bluuebunny/crossref_metadata_2025 huggingface dataset:
Predicting Nonlinear Seismic Response of Structural Braces Using Machine Learning
Authors: Bas Elif Ecem , Aslangil Denis , Moustafa Mohamed Aly
Published: February 2021

Numerical modeling of different structural materials that have highly nonlinear behaviors has always been a challenging problem in engineering disciplines. Experimental data is commonly used to characterize this behavior. This study aims to improve the modeling capabilities by using state of the art Machine Learning techniques, and attempts to answer several scientific questions: (i) Which ML algorithm is capable and is more efficient to learn such a complex and nonlinear problem? (ii) Is it possible to artificially reproduce structural brace seismic behavior that can represent real physics? (iii) How can our findings be extended to the different engineering problems that are driven by similar nonlinear dynamics? To answer these questions, the presented methods are validated by using experimental brace data. The paper shows that after proper data preparation, the long-short term memory (LSTM) method is highly capable of capturing the nonlinear behavior of braces. Additionally, the effects of tuning the hyperparameters on the models, such as layer numbers, neuron numbers, and the activation functions, are presented. Finally, the ability to learn nonlinear dynamics by using deep neural network algorithms and their advantages are briefly discussed.
Artificial Neural Network Based on Ensemble Empirical Mode Decomposition Features for Human Gait Diagnosis and Classification
Authors: Alkhatib Rami , Diab Mohamad O. , Corbier Christophe , El Badaoui Mohamed
Published: February 2021

Human gait analysis has been widely used to assess the stage of disease affecting the walking ability. The gait signals, namely vertical ground reaction force signals, become more unsteady and non-linear with the progress of the disease. This paper makes use of ground reaction force signals measured from both normal and subjects diagnosed with Parkinson. New features are then extracted from different intrinsic mode functions as a result of the ensemble mode decomposition. The extracted features are divided randomly into a training set of 60%, a validation set of 15% and testing set of 25%. The neural network is then employed which yield an interesting overall classification accuracy of 95.7 %. This paper will pave the way for better rehabilitative programs, understanding of gait biomechanics and fall prevention among the elderly.
Hydrogen Embrittlement Characteristics in Irradiated Stainless Steel
Authors: Khalil Sarah , Hatem Tarek M.
Published: February 2021

Hydrogen is produced in nuclear reactors as a by-product of the corrosion reaction between the pressure vessel and the cooling water, where hydrogen produced may enter the metal in atomic form. During operation a reactor vessel is exposed to avalanche of neutron irradiation fluxes, in addition to corrosion from cooling water.

A novel cluster dynamics model that accounts for off-stoichiometry of clusters and matrix was developed and applied to investigate the clustering behavior of Hydrogen-vacancy and Hydrogen-interstitial clusters in proton irradiated stainless steel has been developed. The differences in point defect migration energies and binding energy of H to lattice defects, makes it possible to have vacancy and interstitial clusters having compositions different from those of pure iron. The model predicts populations of Defect-Hydrogen complexes in iron.

The model is applied to the early stage formation of voids and dislocation loops in stainless steel in the presence of atomic hydrogen. This study investigates the effect of irradiation dose and temperature on the concentration of vacancy-Hydrogen (VmHn) and Intersitial Fe-H (FemHn) complexes on bulk α-Iron. The re
Intrinsic Mechanisms of Self-Healing in Metallic Structures
Authors: Ibrahim Mohamed A. , El-Mahallawi Iman S. , Hatem Tarek M. , Khalil Sarah
Published: February 2021

Self-healing is the ability of a material to repair damages automatically. Approaches to self-healing are separated into two major categories, those are: 1) autonomous healing methods that depend on intrinsic mechanisms, and 2) assisted healing methods that need an external intervention. Recently, computational methods have gained a wide application to study self-healing in metals using molecular dynamics (MD) and finite element (FE) methods. These methods can be used to demonstrate and optimize different metallic alloys potential to self-heal, and to further tailor these metallic structures toward improving their mechanical and fracture properties through self-healing. Computational studies of self-healing phenomenon in metals have been small in number and scope till recently. Therefore, the current paper starts with a general introduction of different mechanisms of intrinsic self-healing in metallic structures. The paper highlights previous studies using different experimental and computational approaches to explore self-healing in metallic systems, while focusing on Iron/Steel alloys. Furthermore, the paper present authors work to study self-healing and its impact on mechanical properties of Iron. Simulations are carried on bi-crystalline iron sample to investigate the effect of alloying elements diffusion on the fracture / healing properties of iron alloys and their impact on its mechanical properties. Then the effect of the alloying elements diffusion on healing is studied upon stress application after annealing. Different samples been compared to healthy samples and cracked samples without self-healing to demonstrate the effectiveness of self-healing in Iron alloys.
Blue Shift in Ultraviolet Absorption Spectra of Oxygen Doped Titanium Nitride Thin Films
Authors: Roy Manosi , Kumar Dhananjay
Published: February 2021

