bluuebunny/crossref_metadata_2025_split huggingface dataset:
Stiffness Degradation in CFRP Laminates Subjected to Fatigue Loading
Authors: Prakash Raghu V. 
Published: February 2024

Abstract
Reinforced polymer composite materials are widely used in aerospace, marine or other civilian structures, as it offers the advantage of tailor-suiting to the design requirements. During service, they undergo damage due to service loading, environment as well as accidental bird hit (in case of aircraft), and tool drops during maintenance. Low velocity damage causes a barely visible impact damage (BVID), which is largely conspicuous has the potential to become a serious concern, if the structure undergoes fatigue loading subsequently in-service; the damage progression results in the loss of residual strength and stiffness of the component.
As a part of this study, the stiffness degradation for a woven mat carbon fiber (500 gsm having a nominal thickness of 0.42 mm) polymer composite specimen where the mat is laid in: a) 0–90 orientation and b) quasi-isotropic configuration [0–±45/90]s is examined after it has been subjected to a low velocity drop-impact of 53 J. After the drop impact, these specimens are subjected to constant amplitude loading (0–90 orientation) and to a programmed version of FALSTAFF (Fighter Aircraft Loading Standard for Fatigue) spectrum.
The unloading stiffness of the specimen was estimated from the load versus displacement data that was recorded at periodic intervals of 10000 cycles for the experiments under constant amplitude loading and after every block of FALSTAFF loading, i.e., after 1242 cycles of programmed load application. It is observed that the stiffness of the specimen degrades with the progression of damage. The stiffness degradation is found to have characteristic three stage response and can be corresponding to the three stages of damage progression. Infrared thermographic record of temperature response during fatigue testing was done to understand the damage progression. It was noted that the stiffness degradation was the highest under constant amplitude loading, followed by the spectrum loading.
Local Pursuit Strategy-Inspired Cooperative Formation Flight and Collision Avoidance for UAV Cluster
Authors: Wang Yi , Li Ni , Wang Ban , He Xuemin , Zhu Yongning , Zhou Ming 
Published: February 2024

Abstract
The unmanned aerial vehicles (UAV) cluster, compared to a single UAV, has better application potential in implementing complex and dangerous tasks. In recent research, cooperative formation control strategy and obstacle avoidance have always been focused to secure the safety of flight and task implementation. In this research, we propose the Local Pursuit (LP) strategy-inspired cooperative flight and collision avoidance model inspired by foraging ants, which consists of a formation control module and an obstacle avoidance module. In the formation control module, under the guidance of LP strategy, the follower UAVs will always point their velocity towards the leader UAV (which can be virtual) like ants following the ant ahead. Meanwhile, the velocity and formation position of each follower UAV can be adjusted by the ‘speed control parameter (SCP)’ related to the speed of the follower and the constant bias between the leader and the follower which is determined by the desired formation. Considering there are cases that some areas threatening the safety of the UAV need to be avoided, which can be regarded as external obstacles, an obstacle avoidance module based on velocity alignment with “shill” agents (virtual UAVs moving outward from obstacles to real UAV) is added for effective obstacle avoidance. The proposed method has limited parameters that need to be tuned. Finally, to show the efficiency of the proposed method, the simulations of UAV cluster formation flight and obstacle avoidance by using the proposed method are conducted through MATLAB.
Extrusion Mechanisms for Printing Thermosetting Prepolymers
Authors: Danish Muhammad , Mishra Ravi , Junaid Abid Ali , Emon Omar Faruk 
Published: February 2024

