bluuebunny/crossref_metadata_2025_split huggingface dataset:
Navigation Method for UGV on Aerial Elevation Maps for Autonomous Missions in Sandy Deserts
Authors: Bjelotomic Marko , Subramaniam Prashanth , Ismail Hesham , Aljasmi Abdallah 
Published: January 2022

Abstract
Autonomous navigation in wide outdoor areas faces challenges not present in structured environments. Simultaneous localization and mapping (SLAM) is a popular method of creating the map using the robot, and navigating in it. SLAM is difficult to achieve where there is no paved roads, buildings, and trees like in the sandy deserts. Autonomous robots cannot predict what is waiting behind the sand dune. Even if the terrain was mapped earlier, sand dunes can shift unpredictably. Therefore, mapping and GPS-based path planning should be done before every autonomous mission in such a dynamically shifting environment. Navigation can rely on GPS signals given the lack of obstructions. We develop a method for an Unmanned Aerial Vehicle (UAV) to autonomously take hundreds of images, convert terrain images into an elevation map, analyze the elevation map and generate traversable GPS waypoints for the Unmanned Ground Vehicle (UGV).
A Sensitivity-Based Approach for Interval Reliability Analysis of Structures Under Random Excitation
Authors: Giunta Filippo , Muscolino Giuseppe , Sofi Alba 
Published: January 2022

Abstract
The present study is devoted to the reliability analysis of linear discretized structures, with uncertain-but-bounded mass and stiffness parameters, subjected to stationary Gaussian multi-correlated random excitation. The reliability function for the extreme value stress process is evaluated in the framework of the first-passage theory. Due to the interval uncertainties affecting structural parameters, the reliability function is an interval function.
The aim of the paper is to propose an efficient procedure for the evaluation of the bounds of the interval reliability function which provides a range of structural performance. To this aim, a sensitivity-based approach is applied. So operating, the dangerous overestimation of the solution, caused by the dependency phenomenon, is overcome.
The main advantage of this approach is to provide appropriate combinations of the values of the uncertain-but-bounded parameters which yield accurate predictions of the bounds of the interval reliability function for the extreme value stress process. A case study is analyzed to demonstrate the accuracy and efficiency of the presented method.
Evanescent Coupling Between Aluminum Pillars
Authors: Akiki Rock , Pennec Yan , Noual Adnane , Lheurette Eric , Bonello Bernard , Djafari Rouhani Bahram 
Published: January 2022

Abstract
We theoretically investigate with the help of the finite element method, the interaction between aluminum pillars erected on top of a semi-infinite substrate of silicon in the low frequency range. Our interest is to control and manipulate the propagation of surface acoustic waves (SAW) along the linear chain of pillars for frequencies in the [0, 2] GHz range. We show that a pair or a chain of pillars can interact together through the excitation of the resonant compressional and bending eigen-modes, depending on the length of the secant line between the two pillars and the angle with respect to the incident surface acoustic wave. The interaction comes from the coupling between the compressional mode with the out-of-plane motion of the Rayleigh wave. Such a coupling can be achieved when the pillars are closed enough from each other to allow the recovery of the evanescent fields.
Bell 412 Full Flight Envelope Aircraft Simulation Model Development and Evaluation With Nonlinear Equations of Motion
Authors: Crain Alexander , Ricciardi Joseph , Stachiw Terrin 
Published: January 2022

Abstract
Flight models are used to accurately predict the aircraft performance and response and are used for aircraft development, engineering analysis, and pilot training. It is common practice to use linearized small perturbation equations of motion for the identification of the stability and control derivatives that form the basis of flight models using system identification. Due in large part to advances in computer technology and optimization techniques, it is feasible to use nonlinear equations of motion for time-domain system identification. This report compares two full flight envelope aircraft models that were developed with identical data: one developed using linearized equations of motion in a state space form, and the other with nonlinear equations. The global flight models were developed for the NRC Bell 412 helicopter in forward flight across its range of speeds, altitudes, and configurations. Use of the nonlinear equations of motion produced a model with less parameter variance and its corresponding global model had improved trim characteristics.
Modelling the Time-Dependent Behavior of Elastomers Using Fractional Viscoelastic Material Formulations
Authors: Leenders Arne , Vahdati Zadeh Hamed , Wangenheim Matthias 
Published: January 2022

