bluuebunny/crossref_metadata_2025 huggingface dataset:
Automatic Tool Alignment of an Eye-in-Hand Manipulator for Overhead Line Insulation Cleaning
Authors: Vohra Mohit , Panda Amruta , Subramaniam Prashanth , Althani Thani , Rezk Mahmoud
Published: January 2025

The maintenance of electrical insulators is critical for ensuring the reliability and safety of power transmission networks. Traditional manual cleaning methods, while effective, pose safety risks to workers and require considerable time, compromising operational efficiency. Addressing these challenges, this paper introduces a dual-mode, eye-in-hand depth camera-based robotic system tailored for the cleaning of insulators. The system leverages a combination of automatic and teleoperated modes to optimize both safety and cleaning efficacy. In the automatic mode, the system uses advanced visual servoing techniques to detect and perform coarse alignment of the robotic end-effector with the target insulator. Following this initial alignment, control is seamlessly transitioned to the teleoperated mode, enabling precise manipulation of the cleaning tools by an operator from a safe distance. This dual-mode approach merges the reliability and speed of automated technologies with the precision and flexibility of manual control. The paper provides an in-depth analysis of the system’s architecture, details the integration of its control modes, and describes the operational procedures involved. Experimental results are presented to demonstrate the effectiveness of the entire system.
The Effect of Meniscal Tears on Knee Biomechanics: a Finite Element Analysis Investigation
Authors: Ghidotti Anna , Penzo Michela , Landi Daniele , Regazzoni Daniele , Rizzi Caterina
Published: January 2025

Meniscal tears may adversely impair knee function and biomechanics, with effects varying based on tear type and location. There are several methods for studying meniscal injuries, including human cadavers, and computational models, such as Finite Element Analysis (FEA). Previous studies have used the FEA technique to examine the effects of artificially replicating meniscal tears. The aim of this study is to investigate the effect of meniscal injury on knee biomechanics, by means of FEA, starting with real meniscal tears.

Based on Magnetic Resonance (MR) images of 12 patients, 3D models of the distal femur, proximal tibia, cartilage, and menisci were obtained for each patient through the segmentation process. A preprocessing phase was then necessary to prepare the models for FEA. Finally, simulations were conducted to study the effects of lesions on stress, contact pressure, and meniscal extrusion.

The simulation results showed that meniscal lesions, especially radial tears, increase the pressure between menisci and tibial cartilage, increase the maximum shear stress on menisci, and lead to meniscal extrusion. The integration of FEA with clinical data may provide surgeons with detailed insight, enabling them to decide whether surgical intervention is necessary.
Unsupervised Classification of Perovskite Crystal Structures From XRD Data
Authors: Mathew Ansu , A. B. Baloch Ahmer , Mohammed Yakasai Alamin , Mittal Hemant , Alberts Vivian , Vandavasi Karunamurthy Jayakumar
Published: January 2025

Perovskite materials hold immense promise for applications in photovoltaics, optoelectronics, and photonics. However, accurately characterizing their crystal structures, which directly influence these properties, remains challenging. Traditionally, analyzing these structures from X-ray diffraction (XRD) data relies on labor-intensive steps like crystallographic analysis and phase identification. Hence, this work explores the potential of unsupervised machine learning (ML) for automated and efficient classification of perovskite compounds. By applying unsupervised ML algorithms to raw XRD data, we directly classify crystal structures, bypassing the need for complex traditional procedures. PyMatgen was employed to extract data for 4008 perovskite compounds from the Materials Project repository, followed by initial preprocessing to prepare it for machine learning models. We identified information underlying XRD data using PCA before K-means clustering (Approach 1) and K-means clustering before PCA (Approach 2), which are some typical approaches in machine learning for data analysis and clustering. Elbow method was used to determine the optimal number of crystal structure clusters for perovskites, which was 7 for both approaches. We then employed the silhouette score, Davies-Bouldin Score, and Calinski-Harabasz score to assess our clustering methodology’s effectiveness and identified that approach 1 depicts better quality in clustering. We mapped the clusters to crystal structure classes and evaluated the classification metrics. Both approaches generate similar classification metrics, indicating that the underlying structure of the data is exceptionally robust and can be captured effectively using either method.
An Exploration of 3D Printing’s Contribution to Responsible Consumption and Production (SDG 12)
Authors: Hasaballah Maryam , Pervaiz Salman
Published: January 2025

In 2015, the United Nations (UN) General Assembly established seventeen different sustainable development goals (SDGs) that outlined principles and objectives which tackled challenges of diverse nature around the world and achieve the set-out targets by 2030. On the other hand, 3D printing has been attributed as a game changer for the manufacturing sector. It is due to the disruptive nature of this technology where conventional design to manufacture principles are not strictly applied. It has been observed that inherent nature of 3D printing can positively contribute towards achieving these global challenges. The 3D printing contributes towards achieving multiple goals from the SDGs in many interactions.