The objective of this study is to investigate the effect of film thickness on the bandgap of oxygen (O2)-doped titanium nitride (TiN) thin films. To accomplish this, high-quality two-dimensional O2-doped TiN films have been prepared on single-crystal sapphire substrates using a pulsed laser deposition method. The film thicknesses were varied from 3 to 100 nm by varying the number of laser pulses, while other deposition parameters are kept constant. X-ray diffraction (XRD) patterns have shown that the films grow in (111) orientation on the sapphire substrate. The increase in the intensity of the XRD (111) peak also demonstrates a better orientational alignment of the TiN films with substrate as the film thickness increases. The x-ray rocking curve has been used to measure the full width half maxima (FWHM) for each film. The FWHM values has been found to vary from 0.07 to 0.2° as the film thickness decreases. This is taken to indicate that the grain size decreases with a decrease in film thickness. Ultraviolet visible spectroscopy measurements in the wavelength range (200–800 nm) have been performed as well, which indicates an increase in the bandgap of O2-doped TiN films with a decrease in film thickness. The decrease in the film thickness leads to a blue shift of the peak in the ultraviolet-visible absorption (UV-A) region; this blueshift is accompanied by an increase in the bandgap of O2-doped TiN from 3.2 to 3.8 eV. The change in the bandgap due to a change in film thickness has been explained using the quantum confinement effect.
Functional Requirements of Data Analytic Tools and Software for Metal Additive Manufacturing
Authors: Feng Shaw C. , Moges Tesfaye , Witherell Paul W.
Published: February 2021

Additive manufacturing’s (AM’s) transition to an accepted production technology has led to increasing demands on data requirements. Many of these advances have been made possible by an increase in in-situ sensing and ex-situ measurement devices. These new devices are rapidly increasing the volume, variety, and value of AM data. The number of software tools used to measure, model, simulate, and manage AM material, part, and process is increasing to take advantage of emerging customer needs and market opportunities. However, the capabilities and accessibility of these tools, which are being used by both practitioners and researchers, vary greatly. Software tools for AM users should be able to handle ex-situ needs as well as address emerging in-situ requirements, including 1) process the different types of measured data, 2) understand defect formation, geometric variation, surface roughness, and 3) run fast enough for the layer-by-layer, scanning process. To better understand both the current capabilities and future needs, this paper provides an AM product-lifecycle landscape of software tools. The landscape includes tools for product design, design analysis, process planning, process monitoring, process modeling, process simulation, and production management. A preliminary set of functional requirements are identified, and requirements that if supported will further data analytics capabilities in AM. Furthermore, this paper identifies opportunities to develop new data-analytics tools that can improve product quality and reduce production time.
Three-Dimensional Crystal Plasticity Modelling of High-Strength Tool Steels Using Fourier Based Spectral Solver
Authors: Ibrahim Youssef , Khafagy Khaled H. , Hatem Tarek M. , Hegazi Hesham A.
Published: February 2021

Tool steels are essential for any industry, being used to cut, drill, form, shear, and shape ferrous and non-ferrous materials in bulk or powder forms. Due to the harsh service environment, tool steels are engineered with superior properties that include high wear, corrosion, and impact resistance. The macro properties of tool steel alloys are acknowledged to depend upon their fine martensitic microstructure. Therefore, accurate representation of its microstructures will help to further study its behavior which shall lead in advancing and improving their properties. In the current research, a novel microstructure generator for tool steel alloys will be used to precisely simulate complex microstructures of tool steels. The novel generating algorithm along with multiple-slip crystal plasticity based model and specialize spectral solver formulations are used to investigate high-speed tools steels behavior. The spectral method for elastoviscoplastic boundary value problems implicitly uses fast Fourier transformation algorithm (FFT) by applying periodic BCs. Both quasi-static and dynamic uniaxial tensile loading in the [010] direction is applied on a RVE of AISI H11 martensitic tool steel. Validating the numerical results with the experimental results of tool steels is presented.
Assessment of Mobile Elevating Work Platforms Risks and Review of Changes Introduced in New Industry Standards to Address These Hazards
Authors: Kennedy Eugenia , Guttag Mark , Bress Thomas
Published: February 2021

Three new industry standards, American National Standard/Scaffold & Access Industry Association, Inc. (ANSI/SAIA) A92.20, A92.22 and A92.24, were introduced in December 2018 to address the safe design, use and training associated with mobile elevating work platforms (MEWPs). MEWPs are defined in the new Standards as “machine/device intended for moving persons, tools and material to working positions, consisting of at least a work platform with controls, an extending structure and a chassis.”2 This definition encompasses certain types and sizes of MEWPs such as scissor lifts, articulating boom lifts, telescopic boom lifts and straight mast boom lifts. Replacing product-specific industry standards, these three new Standards significantly change the design, testing, rental, use and training requirements associated with MEWPs. The Standards also offer greater clarity to the assigned responsibilities associated with these functions to various entities including manufacturer, dealer, owner, user, supervisors, operator, occupants, lessor, lessee and brokers. Compliance with these voluntary standards became effective June 1, 2020.3