Abstract
Elastomer materials are often used for components such as tire treads or hydraulic sealings, when deformable and damping behavior of components are desired and high dynamic loads appear. Such elastomers show time- and frequency-dependent characteristics, called viscoelasticity. The modelling of viscoelastic material is mainly implemented in simulations by rheological models, which often consists of elastic and damping elements. A viscoelastic model can be parametrized to experimental data to describe a specific elastomer with high accuracy. The most common model is the Prony-series. This model uses several Maxwell-branches (connection of one elastic and one damping element in series). Every branch is only able to fit the experimental behavior at one single excitation frequency. This fact makes it necessary to use a lot of parameters for adapting the frequency- and temperature-dependent characteristics over decades of the excitation frequency.
To overcome this need for a huge amount of parameters we formulate a fractional viscoelastic model approach that gets along with a much smaller set of parameters, using finite elements. In order to reduce the numerical effort, a similarly formulated model is set up on force-displacement level additionally. In this way, the complexity of the simulation can be reduced with mapping of the material behavior.
Fabrication, Processing and Characterization of Carbon Fibre Reinforced Laminated Composite Embedded With Graphene Lattice Sheets
Authors: Jadhav Vishwas S. , Kelkar Ajit D. 
Published: January 2022

Abstract
This manuscript introduces the challenges in the fabrication of graphene sheet reinforced non-crimp fabric (NCF) composite laminates and their influence on the interlaminar strength of the composite laminates. In the current work, the laminates were fabricated using non-crimp carbon fabric prepreg along with 50,120 and 240 μm thick graphene sheets at the mid-plane. Double Cantilever Beam (DCB) tests are done as per ASTM 5528 using INSTRON electromechanical testing system. Modified Beam Theory method used to compute Mode I fracture toughness, using load, displacement, specimen dimension, and crack opening displacement.
The graphene sheets are brittle; little bonding between the graphene and matrix observed during the fabrication process results in a fragile interface. To overcome this problem, graphene sheets were converted into a lattice structure. The lattice structure used in the present research had horizontal, vertical, and square grids. Effects of sheet thickness, grid pattern were evaluated by Mode I fracture toughness, with and without nanoengineered enhanced laminates. Axio Image upright microscope used to compare the bonding at the midplane after the DCB test. The results indicate that the composite laminates fabricated using lattice graphene structure had better interlaminar strength than the laminates fabricated with straight graphene sheets.
A Combined Theoretical and Experimental Investigation of the Overall Energy Consumption in a Wet Dual Clutch System During a Driving Profile
Authors: Liu Zhihong , He Ping , Rinderknecht Stephan 
Published: January 2022

Abstract
Reduction of the energy consumption and increase of the energy efficiency of the wet dual clutch system are important factors for vehicle transmission improvement. The demand of a precise investigation of the overall energy consumption in the clutch system has therefore increased significantly. A wet dual clutch system represents in this paper dual clutch packs and their electro-hydraulic system. The energy, which is not efficiently transferred in the power flow path but consumed in the wet dual clutch system, is considered here as the energy consumption. The possible energy consumption in the wet dual clutch system are hydrodynamic drag torques between the disengaged discs, frictional torques during the clutch engagement and electro-hydraulic energy usage for actuating and cooling systems.
The aim of this paper is to evaluate the energy consumption in a whole wet dual clutch system at the system level. Firstly, due to lack of experimental data on the system level, all the available analytical models for predicting the hydrodynamic drag torque are analyzed and compared based on the varied influential parameters. The energy dissipation mechanisms in the clutch engaging phase and the energy usage in the electro-hydraulic system are explained based on the measured signals. One driving profile is applied here and the hydrodynamic energy dissipation is calculated with the chosen analytical model, while the other energy usage in the wet dual clutch system is analyzed with the measured signals. Based on the evaluation on the system level, the majority of the energy is dissipated in the clutch packs for reducing the relative speed, while the energy usage in the actuating system comes next. The hydrodynamic energy losses and the consumption in the cooling system share the lowest proportion.
Controlling Global Surface Variations in Face Milling of Variable Stiffness Structure Based on Iterative Prediction via FEMU and GA
Authors: Liu Shun , Jin Sun , Tian Ang , Gu Qunfei , Chen Kun , Mo Wei 
Published: January 2022

Abstract
To control the global surface variations is highly important for improvement of surface properties and it relies on robust process design of process parameters and fixture layout. However, due to the changes of milling conditions, the controlling of surface variations based on discrete sampling of interest points is hard to meet the global optimal in entire milling surface. To address this issue, this paper attempts to provide a methodology to optimize process parameters and fixture layout iteratively towards minimum the machined global surface variations based on the finite element model updating (FEMU) method and genetic algorithm (GA). The global surface variations are determined by a coupled numerical model via FEMU, which enable iterative finite element simulations to decouple the force-deformation coupling at each instant milling moment and also renew the updating of changing milling conditions along with the changing of milling locations during the whole face milling process. In optimization process, the milling parameters and fixture layout are updated in finite element model via FEMU method and optimally selected via GA based on the global surface variations prediction. Based on the proposed method, the continuous milling parameters and discrete fixture layout can be optimized with minimum global surface variations simultaneously. It provides a comprehensive methodology to control the global surface variations in face milling of variable stiffness structure for its industry implementation.
Investigating the Difference in Cortical Bone Adaptation at Endocortical and Periosteal Surfaces by Fluid Flow Analysis
Authors: Singh Sanjay , Singh Satwinder Jit , Prasad Jitendra 
Published: January 2022