This article examines the role of 3D printing technology in contributing to Sustainable Development Goal 12 (SDG 12) — responsible consumption and production. Through a comprehensive literature review and empirical analysis, the study explores various aspects of 3D printing’s impact on sustainability, including resource efficiency, waste reduction, circular economy practices, localized production, customization, longevity, and energy efficiency. Key findings suggest that 3D printing offers significant potential for improving sustainability across industries such as construction, packaging, supply chain management, healthcare, and manufacturing. The synthesis of findings reveals a high impact relationship between 3D printing and SDG 12, with measurable effects on efficiency improvements, waste reduction, and environmental conservation. Challenges and considerations for future research are also discussed, highlighting the need for long-term environmental impact assessments, integration of social sustainability factors, regulatory frameworks, technological advancements, and economic accessibility. By addressing these research areas, the article highlights the potential of 3D printing to contribute to sustainable development goals and foster a more stable future.
Empowering Mobile, Energy-Efficient and Sustainable 3D Printing Systems for Emergencies and Rural Communities
Authors: Linke Barbara , Bashayan Kholoud , Ramírez-Cedillo Erick , Armendariz-Rodriguez Maximiliano , Habibi Mohsen , Vargas Martínez Adriana , Ramírez Ricardo A.
Published: January 2025

Natural disasters and emergencies create pain, diseases, and death. Resources at the affected sites might be sparse, yet the availability of simple parts can be crucial to ease suffering and restore critical infrastructure. Additive manufacturing, also commonly called 3D printing, can provide a fast and flexible way to produce items locally and help provide customized components in rural areas, support Do-It-Yourself users, and address product shortages.

This study therefore addresses an urgent and important need in today’s world by designing mobile, energy-efficient, and sustainable 3D printing systems that can be used in emergencies and in rural communities to print essential medical and mechanical parts. To the best of our knowledge, prior literature on mobile 3D printing systems does not consider critical conditions imposed by solar radiation availability based on both, location and season, days of autonomy, and how the combination of these critical factors affects the sizing and hence mobility of the system. In this study, we aim to bridge this knowledge gap by a literature study and synthesize relevant information into graphs and a step-by-step guide with equations, to design a solar-powered 3D printing system from commercially available solutions. This paper shows a first approach for setting up such a system that can work under critical conditions while still being reasonably sized and walks through a case study.
Assessing Hemodynamic Changes in Cerebral Aneurysms Post Coil Embolization: A Preliminary Investigation
Authors: Monfared Mohammadali , Hollingsworth Luke , Thibbotuwawa Gamage Peshala , Taebi Amirtahà
Published: January 2025

A cerebral aneurysm is a weakened area in the wall of a blood vessel within the brain that causes the vessel to bulge outwards. The choice of treatment depends on various factors including the size and location of the aneurysm and the risk of rupture. The noninvasive methods are watchful waiting and medication. The invasive methods include surgical clipping, flow diversion, and endovascular embolization. Treatment for aneurysms should be tailored to individual situations rather than following a universal approach due to their variability among individuals. This approach provides the possibility of choosing precise treatment plans that are proper for each patient’s circumstances. The utilization of computational fluid dynamics (CFD) allows for the prediction of after-surgery hemodynamic changes before any surgical approach. The goal of this research was to investigate the flow characteristics and hemodynamic parameters in cerebral arteries before and after endovascular embolization treatments using CFD in three patient-specific geometries with at least one aneurysm at the middle cerebral artery’s bifurcation. To model the coiling, the computational domain was divided into two fluid domains, a general fluid domain consisting of the parent arteries and a porous domain inside the aneurysm. CFD modeling was conducted to simulate blood flow in pre- and post-coiling scenarios. Results showed that the coiling model with different porosity values led to a redirection of blood flow away from the aneurysm sac. Additionally, changes in shear stress indicated potential alterations in susceptibility to vascular remodeling. In conclusion, these findings indicated that utilizing CFD modeling for simulating blood flow in patient-specific cerebral arteries could be utilized to predict hemodynamic parameters before any treatment.
Finite Element Modeling of Ballistic Impact for Steel Plates Using Two Constitutive Models
Authors: Johnson Daniel , Peterson Luke , Sharma Gehendra , Mun Sungkwang , Hammi Youssef , Stone Tonya , Sherburn Jesse , Rios-Estremera Daniel
Published: January 2025