Abstract
The Direct Ink Writing (DIW) additive manufacturing method has been widely used in advanced printing applications, although it is often considered a complex and expensive system to construct. This study investigated two extrusion mechanisms that can be integrated with a gantry system for printing functional thermosetting inks. The first mechanism employs a lead screw directly connected to a bipolar stepper motor, which converts the rotary motion of the motor shaft into a linear motion. The lead screw is used to push the plunger of a syringe loaded with viscous thermosetting prepolymers. The second mechanism employs a gear system to transmit the motion from the motor shaft to the lead screw. The mechanical fixtures to hold the motor, lead screw, gears, and other components were designed and fabricated via 3D printing. Different gear ratios were examined for the gear-based system. An acrylate-based photocurable resin was thickened using fumed silica to induce non-Newtonian shear-thinning property. The study focused on various print parameters, including print speed, extrusion rate, linewidth, and ink viscosity, and tailored G-code commands were used to print different inks. The printed structures were photopolymerized using ultraviolet (UV) light. The printed outcomes were evaluated, and two extrusion mechanisms yielded comparable results. It is believed that the proposed system will make 3D printing of functional inks more accessible.
Autonomous Vehicle Guidance Using Neural Network and Random Forest Model
Authors: Gadhvi Tirth , Shankar Praveen 
Published: February 2024

Abstract
In this paper, a guidance strategy for autonomous vehicles aimed at preventing collisions caused by faulty sensor data is presented. A kinematic bicycle model is utilized to generate a vehicle motion dataset for training the prediction models. The algorithm employs a random forest prediction model, utilizing a neural network generated dataset to minimize errors in planning the motion. As a case study for implementing the guidance algorithm, a freeway entrance ramp scenario was simulated utilizing MATLAB. The guidance strategy addresses the limitations of GPS-based autonomous vehicle navigation systems, which may experience inaccuracies due to atmospheric conditions and other factors, thereby preventing collisions under such conditions of error.
Machine Learning Accelerated Atomistic Simulations for 2D Materials With Defects
Authors: Sun Shijie , Singh Akash , Li Yumeng 
Published: February 2024

Abstract
2D materials generally show very different physical and chemical properties from 3D materials, which provide them promising applications in cutting-edge technology areas like aerospace, energy storage and electronics. To better understand and illustrate their unique properties, current research heavily relies on atomistic simulations, while successful simulations require the high reliability of interatomic interaction potentials that empirical potentials cannot provide. The ab initio calculations, for example density functional theory (DFT), are able to conduct high-fidelity simulations in essence, but with a high computational cost and largely limited simulation size. Recently machine learning potentials become a trend to interpolate the potential energy surface based on artificial neural networks (ANNs) with reference datasets from first principle calculations. Although machine learning potentials have been developed for many material systems including 2D materials, there is limited work published regarding structural defects. Using graphene as a model material, we demonstrate a new machine learning potential for 2D materials with defects. The reference energies are generated by DFT calculations. ANN with two hidden layers is used for the training, which has been demonstrated to be suitable for developing machine learning potential in our previous work. For ANN input, the atomic structures are coded using perturbation-invariant representations. After training, the weight and bias parameters are exported as our potential and then imported into LAMMPS software to conduct MD simulations. It is expected that our work provides fundamental support on investigating defective graphene and understanding defect effects to develop structure-property relationship. This will also promote the development of machine learning based simulation tools for the study and design of complex materials.
Analysis and Modeling of the Laser Bore Joint to Prevent Alignment Failures
Authors: Troise Mario , Sorli Davide , Gaidano Matteo , Melchiorre Matteo , Palmieri Pierpaolo , Mauro Stefano 
Published: February 2024

Abstract
The DEMOnstration (DEMO) reactor, the next-generation fusion power plant, requires plasma-facing components (PFC) to be periodically replaced during the reactor lifetime. However, a remote maintenance (RM) is needed due to neutron activation. A relevant RM operation is the replacement of piping supplying around the plasma chamber using a Service Joining System (SJS) for in-bore cutting and welding. Welding operation demands strict alignment requirements obtained with appropriate RM strategy. Therefore, alignment task involves a first phase of positioning during which the new pipes are brought close to their final position by a main system, and in second phase a robot system drives pipes extremity achieving precise coupling. This paper presents the multibody model of Laser Bore Joint (LBJ) of SJS focusing on flexible elements, named bellows, that provide compliance both in axial and radial direction during alignment tasks. The LBJ model is built in Adams environment and a suitable sensors layout is devised to assess the misalignment state in the case of failure in the first alignment phase. The secondary handling device is introduced and its control strategy is implemented in a co-simulation framework with the multibody system. Results underline the feasibility of proposed alignment procedure.
Toward Additive Manufacturing of Architected Materials: A Planar Analysis
Authors: Liu Jitian , Armand Mehran , Kutzer Michael D. M. 
Published: February 2024