Recognized hazards associated with MEWPs include: tip overs associated with wind loads and contact with other obstacles, contact with power lines leading to electrocutions, caught-between the lift and other objects, falls from platform and equipment collapse.4 This effort will analyze injury data including both fatal and nonfatal incidents associated with MEWPs to better understand the trends and quantifying many of the risks associated with the operation and maintenance of MEWPs. Injury data from the U.S. Bureau of Labor and Statistics (BLS) will be reviewed. The effort will also identify and explore how the design, use and training requirements introduced in the new industry standards address the hazards and risk associated with MEWPs.
Blood Flow Velocimetry in a Microchannel During Coagulation Using PIV and wOFV
Authors: Kucukal E. , Man Y. , Gurkan U. A. , Schmidt B. E.
Published: February 2021

This article describes novel measurements of the velocity of whole blood flow in a microchannel during coagulation. The blood is imaged volumetrically using a simple optical setup involving a white light source and a microscope camera. The images are processed using PIV and wavelet-based optical flow velocimetry (wOFV), both of which use images of individual blood cells as flow tracers. Measurements of several clinically relevant parameters such as the clotting time, decay rate, and blockage ratio are computed. The high-resolution wOFV results yield highly detailed information regarding thrombus formation and corresponding flow evolution that is the first of its kind.
An Artificial Intelligence Approach to Real-Time Energy System Performance Monitoring Using Acoustic Signals
Authors: Aliati Valdir , Metghalchi Hameed , Wallace Jon
Published: February 2021

Global warming has caused an increase for more energy efficient combustion engines. Measuring the energy performance at real time may require many sensors that increase the final cost of the energy system. This paper describes the feasibility of using deep learning Artificial Intelligence (A.I.) methods to estimate energy system performance using acoustical signals. First, an audio recorder was set up to measure the acoustic signals, while taking direct measurements of an aircraft propulsion system. Then, an energy balance equation for the aircraft was calibrated, and transformed into an algorithm that calculates the Specific Total Energy (STE) in real-time by using the direct measurements recorded. The acoustic signatures were filtered out and their statistical features were used to train and test an artificial neural network that outputs the aircraft’s energy state. This process showed that it is possible to create and train models with an R2 as high as 0.99854, while avoiding overfitting; proving that it is feasible to monitor an energy system performance by using acoustic signals.
A Physics-Informed Neural Network Constitutive Model for Cross-Linked Polymers
Authors: Ghaderi Aref , Morovati Vahid , Bahrololoumi Amir , Dargazany Roozbeh
Published: February 2021

The behavior of Cross-linked Polymers in finite deformations is often characterized by nonlinear behaviour. In this paper, we propose to embed an artificial neural network (ANN) within a micro-mechanical platform and thus to enforce certain physical restrictions of an amorphous network such as directional dependency and history-dependency of the constitutive behavior of rubber-like materials during loading and unloading. Accordingly, a strain energy density function is assumed for a set of chains in each direction based on ANN and trained with experimental data set. Summation of the energies provided by ANNs in different directions can determine the strain energy density function of the matrix. Stress-strain relation is derived from strain energy density function. Polyconvexity is enforced to assure minimized potential energy, a global solution for an optimization problem, and thermodynamic consistency that show the model cannot generate energy. The model is validated against multiple sets of experimental data, e.g. uniaxial, shear, and biaxial deformation available in the literature. This model captures not only the loading and unloading paths but also the inelastic response of these materials, such as the Mullins effect and permanent set. The model can be generalized to other materials and other inelastic effects as well.
Effect of Friction on Residual Stress Distribution Induced by Split Sleeve Cold Expansion Process
Authors: Dey Mithun K. , Kim Dave , Tan Hua
Published: February 2021

Residual Stress distribution and parametric influence of friction are studied for the split sleeve cold expanded holes in Al 2024 T351 alloy, by developing a three-dimensional finite element model of the process. Fastener holes in the alloy are necessary for the manufacturing process, but they create a potential area for stress concentration, which eventually leads to fatigue under cyclic loading. Beneficial compressive residual stress distribution as a result of the split sleeve cold expansion process provides retardation against crack initiation and propagation at the critical zones near hole edges. In this parametric study, the influence of friction between contact surfaces of the split sleeve and mandrel is numerically investigated. Hole reaming process after split sleeve cold expansion is often not discussed. Without this post-processing procedure, split sleeve cold expansion is incomplete in practice, and its purpose of providing better fatigue performance is invalidated. This study presents results and an overview of the significance of friction with the consideration of the postprocessing of split sleeve cold expansion. The numerical results show that with increasing friction coefficient, compressive residual stress reduces significantly at the mandrel entry side, which makes the hole edge more vulnerable to fatigue. The different aspects of finite element modeling approaches are also discussed to present the accuracy of the prediction. Experimental residual stress observation or visual validation is expensive and time-consuming. So better numerical prediction with the transparency of the analysis design can provide critical information on the process.
Systematic Integration of Topology Optimization Techniques in Design for Additive Manufacturing Methodologies Applied to Automotive Settings
Authors: Dalpadulo Enrico , Pini Fabio , Leali Francesco
Published: February 2021

