the model demonstrate that women have a very different scheme in making decisions about the academic program to choose. While the male gender decides more based on what they know can be successful in the future, the female gender goes further and takes into account aspects such as the sense of fulfillment and belonging. The model also shows that if, during the student recruitment stage, activities designed specifically for what women are most attracted to in engineering were used, more women would enroll in said academic programs.
Design of a Novel Passenger Car Axle Housing Mount Using Alternative Geometry and Material
Authors: Burns Jeremy , Kocharian Tikran
Published: January 2025

The axle housing, typically manufactured from robust metal alloys like cast iron, encloses essential rear axle components in a vehicle and connects to the suspension system, facilitating independent wheel movement while supporting power transmission, weight, stability, and shock absorption. Reverse engineering, a valuable technique in industry, involves understanding existing product functionality through deductive analysis, enabling documentation, replication, enhancement, or adaptation. In the automotive aftermarket, there is significant potential for enhancing products to replace current vehicle components, often necessitating modifications and integration of new hardware. Reverse engineering preserves crucial design specifications while tailoring products for alternative applications. This study focused on reverse engineering and redesigning a passenger car axle housing mount, utilizing practices such as 3-D scanning, hybrid modeling, and 3-D printing. The primary objective was to evaluate the modified axle assembly’s durability under increased loads while minimizing weight gain, utilizing Finite Element Analysis (FEA) to simulate real-life conditions. The innovative mount design successfully met functional requirements, with simulations showing acceptable stress levels. A prototype was fabricated and installed on an existing axle housing, resulting in a positive fit without adjustments. This research offers a versatile solution for passenger cars, promising improved performance and efficiency across models, with an adaptable design poised to expand automotive technology.
Advancements and Challenges in Soft Tissue Engineering: A Comprehensive Review of Additive Manufacturing Technologies
Authors: Azari Fatemeh , Jazaei Robabeh
Published: January 2025

In contemporary biomedical innovation, Additive Manufacturing (AM) has been pivotal since the 1980s, crafting complex structures for bioengineering, biomedicine, and pharmacology [1]. Particularly in pharmacology, AM, notably through Stereolithography (SLA), has transformed drug delivery, enhanced dosage precision and advancing personalized therapies [2]. In tissue engineering, AM is crucial for bioprinting scaffolds and tissue constructs using bio-inks to mimic human tissues, facilitating significant advances in medical treatments [3]. (i) Key techniques include SLA achieving 30-micrometer resolution [4–6], Fused Deposition Modeling (FDM) is an evolving drug delivery technique [9], and Powder Bed Fusion (PBF) 70% to 90% had minimal impact on osteogenesis when using a 700-μm pore size [41]. (ii) Applications span tissue engineering with materials like PLA and PGA [16], achieving 97.2 ± 48.4 implant fit accuracy using MRI and CT scans [40–41]. Drug delivery systems, such as multi-layered tablets, enable sequential release [22]. Personalized medicine benefits from custom-made implants based on patient data [25].

(iii) Challenges include replicating intricate organ structures like the bladder and liver, ensuring vascularization, and integrating immune components [28]. Incorporating vascular endothelial growth factor (VEGF) into bioinks increases angiogenesis [31]. Material limitations and regulatory hurdles persist [34]. (iv) Future directions focus on improving vascularization, immune integration, and personalized oncological treatments [37]. Continued research and technological innovations are essential for accurate tissue replication [40]. (v) In conclusion, AM techniques like SLA and FDM revolutionize drug delivery and tissue bioprinting, marking a transformative period in biomedicine with significant potential for personalized medicine and regenerative therapies, promising breakthroughs in disease modeling and drug testing[41].
Accelerating Microstructure and Melt Pool Prediction in Laser Powder Bed Fusion Processes Using Data Driven Neural Network
Authors: Qadeer Abdul , Akhtar S. Sohail , Abubakar Abba A. , Arif Abul Fazal M. , Mekid Samir , Al-Athel Khaled S.
Published: January 2025