Abstract
The term architected material describes a class of materials whose physical properties are determined both by material properties and geometry. These materials play an increasingly crucial role in various fields including research, medicine, and industry. Due to its ability to produce complex geometries, additive manufacturing (AM) technology has been widely adopted in the fabrication of architected materials. Nonetheless, in most scenarios, AM is only applicable to the geometries containing open voids. This effort investigates the feasibility of fabricating geometries that contain closed voids via support-free AM. This initial investigation considers planar part geometries produced using a three-degree-of-freedom (3-DoF) task space. Part geometry is defined as a two-dimensional (2D) polygon containing convex polygonal holes. Using this assumed part geometry, this work partitions the part using a set of polygons that are ordered using feasible layering directions and collision constraints. Because the 3-DoF task space enables re-orientation of the part during fabrication, the overhang constraints (defined relative to the gravitational vector) can be addressed by rotating the part. Results show a method of generating and ordering polygons that can be printed subject to collision and overhang constraints.
Preliminary Dynamical Model of the LISA/LISA-Pathfinder Release Mechanism
Authors: Tomasi Matteo , Zanoni Carlo , Vignotto Davide , Bortoluzzi Daniele , Dalla Ricca Edoardo 
Published: February 2024

Abstract
LISA Pathfinder (LPF) is a space mission launched in 2015 aimed at testing key technologies for LISA, the first gravitational wave observatory in space. The LPF payload includes two gravitational reference sensors (GRSs), each one hosting a floating test mass (TM). The grabbing, positioning and release mechanism (GPRM) injects the TM into the scientific free-fall state, satisfying strict position and velocity requirements at release. In LPF, the TMs acquired non-compliant velocity at the release, compatible with spurious impacts with the GPRM. The impacts could be caused by oscillations of the mechanism due to the impulsive retraction of a piezo-driven release tip.
Ground-based measurements of the GPRM dynamic response show vibrations triggered by the quick tip retraction together with a static deformation at steady state, so far unnoticed. In this work, we build a lumped parameters model, validate it by matching the experimental data, and use it to understand the mechanism dynamics. Moreover, we reshape an existing GPRM finite element model to the analysis of vibrations, in order to provide a physics-based tool to explore different designs and conditions. We present here the preliminary results of these activities.
Food Quality Inspection and Sorting Using Machine Vision, Machine Learning and Robotics
Authors: Drogalis Conor , Zampino Christopher , Chauhan Vedang 
Published: February 2024

Abstract
The integration of Industry 4.0 technologies, such as machine vision, machine learning, and robotics, has transformed the food industry by enabling more efficient, accurate, and productive food quality inspection. This scope of this work is to implement these technologies in the inspection of chocolate chip cookies in a lab based setting. The PC-based system includes a conveyor belt, webcam, low-cost robotic arm, grayscale sensors, and MATLAB and Arduino microcontroller for communication between the vision system and the robot. Machine vision captures images of cookies and processes these images for feature extraction, while machine learning algorithms classify cookies based on their visual features and identify defects. The use of Artificial Neural Networks for training and testing results in an overall accuracy of 95% and 90%, respectively. The sorting of cookies based on the machine learning classification is carried out using a robotic arm. The robotic arm receives signals from the ML algorithm to remove defective cookies from the conveyor into a rejected cookies bin. The closed-loop system effectively (98%) inspects food quality, reducing defective food from reaching consumers. Machine vision and machine learning techniques offer a promising approach to improving quality control in the food industry.
Using Validation Metrics to Assess RANS Turbulence Models Performance at Full Scale Reynolds Numbers
Authors: Eça Luis , Kerkvliet Maarten , Toxopeus Serge L. 
Published: February 2024