A comparison of Internal State Variable (ISV) and Johnson Cook material models for Finite Element (FE) simulations of ballistic impact of A36 structural steel plates by 4340 steel Fragment Simulating Projectiles (FSP) is performed using the Lagrangian Abaqus Explicit FE solver. A novel ISV plasticity-damage material model calibration for A36 steel is presented and validated through comparison of residual velocity and perforation characteristics of simulated ballistic FSP impacted 12.7 mm plates to experiment data. The ISV and Johnson Cook models’ ability to accurately predict residual velocity and changing perforation characteristics is assessed for a range of target plate thicknesses and impact velocities. Generally, each model shows satisfactory prediction of residual velocity, however the Johnson Cook model tends to inaccurately predict target fragmentation at high impact velocities. Both the ISV and Johnson Cook models predict shear plugging near the ballistic limit, which matches experimental data. At higher impact velocities, both models show varying amounts of fragmentation of target material. Underlying differences in damage evolution during ballistic impact events (and subsequent differences in predicted perforation modes) between the two constitutive models are discussed.
Feasibility of Left Ventricular Function Assessment via Precordial Vibrations in Heart Failure Patients
Authors: Ahmed Mohammad H. , Weerasinghe Yasith , Grillo Michael , Taebi Amirtaha , Kaya Mehmet , Thibbotuwawa Gamage Peshala
Published: January 2025

Heart failure (HF) is a prevalent and potentially life-threatening condition characterized by the heart’s inability to pump blood effectively. Managing HF requires careful monitoring of left ventricular function (LVF), necessitating timely interventions to prevent adverse outcomes. Traditional methods of monitoring LVF can be invasive, costly, and limited to clinical settings which poses challenges for continuous monitoring and early detection of HF progression. This study aims to evaluate the feasibility of estimating left ventricular ejection fraction (LVEF) from precordial vibration signals. Precordial vibration data from 70 patients with varying levels of HF were analyzed. Seismocardiography (SCG) signals were processed to extract features and train machine-learning regression models. The predicted LVEF values from the models were compared with LVEF data obtained from echocardiography, the gold standard for LVF assessment. Among trained machine learning models, Gaussian process regression indicated the greatest correlation between the estimated and obtained values with the least mean squared error. Variations in the precordial vibrations were observed to correlate with the severity of valvular dysfunction, highlighting the sensitivity of this approach. In conclusion, precordial vibration signals have the potential to offer valuable insights into both myocardial and valvular function in HF patients. Precordial vibration analysis shows promise as a non-invasive, cost-effective method for continuous monitoring of LVF in HF patients. Further research and validation are warranted to explore its clinical applicability and potential integration into routine HF management protocols, with the ultimate goal of improving patient outcomes and quality of life.
Numerically Driven Geometrical Modeling and Refinement Approaches to Streamline the Design of Topology Optimized Parts: A Comparative Study
Authors: Dalpadulo Enrico , Pini Fabio , Leali Francesco
Published: January 2025

The integration of structural optimization into product design processes has been a focus of extensive research, leveraging advances in computing technology to embed numerical techniques within simulation-driven approaches. Topology optimization, particularly enabled by Additive Manufacturing, has emerged as a powerful tool for generating structurally efficient geometries. However, practical implementation poses challenges, often requiring manual redesign and interpretation of results. To address these issues, this study explores the potential of numerical optimization techniques, specifically parametric and shape optimization, to refine topology optimized structures automatically. The final aim is to develop a systematic workflow integrating these tools to produce ready-to-print geometries with minimal user intervention. The investigation, based on simple geometries resembling topology optimized structures, investigates the tools for simulation-driven design methods utilizing multi-step numerical optimization within an integrated CAD platform, aiming for a direct, efficient, and mathematically-driven design process. Advantages include faster iterations, reduced errors, and independence from user expertise, while challenges such as computational complexity, sensitivity to initial conditions, and need for specific design expertise are acknowledged. The potential further systematic approaches for broader applications, particularly in additive manufacturing and structural engineering, underscore the significance of further research in this area.
Preparing Generation Z for the Future: A Challenge-Based Learning Approach in Higher Education
Authors: Leão Celina P. , Alves Anabela C. , Silva Vinícius
Published: January 2025

In response to the growing demand for personal skills essential for success in technological careers and the imperative to address Sustainable Development Goals (SDGs), this paper presents the design and implementation of the elective course, “Preparing Generation Z: beyond technologies (Z-Tec+)”, applying challenge-based learning (CBL) methodology. Introduced in the 2021–2022 academic year, Z-Tec+ aims to equip students with skills crucial for a thoughtful transition into the job market while addressing key SDGs, including Quality Education (SDG 4), Gender Equality (SDG 5), Sustainable Cities and Communities (SDG 11), Responsible Consumption and Production (SDG 12), and Partnerships for the Goals (SDG 17). This paper outlines the rationale for adopting CBL and its alignment with the SDGs, details its integration into the course curriculum, and presents the methodology’s application in facilitating student learning and skill development in the context of sustainability challenges. The central challenge presented to students was to mitigate the “fast-fashion” phenomenon. The results, drawn from student-generated solutions over the Z-Tec+ semester, are informed by student perceptions gathered at the start of each semester, their engagement in various CBL activities, and feedback collected at the semester’s end. The study captures insights from 33 students across diverse academic disciplines, including Engineering, Economics, Marketing, Sociology, and International Relations. Each student team utilized Padlet to document their project journey and solution development process, creating a comprehensive portfolio that facilitated collaborative reflection on their collective efforts. By integrating CBL with a focus on SDGs, Z-Tec+ fosters the development of socially responsible professionals equipped to tackle complex global challenges. This study underscores the importance of fostering collaboration between academia, industry, and civil society to scale sustainable initiatives and equip students with the skills and awareness necessary to address these complex challenges effectively.
In Situ Thermal Measurement of Polymers
Authors: Powers Carson , Bratescu Simon , Bearden Peter , Dorsey Fahim , Trinh Jessica , Kim Dal Hyung , Park Jungkyu
Published: January 2025