Additive Manufacturing is having a great trend since its implementation possible benefits have been widely discussed and efforts in technology improvements are having impact on process reliability and industrial application. The aims of this work are to analyze the current and forthcoming scenario of methods for the specific development of parts to be produced by metal AM including topology optimization as a basic design step and to demonstrate that systematical design approaches can be introduced in order to better exploit potentials offered by AM implementation. The general framework composed by the main tasks is introduced and discussed. Key factors such as advance in different design solutions exploration, product-related and process-related design constraint implementation in the design phase and method effectiveness in product development lead time minimization are presented. Linear and iterative workflows are described, considering features, decision making points, pros and cons, possible variants and research hints. A strong connection between methods and actual means is highlighted and workflow implementation using standard and integrated commercial tools is considered. Such methods are related to several automotive case studies presented in order to demonstrate their applicability and to show actual results and possible further development.
A Study on the Tip-Based Nanomachining of Silicon Wafer Using Atomic Force Microscopy (AFM)
Authors: Okwuashi Chionye , Ma J. , Jahan M. P.
Published: February 2021

In this work, the principles of nanoindentation and nanoscratching processes (tip-based nanomachining processes) are applied in the computational study of relevant material behaviors of single crystalline silicon wafer. Molecular dynamics (MD) simulations are carried out to model the tip-based nanomachining process of a silicon substrate by employing LAMMPS, a free MD simulation software. A spherical diamond tool tip with a radius of 8Å is considered. In the MD simulation, a single diamond indenter is treated as a rigid body. The dimensions of the silicon workpiece in the MD simulations are 500Å × 500Å × 350Å, containing 2,207,698 Si atoms. A diamond cubic lattice structure is employed to arrange these Si atoms from the very beginning at 293 K (room temperature) and a lattice constant of 5.43Å is employed. The lowest layer of the Si workpiece model is fixed and this fixed layer is one atom thick i.e. its thickness is 5.43Å. Immediately above the fixed layer is another layer of equal thickness (5.43Å), which is called thermostat layer. This layer serves the purpose of maintaining constant temperature of the system. The force-controlled approach is employed for this study. Essentially, this research evaluates the influence of three parameters: exerted force on indenter, workpiece temperature (room temperature and several higher workpiece temperatures), and indenter size on the depth of indentation, length of scratch, and coordination number of the atoms. Verlet -Velocity algorithm is used to compute the velocities and positions of the atoms. Since we desired to maintain consistency in volume, energy, and the number of particles, the constant-energy ensemble (NVE), also known as microcanonical ensemble is applied in the simulations. Both the Si-Si and C-C interactions are computed using the Tersoff potential throughout the simulations while the Si-C interactions are computed with the Morse potential. The MDS results are visualized and analyzed using OVITO, a free and commonly use visualizing tool. It is found that these parameters (exerted force on indenter, operating temperature of the silicon substrate, and size of the indenter) have substantial influence on the behavior of the silicon substrate.
Towards Deep Learning Approaches for Quantitative Analysis of High-Throughput DLD
Authors: Gioe Eric A. , Chen Xiaolin , Kim Jong-Hoon
Published: February 2021

Microfluidics has shown great promise for the sorting or separation of biological cells such as circulating tumor cells since the first studies came out a few decades ago. With recent advances in high-throughput microfluidics, analysis of massive amounts of data needs to be completed in an iterative, timely manner. However, the majority of analysis is either performed manually or through the use of superimposing multiple images to define the flow of the particles, taking a significant amount of time to complete. The objective of the work is to increase the efficiency and repeatability of particle detection in a high-throughput deterministic lateral displacement (DLD) device.

The program proposed is in the early stages of development, but shows promise. The average time it takes to analyze over a gigabyte of video is 24.21 seconds. The average percent error of the total particle detection was 21.42%. The assumptions made for the initial version of the program affect the accuracy of the particle in wall detection, so new techniques that do not follow the assumptions will need to be investigated. More work will be completed to implement machine learning or deep learning to assist in the development of DLD devices.
Ambient Temperature Effects on Battery Electric Vehicle
Authors: Liu Yiqun , Liao Y. Gene , Lai Ming-Chia
Published: February 2021