Abstract
Load-induced fluid flow in lacunar-canalicular porosity in bone has been suggested to play an essential role in bone adaptation. The applied load causes the fluid inside the lacunar-canalicular system to flow. The osteocytes are believed to sense the shear stress exerted due to the fluid flow and drive new bone formation. The energy dissipated in moving fluid may be considered as a stimulus for bone adaptation. The endocortical bone surfaces are also believed to adapt differently compared to the periosteal surfaces. We investigate such differences and present a finite element poroelasticity-based mathematical model on estimating bone formation rate at mid-diaphysis of a C57BL6 mouse tibia subjected to cantilever loading. A weighted average of dissipation energy in the zone of influence has been considered in accordance with the literature. The model predicts bone formation rate (BFR) at the periosteal surface as well as on the endocortical surface. As desired, the model can differentiate between a continuous cyclic loading and a rest-inserted cyclic loading. The model establishes that the difference in bone formation at the two surfaces, viz. endocortical and periosteal, may be due to the difference in dissipation energy density only, caused by different boundary conditions at the two surfaces.
An Extended Multi-Actuated Optimized Reconfigurable Freeform Surface (e-MORFS) Mold With Targeted Variability Capacity
Authors: Yang Kunlin , Chen Rui , Qaiser Zeeshan , Johnson Shane 
Published: January 2022

Abstract
Custom manufacturing of freeform surfaces, such as that of sheet metal components for automobiles, or the body panels and the composite parts of aircraft provides major challenges for traditional dedicated manufacturing systems (DMSs) due to the following factors: (1) mass customization, (2) rapid prototyping, (3) time and personnel cost, and (4) system complexity. Typically, flexible manufacturing systems (FMSs) and reconfigurable manufacturing systems (RMSs) are designed for fixed and high variability in the design parameters resulting in high system complexity. These design methodologies have many opportunities stemming from some limitations including: (1) lacking feature analysis of high variability products, (2) high system complexity, (3) reduced capacity for manufacturing of high curvature surfaces, and/or (4) low repeatability. An extended Multi-actuated Optimized Reconfigurable Freeform Surface (e-MORFS) mold is developed for a targeted application for the mass production of the freeform surfaces, e.g. custom foot orthoses (CFOs). The e-MORFS mold aims at the following goals: (1) to achieve size and shape variability for a complex product with a large population, (2) to reduce system complexity considering product features, and (3) to achieve high curvature surface reconstruction with proper boundary conditions (BCs) to avoid wrinkling and stretching. The e-MORFS mold achieves the maximum error range of 0.3–0.5mm for the whole population with only 6 actuators. This study provides the e-MORFS mold and design algorithm potentially used for targeted application of freeform surface manufacturing, e.g. CFOs. These design methodologies may guide designs of freeform surface manufacturing in applications, e.g. automobile, aerospace, biomechanics, and architecture.
A Novel, Low-Cost Thermal System for Integrating Laboratory Experiences in Undergraduate Controls Courses
Authors: Terrill Trevor J. 
Published: January 2022

Abstract
Many undergraduate system dynamics & controls courses have limited laboratory experiences. In courses that include labs, many of the experiences include dynamics of motors, electrical systems, and unstable mechanical systems. The dynamics of these systems are often so fast that the system response cannot be viewed or interpreted real-time. This paper presents a novel thermal system that has been designed to address these challenges and provide meaningful lab experiences to students in undergraduate feedback control courses. The presented system offers multiple advantages that are designed to provide impactful lab experiences. The system can be used for multiple lab experiences that span the entire range of topics in a typical undergraduate controls course, including system modeling, time & frequency domain system analysis, design of controllers, and evaluation of controller performance. The system has been designed with the undergraduate student experience in mind, including the use of MATLAB for all student work, safety features that ensure minimal risk to students, and system dynamics that have been sized so that students can watch and interpret the system response real-time. The paper describes the system design and setup, along with sizing calculations used to create appropriate system dynamics. The thermal modeling used to design controllers is then detailed. The laboratory sessions are then described, including topics in thermal system modeling, system parameter identification, frequency analysis, and controller design. The paper concludes by assessing the use of the system in an undergraduate controls course and addressing topics that can be explored for future lab sessions.
A Machine Learning Approach for Predicting Melt-Pool Dynamics of Ti-6Al-4V Alloy in the Laser Powder-Bed Fusion Process
Authors: Rahman M. Shafiqur , Ciaccio Jonathan , Chakravarty Uttam K. 
Published: January 2022