In this research, we conducted a detailed experimental investigation into how strain affects the thermal conductivity of Ecoflex elastomer, utilizing a newly developed method for measuring thermal conductivity under mechanical strain for the first time. In situ thermal conductivity measurement apparatus was developed by combining KLA T150 nanoscale tensile tester and a custom fabricated thermal measurement sensor. The development of experimental method for measuring the thermal conductivity of nanomaterials under mechanical testing simultaneously will contribute to the development of novel materials for flexible electronics by helping us to better understand the strain effect on their thermal performance. Interestingly, the thermal conductivity of Ecoflex elastomer is shown to increase with an increase in tensile strain until the engineering strain reaches 20%. This is understood to be due to the straightened polymer chains, which makes the phonon transport to be more efficient through the stiffened polymer chains. At very high degrees of mechanical strain, the thermal conductivity may decrease due to the disruption of filler-to-filler connections and increased phonon-boundary scattering between polymer chains and magnetic powders. This is caused by the reduced spacing resulting from the increased mechanical strain. The findings from this study are expected to propel the advancement of future flexible electronics by facilitating the creation of a foundational elastomer.
Benchmarking the Efficacy of Feedback Guided Self Balance Equipment With Variable Motion Regimens for Physical Therapy and Elite Athletes
Authors: Tatoglu Akin , Pelczarski Brennen , Balaji Dhananjay , Pezzolesi Justin , Campana Claudio , Asaki Takafumi , Altuger-Genc Gonca
Published: January 2025

We designed and developed a feedback-guided balance device that can be used for patient rehabilitation, training, and athletic conditioning, including for elite athletes. This system is cross-platform, enabling users to connect via Bluetooth using any computer or a mobile device. The user interface is intentionally minimalist to facilitate ease of use. Users can independently adjust the system’s complexity and exercise types. Performance data can be accessed by professionals, such as physical therapists or coaches, who can subsequently tailor recommendations based on this data. The system can also be utilized to monitor patient recovery and ensure correct execution of exercises within prescribed repetition ranges. As exercises are modifiable based on patient age and condition, a framework is being developed to allow clinicians to easily design and implement new exercises on the system.
Markerless Pose Estimation and Wearable Sensor Technology for Enhanced Diagnosis and Monitoring of Idiopathic Toe Walking
Authors: Weerasinghe Yasith , Grillo Michael , Ahmed Mohammad H. , Sheppard Christina , Taebi Amirtaha , Wilder David , Kaya Mehmet , Thibbotuwawa Gamage Peshala
Published: January 2025

Idiopathic Toe Walking (ITW) is a common condition characterized by an individual’s preference for walking on their toes without any identifiable medical reasons. Children usually outgrow this habit; however, those who don’t are often affected by certain conditions such as cerebral palsy, muscular dystrophy, and autism spectrum disorder. If not addressed, ITW can lead to physiological complications such as skeletal and muscular deformations. Identifying and monitoring toe walking patterns is only accessible through qualitative observational gait analysis and electromyography (EMG). This approach’s main objective is to provide quantitative means to analyze ITW and diagnose and monitor patient rehabilitation. The markerless pose estimation approach analyzes the ankle’s range of motion to identify normal and abnormal gait patterns. In addition to the markerless pose estimation, a 6-axis accelerometer and gyroscope inertial measurement unit (IMU) allowed for the continuous monitoring of motion values, which constitute the identification of idiopathic toe walking. Both noninvasive approaches provided quantifiable angles, allowing for convenient and continuous ITW monitoring. By employing markerless pose estimation coupled with a wearable sensor, ITW could be monitored, enhancing the accessibility of continuous monitoring for gait irregularities.
Advancing Geometric Inspection of Non-Rigid Structures Using Mobile Robots and 3D Laser Scanners
Authors: Sattarpanah Karganroudi Sasan , Rahmanian Vahid
Published: January 2025