The driving range of an electric vehicle depends on the vehicle weight, road load conditions, battery capacity, and battery performance. The battery rated capacity and its characteristics could be heavily affected by the ambient temperature. This paper investigates the effects of ambient temperature on the electric vehicle driving range, equivalent fuel economy, and performance. A production-type battery electric vehicle is modeled and simulated in the AVL-Cruise platform using semi-empirical data. The modeled vehicle battery pack consists of 20Ah Lithium-Nickel-Manganese-Cobalt-Oxide (LiNiMnCoO2) cells. The battery cell characteristics are experimentally measured to build the battery pack model. The simulated driving range and equivalent fuel economy are correlated with the published information as vehicle model validation. Series of simulations on driving cycles (UDDS, HWFET, US06, and WLTP) with across a broad range of ambient temperatures are conducted to investigate the quantified effects of ambient temperature on driving range, equivalent fuel economy, and vehicle performance. Simulation results show that driving range and fuel economy are much reduced to 70% at low ambient temperature. Driving range and fuel economy are almost not affected by high ambient temperature, such as 50 C, since this model does not include accessory load of thermal management. The vehicle performance is almost not affected by the ambient temperature.
Information Model for A-UGV Performance Measurement Standard
Authors: Yoon Soocheol , Bostelman Roger
Published: February 2021

Automatic through autonomous - Unmanned Ground Vehicles (A-UGVs) have the potential to be applied over a wide range of manufacturing systems under industry 4.0 paradigm. In order to use A-UGVs efficiently in a manufacturing system, it is necessary to select the A-UGV suitable for each factory or workplace. A framework for evaluating the A-UGV performance under a specific manufacturing environment is needed. ASTM International Committee F45 has been developing standards2 providing a basis for A-UGV manufacturers and users to compare tasks to the A-UGV capabilities. This paper proposes information models for A-UGV performance measurement along the standards development. The standard needs are analyzed to show how the standard and information model can be used for the introduction of A-UGVs into factories. The information model in this paper provides a structured way to describe the factory elements affecting the A-UGV performance, and the measured A-UGV performances against the factory elements. To validate the proposed information model, an A-UGV performance testbed was built and the information model instance is developed to describe the testbed elements. An A-UGV is tested against the testbed elements and the measured performance is described by the other instance. This paper contributes to mutual understanding, between A-UGV makers and users, to deliver A-UGV performance information efficiently and to provide basis for A-UGVs to be tested under the same conditions.
Overall Temperature-Dependent Elastic Properties of Carbon Fiber Polymer Matrix Composites at High Temperatures
Authors: Konduri Teja G. K. , Zhupanska Olesya I.
Published: February 2021

In this paper we discuss the effect of volumetric ablation on the overall elastic properties of the carbon fiber reinforced polymer matrix composite. An Arrhenius type equation describing polymer decomposition was used to determine volume fractions of evolving polymer matrix phases (i.e. polymer, growing pores filled with pyrolysis gases, and char). The effect of the pressure exerted by pyrolysis gases trapped inside the pores was analyzed. Microstructures consisting of carbon fibers (circular inclusions) in the matrix and pores (elliptic inclusions) in the polymer were generated. Temperature dependency was addressed by generating microstructures with different volume fraction of pores, which were calculated from the mass loss model. Two-step numerical homogenization of representative volume elements (RVEs) was performed using finite element analysis (FEA). The developed procedures were applied to calculate temperature dependent (up to 700 K) effective elastic properties of the AS4/3501-6 composite. The results are compared to the existing experimental data and show good agreement.
Evaluation of Automotive Hood and Bumper Performance With Composite Material by Pedestrian Impactor Systems
Authors: Mohammed Obaidur Rahman , Suresh D. V. , Lankarani Hamid M.
Published: February 2021

The quality and reliability of vehicle safety in the automotive industry has greatly improved over the last years. Automotive manufacturers are constantly investigating the potential for enhancing rigidity for passenger car components and reducing the vehicle’s weight costs. Frontal elements such as the hood and bumper are more complex to analyze during the frontal collisions for weight reduction with the same rigidity and shape design. The objective of this study is to examine the crash efficiency of a vehicle’s hood and bumper with additive composite materials for enhancement of the design. The system is evaluated with the FE upper legform and adult headform impactor sub-system models. Under EEVC WG17 regulations, finite element simulations are performed with a sedan vehicle model. Analysis of the head impact on the car hood, subsequent head acceleration and HIC values were analyzed using pedestrian impactor. Differences in simulation results are examined for steel and composite material for better material selection. To estimate the weight reduction and pedestrian protection, a comparison between the composite material and the steel material is performed for the sedan vehicle. The study, thus, examines safety issues regarding the new material Carbon fiber-reinforced plastic (CFRP) for vehicle front-end and whether it would be less safe to use during frontal collision for weight reduction and comprehension behavior on pedestrian injuries.
Manufacturing and Characterization of Continuous Carbon Fiber Reinforced Polymer Composites
Authors: Choi Hongbin , Honarmandi Peyman
Published: February 2021