Laser powder bed fusion (LPBF) is a pivotal additive manufacturing process characterized by selective melting and solidification of powder layers to form complex parts. The microstructure of the final part plays a crucial role in determining its properties, which are heavily influenced by process parameters such as laser power, scanning speed, powder layer thickness, and material properties. Understanding the influence of process parameters on melt pool geometry is crucial for minimizing defects due to unmelted powder regions and optimizing hatch spacing. Additionally, the microstructure plays a vital role, as process parameters can lead to the formation of columnar grains, promoting anisotropy in the fabricated parts. Therefore, understanding microstructural characteristics is key to ensuring the reliability of manufactured parts. Predicting the microstructure and melt pool geometry in laser powder bed fusion (LPBF) processes via finite element modeling is computationally demanding and time-intensive. To mitigate these challenges, we propose the development of a machine learning model capable of accurately predicting melt pool geometry and microstructure based on process parameter variation, thereby reducing time in the identification of optimal process parameters for further experimental investigation. This study utilizes a physics-based numerical model within ANSYS Additive to predict melt pool geometry and microstructure in the LPBF process. The model’s accuracy was validated using single-track metal additive manufacturing (AM) experiments extracted from relevant literature. A comprehensive database was compiled by numerical simulation, including melt pool geometry, microstructure, process parameters, and material IDs. This dataset was then employed to train a data-driven model, with process parameters and material IDs serving as inputs and melt pool width, depth, and microstructure images as outputs. Additionally, the images were resized to 100 × 200 pixels for computational efficiency. Various neural network architectures, including Multilayer Perceptron (MLP) and Convolutional Neural Networks (CNN), were utilized to enhance the predictive capabilities of the model. These architectures were trained and fine-tuned to achieve precise predictions that closely matched the actual dataset. The study showcased the efficiency of neural networks in accurately predicting melt pool geometry and microstructure, significantly reducing prediction times compared to conventional numerical simulations by several orders of magnitude. Future research direction will focus on incorporating physics constraints into the loss function to accelerate the training process, thereby reducing the number of epochs required. However, the development of a rapid data-driven model with minimal mean absolute error is vital for enhancing part optimization and promoting the widespread adoption of MAM.
Characterization of Droplet Dynamics Under Impact of a Shockwave Interacting With a Solid Surface
Authors: Giacoma Jordan , Ahumada Lazo Jorge , Zhang Haipeng , Liu Yang
Published: January 2025

This paper presents an experimental study on the transient deformation and breakup morphology of a water droplet as it approaches a solid surface driven by an interaction with a shockwave. A droplet injection system integrated into the top of the Shock Tube Test Section produces consistently sized water droplets of a prescribed diameter. A super-sonic shockwave is then shot at the falling water droplet. A high-speed imaging system, consisting of two high-speed cameras, a high-intensity LED point light source, and a series of optics, is used to visualize the complex shock-droplet-surface interaction. One of the high-speed cameras witnesses the impact through Schlieren conditions, to clearly visualize the large alterations of the density field generated by the shock wave. The other receives a collimated light beam through a cubic beam splitter, more clearly able to visualize the breakup morphology of the droplet. After the shockwave impacts the droplet, it encounters a metal plate and rebounds and impacts the falling water droplet a second time, causing significant shockwave propagation within the droplet. It is found that the duration of time between impacts due to the droplets initial distance from the plate, influences the duration of time under which the droplet deforms before failing.
A Framework for Surface Scanning to Additive Repairing Using Point Cloud Data and Surface Reconstruction Algorithms
Authors: Ahmed Radif Uddin , Nabi Iftesam , Alam Chowdhury Sadid , Chen Yun , Rahman M. Shafiqur
Published: January 2025

This study represents an additive repairing method for solid bodies by accurately scanning and reconstructing three-dimensional (3D) surfaces inspired by the additive manufacturing (AM) technique. The proposed methodology utilizes a laser profiler (Keyence LJX 8200) to scan an arbitrary surface and obtain point cloud data (PCD). The laser profiler functions as a line scan device that can provide 3200 data points for a single two-dimensional XZ plane. To extend the scanning capabilities beyond a single line, a computer-controlled gantry system driven by a stepper motor is integrated to offer significant information on the Y-axis. The scanned PCD is then evaluated for quality consistency by implementing various preprocessing techniques, including outlier elimination and data sorting using Python. The preprocessed data is exported into the final PCD file, which contains crucial information about the vertices of the scanned surface in terms of coordinates; however, it does not provide information about the faces of the actual surface. To prepare the actual surfaces effectively from the vertices and normal to the faces, several surface reconstruction techniques, including Delaunay Triangulation, Ball Pivoting Algorithm, and Poisson’s Surface Reconstruction, are applied to the point cloud dataset. Results show that Poisson’s Surface Reconstruction consistently produces the highest level of accuracy among the tested algorithms, making it the optimal choice for surface reconstruction in additive repair applications. The 3D surface produced through this process is exported as a Standard Tessellation Language (STL) file, which can be printed via any 3D printer. The precision of the surface reconstruction algorithm is experimentally evaluated by comparing the dimensions of the scanned item with that of the 3D-printed item.
Enhancing Energy Absorption Capacity of Thin-Walled Box Structures Through the Integration of Auxetic Materials
Authors: Narayana Peddi Sai Rama , Prakash Raghu V. , Gunti Srinivas , Kanugula Raghu
Published: January 2025