This paper introduces a novel approach for inspecting non-rigid structures, which are difficult to assess using traditional methods due to their flexibility and susceptibility to deformation. Our method enables comprehensive geometric inspection by integrating mobile robotics and 3D laser scanning. The system autonomously navigates around the structure, performing high-resolution scans with real-time adjustment of scanning parameters for optimal coverage. Non-rigid registration techniques align the acquired data with the structure’s CAD model, enhancing defect identification accuracy. Geometric defects are quantified through comparative analysis, providing insights into structural health. This proactive approach helps prevent costly damages and mitigate risks, offering advantages over traditional methods such as enhanced efficiency and safety. Incorporating real-time monitoring into maintenance schedules optimizes asset performance and extends operational lifespan, which is particularly beneficial for industries like aeronautics and automotive, where inspection is mandatory for ensuring part and structure conformity to design requirements.
Multi-Physics Modeling of Continuous and Pulsed Lasers: Impact on Thermal History and Surface Properties in Powder Bed Fusion Additive Manufacturing
Authors: Qadeer Abdul , Akhtar S. Sohail , Abubakar Abba A. , Arif Abul Fazal M. , Mekid Samir , Al-Athel Khaled S.
Published: January 2025

Metal additive manufacturing (MAM) processes have revolutionized manufacturing and design, offering unprecedented freedom to create intricate and complex parts. Research has demonstrated that in laser powder bed fusion (LPBF) of metal additive manufactured parts, the microstructure and surface can be influenced by various process parameters. However, the influence of laser pulse parameters in LPBF remains relatively unexplored. Laser pulse parameters significantly affect the microstructure and melt pool evolution in metal powder bed additive manufacturing processes. Control over these variations is crucial for achieving desired material properties and part quality. Adjustments in pulse parameters, such as power, width, and interval, can alter grain size, orientation, and subcellular structure, thus impacting mechanical properties. Moreover, optimizing laser energy density by controlling pulse parameters can mitigate defect formation, enhancing density and mechanical properties. Hence, exploring precise control over pulse width and interval during manufacturing contributes significantly to achieving high-quality components.

In this study, a meso-scale numerical model was employed to investigate the influence of pulse parameters, such as pulse width and interval on the thermal history and melt pool evolution in LPBF. The physics-based model incorporates key phenomena such as heat transfer via radiation & convection, phase change, recoil pressure, and density-driven melt pool flow. These physical phenomena play a crucial role in the surface finish and microstructure of fabricated parts, affecting the formation of defects such as balling, keyhole, and spattering. A discrete element model (DEM) was employed to construct the powder bed, while the finite volume method (FVM) simulated the thermal-fluid behavior using an initial condition derived from an STL file. Validation of the numerical model against existing literature has confirmed its capacity to accurately predict melt pool behavior, including its influence on surface roughness, as well as temperature distribution and cooling rates across different laser source pulse width and interval settings. Additionally, it can also pave the way for future research directions, including the exploration of in situ hybrid processes involving multiple lasers for surface processing and enhancement. The evolution of computational models promises to facilitate more sophisticated control strategies, ultimately enhancing outcomes and efficiency in metal additive manufacturing processes, paving the way for tailored and optimized LPBF MAM parts.
Influence of Tool Diameter in Single Point Incremental Forming of Polymeric Materials
Authors: Diefenderfer Rachel , Vanderwiel Chad , Ragai Ihab , Rubeo Mark , Barkley Natalie , Best Conner , Laser Kristofer
Published: January 2025

Single point incremental forming (SPIF) is a versatile and cost-effective manufacturing process used extensively in prototyping and low-batch engineering components. It involves the precise movement of a tool over a thin sheet of material, incrementally deforming it into the desired shape. Process parameters significantly influence the quality and formability of the formed component. Factors such as spindle speed, feed rate, toolpath geometry, and tool dimensions impact material deformation, surface finish, and dimensional accuracy. Optimal parameter selection is critical to avoid defects like springback, wrinkling, tearing, or excessive surface degradation, particularly in polymers. Adjusting these parameters enables SPIF to accommodate a wide range of materials and geometries, making it a versatile manufacturing solution for various industries.

The work presented herein investigates the effect of process parameters, namely tool diameter and step size on the formability of polymeric materials. The materials investigated in this study are polycarbonate and nylon 6. A full factorial design of experiments schedule was conducted. Tool diameters varied between 8 mm and 20 mm and step sizes varied between 0.5 mm and 2.0 mm. Feed rate and wall angle remained constant. throughout the experiments. Various measurements took place, such as profile evaluation, thickness distribution, and surface roughness. It was found that in most cases, small tool diameters result in better formability, minimum springback, and more uniform thinning. Additionally, no discernable trend was observed between forming conditions and surface roughness.
Optimizing Mechanical Performance of Carbon Fiber Reinforced Polyphenylene Sulfide Thermoplastic Film Composite via Hot Press Technique
Authors: More Nitin , Mohan Ram V.
Published: January 2025

This paper focuses on enhancing the mechanical properties of carbon fiber-reinforced polyphenylene sulfide (PPS) thermoplastic film composites. We achieve this by employing a controlled hot press process, which involves forming composites with woven carbon fiber layers integrated with the PPS film matrix and subjecting the mixture to elevated temperature and pressure conditions using a hot press. This technique facilitates the complete consolidation of the composite layup with desired layup orientations. As a result, composite films exhibit enhanced mechanical properties compared to conventional processing methods.