Abstract
One of the major advantages of Computational Fluid Dynamics (CFD) is the ability to simulate flows at full-scale Reynolds numbers, which for naval applications can reach values of 109. For this range of Reynolds numbers, the Reynolds-averaged Navier-Stokes (RANS) equations are still the most common mathematical model. The averaging procedure used to derive the RANS equations generates the Reynolds-stresses that are determined by a turbulence model. In the last 50 years several turbulence models have been proposed in the open literature ranging from the initial eddy-viscosity algebraic methods to Reynolds-stress models that solve 7 additional transport equations. The assessment of the modeling error of RANS simulations requires the existence of experimental data and so most of the reported exercises of modeling error assessment are performed at model-scale Reynolds numbers. Furthermore, many of these studies rely on simple graphical comparisons without the quantification of experimental, numerical and input uncertainties.
In this paper we apply the V&V20 validation metrics to quantitatively assess the modeling error of RANS using six different turbulence models in the simulation of the flow around the KVLCC2 at model-scale Reynolds number, for which experimental data are available at six cross-sections of the stern and near-wake region including the propeller plane. It is illustrated that graphical comparisons are less conclusive than the quantitative assessment of the modeling error taking into account uncertainties of experiments and simulations.
The same validation metrics are applied to quantify the differences between simulations performed with the same models at full-scale Reynolds number. The results show quantitatively that differences between the results obtained with the six turbulence models at full-scale Reynolds number are much smaller than those observed at model-scale Reynolds number. In the absence of experimental data at full-scale, such exercise suggests that the role of the turbulence model in full-scale flows around ships is not as important as in model-scale simulations.
Influence of Agility on the Innovation Capability of Organizations - An Empirical Study
Authors: Schoeck Moritz , Batora Mona , Mueller Johannes , Bursac Nikola , Albers Albert 
Published: February 2024

Abstract
In today’s dynamic market landscape, mechatronic product development faces challenges due to changing customer demands and unpredictable events. To secure organizations’ innovation capability in this environment, agile methods have gained prominence. Previously, the authors identified success factors and characteristics of agility and innovation capability through extensive literature research. This study now focuses on establishing cause-effect relationships between agility and innovation capability using these factors. An empirical study involving professionals from diverse industries explores how introducing agility in mechatronic product development can improve organizational innovation capability. The findings indicate that agility positively influences innovation capability to varying degrees. This research contributes valuable insights to enhance the innovation capability and agility of industrial organizations sustainably. By embracing agile approaches, organizations can thrive in the face of a dynamic market environment.
Characterization of Functionalized Nanodiamonds in Mineral Oils for Transformer Applications
Authors: Swiecichowski Patrick , Nevills Miles , Languri Ethan , Davidson Jim , Costa Lino , Kerns David 
Published: February 2024