In frontal impacts, the usage of thin-walled box structures in the front-end of vehicles is crucial for effectively absorbing collision energy. However, achieving maximum energy absorption while considering constraints such as available space, weight, cost, and maintaining occupant safety is a challenging task. Previous studies have indicated that the structural shape, utilization of high strength materials, and incorporating foam-filled structures can significantly enhance energy absorption capacity. This paper presents a novel approach to optimize the energy absorption capacity with a combination of conventional and auxetic materials. Auxetic materials possess a unique characteristic of exhibiting a negative Poisson’s ratio, which means they contract when compressed and expand when subjected to tension. These distinctive properties enable auxetic structures to achieve higher stiffness and improved impact resistance while remaining lightweight.

In this paper, a Finite Element Analysis (FEA) methodology has been formulated with a simple crush box, and with multiple materials combinations of conventional materials such as high strength steels, Aluminum and auxetic materials, to improve the energy absorption capacity. The significant factors affecting the energy absorption which are critical to occupant safety have been compared between the simulations. It is demonstrated that the optimal blend of conventional materials such as Aluminum and Ultra High Strength Steels, when integrated with auxetic structures, exhibits a higher potential for maximizing the specific energy absorption capacity of these structures. The findings reveal that the optimized combination of the above-mentioned materials can increase the specific energy absorption (SEA) capacity by up to 256%. Additionally, the practical application of these findings is demonstrated through full vehicle level crash analyses aimed at achieving light weighting. The research findings were implemented on the Ford Taurus FE model, available online and validated experimentally that shows benefits of 7.83kg weight reduction for the same crash performance of a standard material taken as baseline.
Towards Understanding the Relationships Between Personal Motivation and Design Quality
Authors: Blackwell Spencer , Kado Corey , Bohm Matt , Kames Elisabeth
Published: January 2025

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

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

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

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

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

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

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

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

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

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

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

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

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

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.
The Development of a Risk Oriented State-of-Charge Estimation Algorithm for a Heavy-Duty FCEV Truck
Authors: Alsharif Khaled I. , Michelotti Mark
Published: January 2025

Providing an accurate estimate of a battery’s State-of-charge (SOC) is an important function of a Battery Management System (BMS). The SOC must be maintained within an appropriate range to ensure the overall safety and efficiency of the battery system. In particular, the SOC estimate is often used to avoid extreme regions of operation in which dangerous over- and under-voltage conditions are possible.

The current state-of-the-art techniques for estimating SOC are nonlinear Kalman-like observers, which combine current integration (i.e. “coulomb counting”) with measurements of battery cell terminal voltage. These methods typically aim to minimize the Mean Squared Error (MSE) or some similar measure. These methods were found to be inadequate in certain scenarios in a vehicle application — specifically, a heavy-duty Fuel Cell Electric Vehicle (FCEV) using Lithium Iron Phosphate (LFP) cells. A relatively small estimate error could lead to significantly adverse outcomes at the vehicle level.

This paper develops an alternate approach more appropriate for the application. Instead of minimizing the MSE, the strategy aims to avoid worst-case scenarios. Using established risk analysis methods (e.g. Failure Modes and Effects Analysis), undesirable scenarios are identified and prioritized. The SOC estimation algorithm is designed with the explicit aim of avoiding these scenarios.
Wavefield Analysis Enabled Multi-Directional Mechanical Properties Characterization for 3D Printed Composites
Authors: Cai Bowen , Watts Hunter , Sun Chuangchuang , Huberty Wayne , Tian Zhenhua
Published: January 2025