Mechanical characterization, including tensile, flexural, and impact testing, is conducted to evaluate the performance of the formed thermoplastic composite. Test data demonstrate notable improvements in tensile strength, flexural strength, and impact resistance. This can be attributed to the efficient load transfer between the carbon fibers and the PPS matrix due to effective consolidation achieved through the hot press technique. Moreover, systematic investigation into process parameters such as temperature, pressure, and processing time enables the optimization of mechanical properties to meet specific application requirements. This comprehensive understanding of process parameters facilitates the production of thermoplastic woven fabric composites with tailored mechanical characteristics, offering a promising avenue for lightweight, high-strength materials in various industries. Furthermore, scanning electron microscopy (SEM) analysis was conducted to examine the microstructure and interfacial characteristics of the formed composites. The micrographs revealed a uniform and oriented distribution of carbon woven fabrics within the PPS matrix and good interfacial bonding. This indicates effective fiber-matrix interaction, which has practical implications for developing high-performance materials.

The influence of processing parameters on the mechanical properties and microstructure of the composites was systematically studied to optimize the fabrication process. The findings demonstrate the feasibility of producing high-performance carbon fiber-reinforced PPS thermoplastic film-based composites via the hot press technique, offering promising opportunities for advanced engineering applications requiring lightweight, durable, and high-strength materials.
Deep Reinforcement Learning for Decentralized Multi-Robot Control: A DQN Approach to Robustness and Information Integration
Authors: Wu Bin , Suh C. Steve
Published: January 2025

The superiority of Multi-Robot Systems (MRS) in various complex environments is unquestionable. However, in complex situations such as search and rescue, environmental monitoring, and automated production, robots are often required to work collaboratively without a central control unit. This necessitates an efficient and robust decentralized control mechanism to process local information and guide the robots’ behavior. In this work, we propose a new decentralized controller design method that utilizes the Deep Q-Network (DQN) algorithm from deep reinforcement learning, aimed at improving the integration of local information and robustness of multi-robot systems. The designed controller allows each robot to make decisions independently based on its local observations while enhancing the overall system’s collaborative efficiency and adaptability to dynamic environments through a shared learning mechanism. Through testing in simulated environments, we have demonstrated the effectiveness of this controller in improving task execution efficiency, strengthening system fault tolerance, and enhancing adaptability to the environment. Furthermore, we explored the impact of DQN parameter tuning on system performance, providing insights for further optimization of the controller design. Our research not only showcases the potential application of the DQN algorithm in the decentralized control of multi-robot systems but also offers a new perspective on how to enhance the overall performance and robustness of the system through the integration of local information.
Investigation of Thermal, Dielectric and Mechanical Characteristics of Polymer Nanocomposite Films
Authors: Ericksen Jared , Smith Michael , Terifay John , Insinga Nicholas , Hayes John , Marshina Joseph , Olitsky Benjamin , Seybold Madeline , Olivo Matthew , Allison Logan , Singh Amit , Xue Wei
Published: January 2025

Nanocomposites have the unique ability to improve various properties of a host material while allowing it to maintain its other properties and form factor. Polymers can function as excellent host materials because of their strong intrinsic properties and their ability to accept a wide variety of nano-scale fillers. These polymer nanocomposites have the capacity to improve the field of high-temperature superconducting (HTS) power transmission. HTS power transmission allows for significant weight reductions as well as improved power/volume capacity when compared with standard copper wire systems. HTS still requires further technical development to become widely applicable. With the current lack of thermally stable dielectric insulation materials, polymer nanocomposites can provide the proper blend of thermal, electrical, and mechanical properties necessary to create the ideal superconducting cable. This paper discusses the characterization of poly (pyromellitic dianhydride-co-4,4’oxydianiline), amic acid (PAA) and silicon dioxide (SiO2) nanocomposites. It has been determined that increasing nanoparticle concentration can lead to improved electrical and mechanical properties for various test methods, including liquid nitrogen (at −60°C and −196°C) and gas helium (at room temperature) environments. These testing environments relate directly to potential HTS applications and provide valuable insight into the nanocomposites’ applicability in real-world applications.
Evaluating Bead Geometry, Hardness, and Residual Stress for a Multi-Layer 316L Laser-Wire Based DED Block
Authors: Saghafi Marzia , Mirazimzadeh Seyedeh Elnaz , Urbanic Ruth Jill , Alpas Ahmet
Published: January 2025

