bluuebunny/crossref_metadata_2025_split huggingface dataset:
Advancing Geometric Inspection of Non-Rigid Structures Using Mobile Robots and 3D Laser Scanners
Authors: Sattarpanah Karganroudi Sasan , Rahmanian Vahid 
Published: January 2025

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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.