Additive manufacturing (AM), also popularly known as 3D-printing technology, is innovative as it manufactures a part by adding small portions of materials layer by layer, instead of removing materials from a larger bulk. This technology allows designers to create and manufacture many complex geometries. There are several AM techniques but this research focuses on fused deposition modeling (FDM) technique due to its relatively cheaper price tag and robustness. Although FDM is great at manufacturing complex geometries, the produced parts are weaker compared to the ones manufactured by the traditional techniques due to the weak layer adhesion, porous inner structure and poor surface finish. To overcome the limitations of the technology, this research was focused on reinforcing 3D-printed parts with continuous fibrous materials. In composite materials, previous studies show that among the numerous ways to inserting carbon fibers, direct insertion method yields the best results. Since none of the prior studies present standardized manufacturing and post-processing protocol, this research seeks to address this issue by developing and presenting the detailed protocols for both processes and fully characterize the created composite material experimentally. The result shows that heat-treated carbon fiber reinforced sample has exceptional increases in mechanical properties. Most notably, yield strength and resilience compared to the stock sample, increase by whopping 60% and 64% respectively.
In-Process Defect Detection for Additively Manufactured Metal Lattices
Authors: Cummings Ian T. , Jacobson Erica M. , Fickenwirth Peter H. , Flynn Eric B. , Wachtor Adam J.
Published: February 2021

The internal lattice structures common to laser powder bed fusion parts pose significant challenges for x-ray computed tomography scans, which are the current default post-process inspection technique. In this work, we present an alternative in-process inspection technique that acquires a full-field ultrasonic response measurement for each layer of the build via ultrasonic excitation of the build plate and non-contact measurement with a scanning laser Doppler vibrometer. We implement a maximum a posteriori detection technique based on those ultrasonic response measurements that can detect malformed lattice elements by combining the information available from the measurements with the ideal part geometry as encoded in the part STL file. We demonstrate successful detection of extra sintered material in the part as well as lattice strut member breakage. Further development of this inspection method in laser powder bed fusion production environments will aide qualification of latticed parts with increased confidence.
A Kriging Based Fast and Efficient Method for Defect Detection in Massive Pipelines Using Magnetic Flux Leakages
Authors: Mukherjee Subrata , Huang Xuhui , Udpa Lalita , Deng Yiming
Published: February 2021

Systems in service continue to degrade with passage of time. Pipelines are among the most common systems that wear away with usage. For public safety it is of utmost importance to monitor pipelines and detect new defects within the pipelines. Magnetic flux leakage (MFL) testing is a widely used nondestructive evaluation (NDE) technique for defect detections within the pipelines, particularly those composed of ferromagnetic materials. Pipeline inspection gauge (PIG) procedure based on line-scans or 2D-scans can collect accurate MFL readings for defect detection. However, in real world applications involving large pipe-sectors such extensive scanning techniques are extremely time consuming and costly. In this paper, we develop a fast and cheap methodology that does not need MFL readings at all the points used in traditional PIG procedures but conducts defect detection with similar accuracy. We consider an under-sampling based scheme that collects MFL at uniformly chosen random scan-points over large lattices instead of extensive PIG scans over all lattice points. Based on readings for the chosen random scan points, we use Kriging to reconstruct MFL readings over the entire pipe-sectors. Thereafter, we use thresholding-based segmentation on the reconstructed data for detecting defective areas. We demonstrate the applicability of our methodology on synthetic data generated using popular finite element models as well as on MFL data collected via laboratory experiments. In these experiments spanning a wide range of defect types, our proposed novel MFL based NDE methodology is witnessed to have operating characteristics within the acceptable threshold of PIG based traditional methods and thus provide an extremely cost-effective, fast procedure with competing error rates that can be successfully used for scanning massive pipeline sectors.
Left Ventricular Assist Device Thrombus Detection Using Wavelets and Image Classification
Authors: Reuter Steven P. , Prechtl Ian R. , Day Steven W. , Kolodziej Jason R.
Published: February 2021

Cardiac related diseases are a common health risk for adults. Consequently, therapies such as heart transplants and medication exist to treat these ailments. Heart transplants remain the gold standard for treating severe heart failure, however, left ventricular assistive devices, a cardiac blood pump, are gaining popularity and not just as a bridge for long term care. Unfortunately, with the benefits of these devices come risks of clot formation. These occlusions can cause strokes, further cardiac damage, or even death. Therefore, these occlusions must be detected at the onset. This work presents a method to non-invasively monitor the condition of a Thoratec HeartMate II ventricular device. The application of a neural network and a classification tree are designed to detect the presence of an aortic graft occlusion that has been seeded into an in vitro cardiac simulator. Using acoustic digital heart sounds, the classification tree showed the most favorable results, outperforming the existing support vector machine method by roughly 20%.
CFD Investigations on Drag Reduction due to Cavitator Heads on Supercavitating Bodies
Authors: Steppert Michael , Epple Philipp , Gast Stefan , Von Mohos Zoltan , Wortmann Martin
Published: February 2021

In the present study, a supercavitating elliptical body was investigated, using the commercial CFD Solver Star-CCM+. For the study the Reynolds Averaged Navier-Stokes Equation was solved using the realizable k-ε turbulence model. The cavitation model that was used is the Schnerr-Sauer model.