Thermoset composites, utilized in additive manufacturing, are distinguished by their excellent thermal and mechanical properties, enabling them to maintain structural integrity even under high-temperature conditions. An accurate method for characterizing the mechanical properties is necessary to ensure the performance parameters, reliability, and safety of materials during and post-manufacturing. However, characterizing 3D-printed thermoset composites is challenging due to the anisotropy introduced by the additive manufacturing process and factors such as delamination and porosity. This also leads to difficulties in accurately characterizing composites with traditional testing methods. To address this, this paper introduces a novel method that combines a non-destructive Piezoelectric transducer-laser Doppler Vibrometer (PZT-LDV) guided wave sensing system with an optimization algorithm-enhanced wavenumber analysis technique. A series of experiments were conducted to validate the concept of measuring the mechanical properties of a 3D-printed thermoset material panel. Our method successfully determined two material properties — shear wave speed and Poisson’s ratio in multiple directions on the test panel. This study aims to establish a precise and rapid non-destructive testing method that can effectively characterize various composite materials and monitor their performance throughout the additive manufacturing process.
An Experimental Study of Weight-Based Compensation for Asymmetric Upper Limb Growth Secondary to Pre-Pubertal Operative Humeral Epiphyseal Plate Damage
Authors: Fernandez Dane , Fernandez Ryan , Jawad Badih , Fernandez Vernon
Published: January 2025

This research was conducted to verify previously-developed equations analyzing musculoskeletal strain caused by uneven weight distribution inherent in a case of upper limb length discrepancy. The issue is an imbalance due to the growth of a shorter humerus in the individual’s right upper limb (RUL) as the result of a prior surgery affecting the individual’s right humeral growth plate. This shortened RUL weighs less than the left upper limb (LUL), lowering the balancing moment on the torso. When the individual sprints during physical exercise, there is an imbalance in rotational momentum created between the two arms in the sagittal plane. This imbalance in momentum compensation by one of the individual’s lower limbs. To solve this biomechanical problem, kinematic equations were developed to model the motion of a sprinter. In particular, the model defines the influence of the opposing upper limb motion on the lower limb motion that results in forward motion while sprinting. Furthermore, the equations incorporate the rotational motions of the upper and lower limbs. Trials were conducted with a test subject having a shorter RUL than LUL. In the trials, the subject initially started from rest before accelerating to a constant speed. Two Kistler force plates were used to generate force vectors of each of the subject’s feet once the constant speed was reached. Kistler force plates contain piezoelectric sensors which can be used to record ground force reaction. In this case, the Kistler force plates measured the horizontal, transverse, and longitudinal force vectors created when the subject’s foot contacted the plate. Additionally, the subject had several reflectors placed along his body. Using the Vicon three-dimensional (3D) optical motion analysis system, ten cameras in the laboratory captured the movement of the reflectors, generating vector motion for each limb during each trial run. To balance the rotational momentum of the upper limbs, a counteracting weight was attached to the wrist of the RUL in an attempt to minimize the effects on lower limb musculature. The difference in weight between each arm was determined, allowing a wearable device to be created which accounted for the difference. The device was attached to the subject’s right wrist to balance the weight of both arms. Hence, the left lower limb would not have to overcompensate for the shorter RUL’s lack of momentum. Similar running trials were conducted with the counterweight adjustment. The data from the trials, both with and without the counterweight, were compared to the theoretical equations determined from previous research.
Multi-Inlet Hydraulic Ram Pump System
Authors: Sharma Ashokkumar M. , Dady Trevor , Alam Mohammad Ashikul , Pidugu Srikanth B.
Published: January 2025

Water is required for the survival of human beings. With billions affected by water scarcity globally, there is a pressing demand for innovative technologies in water transportation. A potential solution to the issue of water scarcity is to adopt the technology of the multi-inlet hydraulic ram pump system, which is a mechanical device that utilizes the energy from a water source flowing downhill to pump a portion of that water to a higher elevation. It operates utilizing the property of fluid hammering and omits the necessity of any electrical or other power sources. This system could provide an efficient, simple, self-sustaining, and more frugal option to combat water scarcity. In this study, a multi-inlet hydraulic ram pump was built and evaluated to understand the flow dynamics and performance of the multi-inlet hydraulic ram pump. Additionally, a solar-based water handling unit was introduced to recirculate waste water back to the source tank. Results showed an improvement upon its predecessor. From the studied four inlet drive pipe configurations, the 2-drive pipe ram pump configuration demonstrated an overall better performance in terms of pump volumetric efficiency of 9.2%, energy efficiency of 15.6%, and water delivery capacity of near about 1.25 m3/day.