Abstract
The use of transformers for varying the voltage in a power grid is heavily limited and determined by its thermal management. This thermal management is largely based on the mineral oil that surrounds the core of the transformer, this mineral oil dissipates the heat that is produced by the core of said transformer. The current mineral oil that is used is a dielectric, that restricts the ability to conduct electricity, and is widely used since the thermal properties of the mineral oil are high compared to the low cost of the mineral oil itself. The improvement in the heat transfer of the transformer not only increases its lifespan but also increases the capacity for the throughput of power. With electrification being an increasing focus, the demands on the power grids are only increasing as time passes. Nanodiamond-enhanced mineral oils present a potentially novel and surprisingly inexpensive solution to increase the heat transfer rate from transformers fins, leading to an increase in grid throughput capability. By using the nanodiamond-enhanced mineral oil, potentially little to no alterations to the current design of the transformer would be necessary. Because all that would need to be changed would be the mineral oil surrounding the core, this means that most transformers currently in use could also be enhanced, reducing the cost by not manufacturing another replacement transformer and just replacing the mineral oil inside. This is also a passive solution compared to active solutions such as circulating pumps for the oil dissipating the heat through an exchanger, so there is no added mechanical complexity or electrical consumption. This paper investigates the physical and thermal properties of functionalized nanodiamonds in mineral oils at various concentrations. A detailed discussion of multiple properties of the fluids will be performed including: how the introduction of the functionalized nano-diamonds to the mineral oil affects the thermal conductivity the mineral oil experiences, how the change in the mineral oil affects the viscosity and how this change in viscosity might impact the function of the transformer, how this change in the mineral oil affects the specific heat capacity and the potential impact this might have on the longevity and efficiency of the transformer system, and lastly, how the density of the mineral oil is impacted and what kind of implications or complications this might pose for the prolonged use in transformers. This paper will also take a look into the changes that adding the functionalized nano-diamonds to the mineral oil will have on the oil itself, microscopically, and the interactions it might experience by using Scanning Electron Microscopy, Dynamic Light Scattering, and X-ray Diffraction.
Spatially Resolved Ion Current Density Measurements With a Transient Insertion Langmuir Probe
Authors: Martin Christopher R. , Orr Jacob , Rahman S. M. Mahbobur , Untaroiu Alexandrina 
Published: February 2024

Abstract
This work presents a novel technique for constructing spatially resolved ion densities from Transient Insertion Langmuir Probe (TIL Probe) measurements in a flame. Similar to a tomographic transformation, this technique is used to deduce the spatial distribution of ions in a flame from many individual measurements that are integrated along a probe’s length. We demonstrate the approach in the oxyfuel cutting torch preheat flame, which presents two severe challenges for electrical measurements: (1) temperatures over 3,000K destroy most probes made from alloys with appropriate chemical stability, and (2) the relevant length scales are on the order 0.15 mm. Presented here are (1) a Fourier series formulation for the current density, (2) a least-square problem for calculating the coefficients, (3) criteria for the highest wavenumber allowed in the expansion, (4) description of an experiment used to measure probe currents in an oxyfuel cutting torch preheat flame, (5) solution for spatially resolved current density in the oxyfuel cutting torch flame.
Images of ion current density are produced with a resolution of 0.15 mm (0.0059 in), exhibiting peak current densities around 14 μA/mm. It is found that low-signal regions in the “shadow” of high-signal regions can suffer from signal-to-noise ratio problems due to natural fluctuations in the flame, and improvements are proposed to mitigate the effect. It is found that the numerical cost of setting up the resulting Hermitian-matrix linear problem far exceeds the numerical cost of inversion. High-level packages like Python and MATLAB are far too slow, so a multi-threaded algorithm is implemented in C, and the LAPACKE C library is used for efficient linear algebra support.
In-Situ Dynamic Strain Sensing Using Mechanized Air-Atomized Sprayed Flexible Nanocomposite Sensors
Authors: Li Pengfei , Xu Chao , Su Zhongqing 
Published: February 2024

Abstract
Nanocomposites are gaining popularity in the field of structural health monitoring due to their ability to sense structural dynamic strain. This paper introduces a breed of flexible nanocomposite sensors manufactured by mechanized air-atomized spray coating process. The employed method not only enables the automation and continuous production of nanocomposite coatings, raising efficiency in production, but also allows for precise control of spraying parameters to reduce the impact of human factors, ensuring coating uniformity and quality. Flexible nanocomposite sensors are fabricated by doping carbon black nanoparticles in polyvinylpyrrolidone powder with the mechanized air-atomized spray coating process. Experimental results indicate that the flexible nanocomposite sensors can effectively respond to vibration-induced dynamic strain. The flexibility and sensitivity of these sensors make them promising for use in various structural health monitoring applications.