The shape of the elliptical bodies was designed according to the empirical formula of Reichardt for cavitation bubbles, which is almost an ellipse where the ratio of the semi-axes of the quasiellipse is given as a function of the cavitation number. The present studies include investigations without a cavitator at the tip and with a flat, round and conical cavitator head design at the tip. The different designs were simulated for various Reynolds numbers.

For all studies, the drag coefficient was evaluated. For better understanding the drag coefficient is split into two parts, the shear drag coefficient and the pressure drag coefficient. The results are shown in dependency of the dimensionless Reynolds number. In the simulations, the Reynolds number was changed by increasing the velocity and changing the characteristic length.
Quantifying the Dynamics of Water-CO2 Multiphase Flow in Microfluidic Porous Media Using High-Speed Micro-PIV
Authors: Li Yaofa , Blois Gianluca , Kazemifar Farzan , Christensen Kenneth T.
Published: February 2021

Multiphase flow in porous media is central to a large range of applications in the energy and environmental sectors, such as enhanced oil recovery, groundwater remediation, and geologic CO2 storage and sequestration (CCS). Herein we present an experimental study of pore-scale flow dynamics of liquid CO2 and water in two-dimensional (2D) heterogeneous porous micromodels employing high-speed microscopic particle image velocimetry (micro-PIV). This novel technique allowed us to spatially and temporally resolve the dynamics of multiphase flow of CO2 and water under reservoir-relevant conditions for varying wettabilities and thus to evaluate the impact of wettability on the observed physics and dynamics. The preliminary results show that multiphase flow of liquid CO2 and water in hydrophilic micromodels is strongly dominated by successive pore-scale burst events, resulting in velocities of two orders of magnitude larger than the bulk velocity. When the surface wettability was altered such that imbibtion takes place, capillarity and instability are significantly suppressed, leading to more compact and axi-symmetric displacement of water by liquid CO2 with generally low flow velocities. To our knowledge, this work represents the first of its kind, and will be useful for advancing our fundamental understanding and facilitating pore-scale model development and validation.
Manufacture, Modeling, and Testing of Wire-Reinforced Metallic Pressure Vessel Prototypes
Authors: Celaya-García Luis , Gutierrez-Rivera Miguel , Ledesma-Orozco Elías , Aceves Salvador M.
Published: February 2021

This article describes the manufacture, testing, and finite element modeling of prototype pressure vessels made of steel and reinforced with high-strength steel wire in the cylindrical part. Vessel prototypes were manufactured with pipe fittings and either no wire reinforcement, one layer of wire reinforcement, or two layers of wire reinforcement, with the purpose of developing an improved understanding of the effect of the wire reinforcement, and the number of reinforcement layers on prototype pressure strength. Pressure tests were conducted for instrumented vessels to determine strength up to 70 bar with a test system equipped with pressure and velocity regulators to guarantee the stability of the supplied flow and improve measurement accuracy and repeatability. Finite element modeling is conducted with the commercial code ANSYS and equivalent orthotropic properties obtained with the unit cell method, assuming a high value for the volume fraction of steel wire, and a matrix with low elastic properties compared with those of the steel wire. The results show that there is an interaction between the cylindrical part and the reinforcing wire, and that this relation is affected by external factors resulting from manufacturing process and material properties. Strain reduction in prototypes with thicker reinforcement is an indicator of the improvement on pressure resistance.
Diagram Design of Weaving Process for Touch-Feel Estimation of Plain-Woven Fabrics by Finite Element Method
Authors: Miyaki Hikaru , Sakuma Atsushi
Published: February 2021

Digital evaluation of touch-feel in textiles is useful to design fundamental functions of clothing. Here, it is necessary to design textiles for a detailed evaluation of the sensitivity in human’s feelings to consider the life-style creation in various aspects. Then, the objective of this paper is to propose a design method for plain-woven fabrics by touch-feel estimation considering the weaving process with the constitutive relations of yarn. Here, a diagram for control weaving is defined by the diameter of the yarn and displacement quantity of the weaving and the cramping by defining the theoretical thickness. For the effective design to consider various processes, unit-cell of plain-woven structures are fundamentally classified as open set models and closed set models. One of the unit-cell models in the finite element method (FEM) for the plain-woven structure is adopted because the adopted model can consider initial-stress distribution in the weaving process. For touch-feel estimation, an analysis model is constructed by warp, weft, and plungers that cramps the woven structure. A series of diagrams to compress with plungers is shown after constructing a plain-woven structure. As for analyzing the weaving process and the touch-feel estimation in one model, realization of the effective engineering is enabled. This procedure yields that the relationship between the displacement and simulation time suggests for consideration of initial-stress.
Minimizing Residual Stress in Brazed Joints by Optimizing the Brazing Thermal Profile
Authors: Mann Ben , Ford Kurtis , Neilsen Mike , Kammler Dan
Published: February 2021

Ceramic to metal brazing is a common bonding process used in many advanced systems such as automotive engines, aircraft engines, and electronics. In this study, we use optimization techniques and finite element analysis utilizing viscoplastic and thermo-elastic material models to find an optimum thermal profile for a Kovar® washer bonded to an alumina button that is typical of a tension pull test. Several active braze filler materials are included in this work. Cooling rates, annealing times, aging, and thermal profile shapes are related to specific material behaviors. Viscoplastic material models are used to represent the creep and plasticity behavior in the Kovar® and braze materials while a thermo-elastic material model is used on the alumina.