Additive Manufacturing (AM) has revolutionized manufacturing by enabling the layer-by-layer fabrication of components from 3D model data, offering unprecedented design flexibility. Despite its numerous advantages, concerns persist regarding costs, technology readiness, and product reliability. AM excels in low volume production runs and processes like Directed Energy Deposition (DED) have gained recognition for manufacturing large metal parts and component repair. However, understanding the influence of heating and cooling cycles on final part properties remains a crucial research gap. This study focuses on investigating the impact of geometric location for a multi-layer sample with a one-way deposition sequence on the microstructure, bead geometry, and mechanical properties (Vickers microhardness and residual stress) of 316L DED-manufactured parts. By examining these characteristics across different geometric locations and comparing the experimental data to simulation results, this study aims to provide valuable insights to build a foundation for optimizing the DED manufacturing process.
Productivity Analysis of Automated 3D Printing System
Authors: Aljasmi Nawal , Almaeeni Khuloud , Alyammahi Mozah , Panda Amrut Sekhar , Almheiri Ahmad , Joy Arun , Susantyoko Rahmat Agung , Subramanian Vinod
Published: January 2025

Automated in-house systems are essential in today’s manufacturing and prototyping, providing several benefits that improve efficiency, productivity, and creativity. By automating manufacturing processes in-house, organizations get better control over production schedules, control of quality, and cost reduction. Automation minimizes the need for manual work, decreasing human error and unpredictability while enhancing productivity and uniformity. Automated technologies in prototypes allow for quick design iteration and testing, which speeds up the product development cycle and reduces time to market. Furthermore, in-house automation increases adaptability to changing market needs and customized requirements, improving competitiveness and customer satisfaction. In this research work, we built and investigated an automated 3D printing system to facilitate and reduce the time and effort consumed in operating the machines — an automated system for 3D printing utilized with robotic arms and FFF 3D printers. We conducted a comparative study, evaluating the efficiency of our system against manual operation in terms of print job accomplishment. By incorporating advanced technologies, our robotic automated 3D Printing system significantly enhances productivity. This paper serves two main purposes: firstly, it provides a detailed system architecture, and secondly, it offers a systematic survey study with quantitative results. This study is particularly useful as a reference for users seeking to increase the productivity of their additive manufacturing operations.
Numerical Modeling of Hydrodynamic and Thermal Behavior During Bubble Growth in Film Boiling
Authors: Pal Divyprakash , Perez-Raya Isaac
Published: January 2025

Film boiling occurs in various high-temperature applications that require accurate heat transfer analysis. Utilizing computer simulations, researchers have effectively investigated boiling mechanisms and the influence of thermal and fluid properties. In this study, a horizontal surface film boiling numerical simulation was carried out using a two-phase fluid. The saturation temperature and pressure are 500 K and 1 atm, and the heated wall is at 505 K. Ansys Fluent was customized using user-defined function (UDFs) to accommodate for the interfacial heat and mass transport, including a sharp interface and direct calculation of mass transfer with temperature gradients. Four different mesh resolutions were analyzed, achieving good agreement between the shape of the bubble and minimum difference of 6% and 12% for curvature height and the average Nusselt number values. Verification using the 1D Stefan problem for interface displacement showed a maximum error of 0.45% and less than 0.1% for temperature distribution. The results show that before the bubble departs, a zone of high-temperature variation develops in the neck of the bubble, and the thermal film is suppressed at the bottom surface. Local heat transfer coefficients show a similar trend of high peaks at the locations where the thermal film is comparatively thinner from other locations.
A Novel Digital Twin Model to Support Intelligent Robotic Manufacturing System According to Industry 4.0 Trends
Authors: Guerra-Zubiaga David A. , Colani Melody , Richards Gershom , Cavens Katrina , Block Logan , Olliff George , Aksu Murat
Published: January 2025

Data, Information, and knowledge models are vital research topics explored in recent years to support advanced manufacturing decisions through important systems. Today, several tools assist in modeling these parameters, one of which is known as Knowledge-Based Engineering Systems (KBES). A KBES integrates different knowledge types to support decisions and allows the incorporation of multiple types of manufacturing and organization standards to work in a variety of business contexts. With the increased complexity of industrial manufacturing systems caused by Industrial 4.0 trends such as digital manufacturing and cyber-physical technology like digital twins (DTs), the use of KBES is becoming an essential tool in Project Life Management (PLM) to ensure DT design is suitable to preserve the integrity of industrial standards. This paper aims to utilize Industry 4.0 trends, ISO standards, and the Computer Integrated Manufacturing Open System Architecture (CIMOSA) as a framework to build a KBES that assists in the development of a novel DT model. This model supports the design and implementation of a DT within the environment of an intelligent robotic manufacturing system. The proposed DT model is beneficial in preserving industrial standards in DT development and aids as a reference for the foundation of DTs design in other manufacturing environments. A case study is performed by applying the proposed DT model to the design and implementation of a DT within a basic pick-and-place system, proving its applicability and function in the support of intelligent robotic manufacturing systems.
Investigating the Impact of PVD Process Parameters on Residual Stress Distribution: A Multiscale Numerical Analysis Approach
Authors: Gul Jan Shazia , Abubakar Abba Abdulhamid , Arif Abul Fazal M. , Chowdhury Mohammad Shariful Islam , Akhtar Syed Sohail , Bose Bipasha , Al-Athel Khaled
Published: January 2025