Abstract
This paper presents a supervised machine learning (ML) model to predict the melt-pool geometries of Ti-6Al-4V alloy in the laser powder-bed fusion (L-PBF) process. The ML model is developed based on the normalized values of the five key features (i.e., the laser and material parameters) — laser power, scanning speed, spot size, powder layer thickness, and powder porosity. The two target variables are the melt-pool width and depth, which define the melt-pool geometry and strongly correlate the geometry with the melt-pool dynamics. Information about the features and the corresponding target variables are compiled from an extensive literature survey. A trained data set is created with the melt-pool evolution data collected from experiments. The data set is divided into training and testing sets before any feature engineering, visualization, and analysis, to prevent any data leakage. The k-fold cross-validation technique is applied to minimize the error and find the best performance. Multiple regression methods are trained and tested to find the best model to predict the melt-pool geometry data. Extra trees regressor is found to be the model with the least amount of error using the mean absolute error function. The verification of the ML model is performed by comparing its results with the experimental and CFD modeling results for the melt-pool geometry at a given combination of the processing parameters in the L-PBF process. The melt-pool geometry outputs obtained for the ML model are consistent with the experimental and CFD modeling results.
Study on Underwater Friction Stir Welded AA 2024-T3 Pipes Using Machine Learning Algorithms
Authors: Sabry Ibrahim , Mourad Abdel-Hamid I. , Thekkuden Dinu Thomas 
Published: January 2022

Abstract
Underwater friction stir welding, a new variant of friction stir welding process in which the weld coupons and tool-specimen interface are completely immersed in the water, has been successful to achieve wide popularity among researchers recently. In most of the studies, the underwater friction stir welding process is limited to join the flat plates. The research conducted on the underwater friction stir welding of pipes is rare due to the complexity in the design of the fixture and setup. Therefore, the current research is aimed to investigate the scope of underwater friction stir welding process for producing quality welded pipe joints. Initially, the current research focused on developing a system with proper components and fixture attached to the vertical milling machine for underwater friction stir welding of pipes. Twenty-seven experiment runs with three intermittent levels of process parameters — spindle speed of milling machine (1000 rpm, 1400 rpm, 1800 rpm), travel speed (10 mm/min, 16 mm/min, 20 mm/min) and shoulder diameter of tool (10 mm, 15 mm, 20 mm) are designed. Secondly, Al 2024-T3 pipes having an outer diameter of 30 mm and a thickness of 3 mm are welded using an underwater friction stir welding process for every combination of the process parameter. The elongation percentage, yield strength and tensile strength are experimentally evaluated from the tensile tests. Finally, the prediction capability of machine learning algorithms such as artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and adaptive neuro-fuzzy inference system with Harris hawks optimization (ANFIS-HHO) for 70% training data and 30% testing data was evaluated. The prediction capability of the machine learning algorithms was evaluated using the Mean Absolute Error, R2 statistic and Root Mean Square Error. ANN was found to the best with the highest R2 and least RMSE for predicting all three responses. Though the ANFIS exhibited the highest R2 and highest RMSE for every response, the incorporation of Harris hawks optimization to the ANFIS slightly improved the prediction capability of ANFIS. The prediction accuracy for elongation percentage, yield strength and tensile strength is found to be in the increasing order of ANFIS, ANFIS-HHO and ANN. The underwater friction stir welding process, machine learning algorithms, methods and results discussed in the paper are promising and useful for experts in the industries.
Acoustic Emission-Based Structural Health Monitoring for Future Lunar Pipelines
Authors: Escarcega Mario , Cephus Meghan , Hughes Skyler , Tsosie Nakii , Kelso Kimberly , Sandoval Raechelle , Ebrahimkhanlou Arvin 
Published: January 2022

Abstract
This paper explores the use of acoustic-based structural health monitoring (SHM) in lunar habitats to detect damage and failure in pipelines used for resource transportation. Acoustic-based SHM on Earth is a well-studied field of research. Various studies validate the effectiveness of acoustic-based SHM to detect, locate, and characterize damage in pipelines. Relevant literature shows that little to no research has been conducted on SHM regarding simulated lunar pipelines. In this paper, acoustic emission (AE) waveforms were collected and analyzed for aluminum pipe sections that were damaged from three separately simulated lunar conditions. Experiments simulating lunar regolith abrasion, internal galvanic corrosion, and irradiation were conducted on aluminum pipes. Pipes on the lunar surface will be constantly exposed to radiation, abrasion, and corrosion. As such, it is important to detect, localize, and predict damage resulting from these lunar hazards. The waveform data were clustered based on hit-driven properties. These clusters showed distinct differences between the datasets, which allowed for comparison and characterization of the data. It was found that variations in cluster shape and placement in peak, centroid, and average frequency could reliably distinguish between corrosive and abrasive processes. Understanding the differences in the data that contribute to distinctions between event types, and those that do not, will enable AE monitoring systems to better identify, characterize, and predict lunar pipeline failure.