The Kovar® is particularly interesting because it has a Curie point at 435°C that creates a nonlinearity in its thermal strain and stiffness profiles. This complex behavior incentivizes the optimizer to maximize the stress above the Curie point with a fast cooling rate and then favors slow cooling rates below the Curie point to anneal the material. It is assumed that if failure occurs in these joints, it will occur in the ceramic material. Consequently, the maximum principle stress of the ceramic is minimized in the objective function. Specific details of the stress state are considered and discussed.
Tensegrity Structures Incorporating Actuator and Pseudoelastic Shape Memory Alloys
Authors: Butler Gavin M. , Peraza Hernandez Edwin A.
Published: February 2021

Tensegrity structures are networks of tensile and compressive truss members that have pre-stressability and shape-morphing capabilities. Potential applications of tensegrities in the aerospace, civil, and robotics fields require them to have actuation capabilities and adjustable stiffness. An approach to infuse these properties into tensegrities is to employ active materials. Shape memory alloys (SMAs) are active materials with the ability of exchanging mechanical and thermal energies. They have actuation capabilities enabled by the shape memory effect and large recoverable deformations enabled by the pseudoelastic effect. This paper presents a study on the integration of actuator and pseudoelastic SMAs into tensegrities to create a new class of stifftruss structures that exhibit controlled large deformations. A model for tensegrities that incorporates mechanical equilibrium, thermal equilibrium, and an SMA constitutive model is first developed. The tensile members in the tensegrities may be comprised of actuator or pseudoelastic SMA wires. The actuator wires can be manipulated through Joule heating to change the shape of the tensegrity structure on demand. The pseudoelastic wires provide high stiffness under moderate external disturbances, and become compliant and allow for large deformations as their stress is increased by the actuator wires. This unique combination of actuator and pseudoelastic SMA members in tensegrities is demonstrated through examples of controlled morphing of a tensegrity beam and a tensegrity plate. The results show that using pseudoelastic members antagonistic to the actuators, as opposed to elastic members, reduces the accumulated error and the energy required to control the tensegrities.
High-Intensity Focused Ultrasound Scattering at Mammal Vertebrae
Authors: Sanford David , Schaal Christoph
Published: February 2021

High-intensity focused ultrasound (HIFU) is used clinically to heat cells therapeutically or to destroy them through heat or cavitation. In homogeneous media, the highest wave amplitudes occur at a predictable focal region. However, HIFU is generally not used in the proximity of bones due to wave absorption and scattering. Ultrasound is passed through the skull in some clinical trials, but the complex geometry of the spine poses a greater targeting challenge and currently prohibits therapeutic ultrasound treatments near the vertebral column. This paper presents a comprehensive experimental study involving shadowgraphy and hydrophone measurements to determine the spatial distribution of pressure amplitudes from induced HIFU waves near vertebrae. First, a bone-like composite plate that is partially obstructing the induced waves is shown to break the conical HIFU form into two regions. Wave images are captured using pulsed laser shadowgraphy, and hydrophone measurements over the same region are compared to the shadowgraphy intensity plots to validate the procedure. Next, shadowgraphy is performed for an individual, clean, ex-vivo feline vertebra. The results indicate that shadowgraphy can be used to determine energy deposition patterns and to determine heating at a specific location. The latter is confirmed through additional temperature measurements. Overall, these laboratory experiments may help determine the efficacy of warming specific nerve cells within mammal vertebrae without causing damage to adjacent tissue.
Synthetic adaptive matched field processing for moving source with a horizontal line array
Authors: Li Fenghua , Zhu Feilong , Zhang Yanjun , Zhang Bo , Li Wen , Luo Wenyu
Published: February 2021

Using adaptive matched field processing (AMFP) to search for targets in shallow water is challenged by source motion. For AMFP, a relatively large number of samples is required to minimize the variance of the covariance matrix. For a fast moving target, direct integrating over a large number of data snapshots will blur the sound interference structure and, hence, degrade the ability of AMFP to produce a sharp main peak. This paper presents a source motion mitigation technique for broadband moving targets. By applying the waveguide invariant theory, the covariance matrix can be reformulated by frequency and phase shifting according to a single scalar parameter hypothesis. When the hypothesis parameter is in accordance with the true value, the moving target can be considered stationary. The technique is applied to experimental data acquired by a bottom mounted horizontal line array and demonstrates an increase in detection ranges.