Residual stress in thin PVD coatings arises from a combination of growth stress and thermal stress. Despite the lower processing temperature, thermal mismatch stresses can be significant due to variations in physio-thermal properties between the coating and substrate materials. Understanding the underlying physics of the deposition process and resulting stresses, particularly thermal stresses within the coating-substrate system, is paramount. Key physical parameters of both the coating and the substrate, such as coefficient of thermal expansion and elastic modulus, significantly influence thermal stresses. There is a demand for a comprehensive methodology in thermo-mechanical design, specifically for layered coatings, that should complement experimental procedures used to evaluate coating performance. Although analytical models can handle linear-elastic or simple elastic-plastic materials for thermal stress prediction, numerical techniques such as finite element analysis (FEA) can address more general 2D or 3D problems in simulating thermal stresses within coating-substrate systems. This study employs a multi-scale numerical approach to estimate the residual stress distribution within the coating-substrate system. Validation of the numerical model is conducted against experimental findings concerning a TiB2 - Tungsten Carbide coating-substrate configuration. The validated model is then utilized to explore the influence of process parameters on residual stress distribution across various coating-substrate systems.
Assessment of Machine Learning Approaches for the Predictive Modeling of Plasma-Assisted Ignition Kernel Growth
Authors: Bhuiyan Sharif Md Yousuf , Mostafa Tanzim , Schoen Marco P. , Mahamud Rajib
Published: January 2025

Plasma-assisted combustion has become popular in propulsion and combustion due to advanced emissions reduction and energy efficiency improvements. The rate of growth of the ignition kernel, within the development of the initial combustion zone, has a significant impact on combustion stability and efficiency and pollutant emissions. The plasma-assisted ignition methods, such as nanosecond pulse and laser discharge systems, have been used to create high-energy plasmas to ignite fuel-air mixtures. Such systems provide the benefits of a reduction in energy, increased flame stability, enhancement in igniting leaner mixtures, better reliability, and improvement in engine performance with reduced emissions. Such systems are also able to reduce the ignition delay, stabilize the flame with free radicals, and reduce the need for ignition energy. The numerical models used for prediction, with the combination of Computational Fluid Dynamics (CFD) and chemical kinetics, rely on sparse experimental data for the reproduction of heat release. A validated model and machine learning are used to predict kernel growth in igniting methane-air mixtures considering laser intensity and initial electron density. It employs two algorithms to predict temperature profiles. This study develops machine learning models to predict ignition kernel growth in methane-air mixtures using validated model data. Factors include laser intensity, initial kernel size and shape, and initial electron density for plasma energy deposition. Machine learning algorithms, including Decision Tree (DT) and Random Forest (RF), predict temperature profiles to optimize ignition parameters and improve combustion efficiency.
UV-Resistant Colored Radiative Cooling Paints for Robust Outdoor Performance
Authors: Carne Daniel , Barber Emily , Jain Aakar , Lee Wonjune , Ruan Xiulin
Published: January 2025

Radiative cooling paint provides passive cooling by reflecting sunlight and emitting thermal radiation through the sky window to deep space. To maximize solar reflectance, radiative cooling paints are often white, however, colored paints are also necessary for aesthetic, anti-glare, and color-coding purposes. Here, we demonstrate single-layer green and blue colored radiative cooling paint with a 26.9 and 13.7 percentage point enhancement, respectively, over commercial paints of a similar color. The enhanced solar reflectance is achieved by increasing the UV and NIR reflectance through a narrow band absorption color pigment, a low absorption binder, and a high volume fraction of BaSO4 pigment. Additionally, accelerated UV weathering tests show this paint retains high UV-resistance. This is attributed to the use of inorganic pigment instead of dyes or organic pigment which are commonly used in colored radiative cooling paints. Field tests further confirm these results with our colored radiative cooling paints showing up to 6.3 °C (11.3 °F) and 3.2 °C (5.8 °F) lower temperatures than the commercial paints under direct sunlight. Overall, our work realizes single-layer, scalable, and cost effective colored radiative cooling paints that allow surface to stay cooler, thereby reducing energy demand for buildings, vehicles, and outdoor equipment while retaining high UV-resistance.