bluuebunny/crossref_metadata_2025_split huggingface dataset:
Dynamic Simulations of a Cam Planar Mechanism With Impacts and Clearance
Authors: Yousuf Louay S. , Marghitu Dan B. 
Published: February 2024

Abstract
The dynamic simulation of flat-faced follower, spinning cam, and a slider with different clearances are analyzed at distinctive speeds. There is a concentric joint between the flat-faced follower and connecting rod. The follower impacts with friction the spinning cam at various contact points of the cam profile. The simulation is done using SolidWorks. The cause of the chaotic phenomenon is the impact and clearance. The impact parameters are the sliding contact velocity, kinetic coefficient of friction, contact stiffness, exponent, and coefficient of restitution. The nonlinear analysis of the midpoint of the connecting rod is considered in time delay, global, the local embedding dimensions, and Lyapunov exponent calculations. The system’s motion is examined for different coefficients of restitution and additional clearances of the slider. Phase plane and Poincaré maps are developed for nonlinear analysis.
Digital Twin Based Learning From Demonstration System for Industrial Robots
Authors: Bondarenko Yevhen , Pizzagalli Simone Luca , Kuts Vladimir , Petlenkov Eduard , Otto Tauno 
Published: February 2024

Abstract
Learning from Demonstration (LfD) is an approach to robot programming where the machine aims to replicate the task presented by a human without being explicitly programmed to execute this task. While being an effective way to create complex robot routines even for users without coding skills, most LfD implementations heavily rely on sensors for the robot to capture the state of the surrounding world and the task being demonstrated.
In this research paper we offer an alternative LfD approach based on a fully simulated 3D environment. We demonstreate how simulation can eliminate the need for real-life sensors on the robot, serve as a unified medium for recording demonstrated tasks, and facilitate sharing of the produced solutions between different types of robotic cells with minimal to no reconfiguration. A Virtual Reality interface allows the operator to interact with the LfD environment in a natural way when recording task demonstrations for the robot. The system is built on top of commonly available software such as Unity engine, Robot Operating System, ROS-Industrial and MoveIt motion planning framework. It will be published on the public GitHub page of TalTech IVAR Lab [1].
We also provide a simple experimental procedure to validate the system’s ability to generate programs for multiple robot models from a single task demonstration, demonstrating its flexibility and ease of use for the operator.
Fabrication of Ultra-High Aspect Ratio Array Structures Using Spontaneous Evolution in Multiport Lifted Hele-Shaw Cell
Authors: Rakshe Makrand A. , Gandhi Prasanna S. 
Published: February 2024

Abstract
High aspect ratio (HAR) micro/meso-well array structures are useful in biomedical devices such as spheroid formation, drug-releasing patches, and cell-trapping. Nature-inspired honeycomb structures with high aspect ratio walls have the potential for such applications. This paper demonstrates the lithography-less, spontaneous, scalable fabrication of an ultra-high aspect ratio structure in polymer by disciplining Saffman-Taylor fluid-fluid interface instability. The fabrication process includes (i) rearrangement of fluid in the array during the initial stage of the plate separation due to the evolution of air fingers without splitting because of controlled Saffman-Taylor instability, (ii) subsequent pinning of the fluid boundary due to evaporation of the solvent from the polymer solution (iii) stretching of fluid due to continuous increment in the viscosity of the volatile polymer solution, and finally (iv) solidification due to volatility of the fluid. With the proposed method, we successfully demonstrated the fabrication of ultra-high aspect ratio (in the range of 100–200) structures with less than 10-micron wall thickness.
Improved Six Rules-Based Fuzzy Compensation for Time-Varying Control Cycle in Active Powertrain Oscillation Reduction
Authors: Yonezawa Heisei , Yonezawa Ansei , Kajiwara Itsuro 
Published: February 2024

Abstract
To prevent the degradations of comfort and drivability, this research presents an improved fuzzy-logic-based active oscillation control algorithm for an automotive powertrain with a time-fluctuated control cycle limitation. A sampled-data controller is firstly employed as the base control, and a model-predictive algorithm is implemented to compute the optimal control signals at a periodic time interval. However, the update timing of the real control input fluctuates over time due to the powertrain actuator limitation. The fuzzy reasoning is applied to tackle the time-fluctuated control cycles. In the present method, the update timing is translated into some fuzzy sets. Moreover, the periodic control signals by the sampled-data controller are combined as fuzziness, resulting in linguistic fuzzy sets. Six fuzzy rules are created based on those fuzzy sets to derive feasible control inputs at the fluctuated updating timings. The control strategy is tested via simulations with a powertrain oscillation model. The compensation effect is observed for the two patterns of the fluctuated control cycles. In addition, the test results confirm the improvement of the oscillation reduction over a conventional control algorithm.
Robust Contact Modeling in Non-Rigid Variation Simulation
Authors: Sadeghi Tabar Roham , Lorin Samuel , Lindkvist Lars , Wärmefjord Kristina , Söderberg Rikard 
Published: February 2024

Abstract
Geometric variation is an inevitable element of any fabrication process. To secure the geometric quality of the assembled products, variation simulation is performed, approximating the statistical moments of the geometric variation, thereby controlling compliance with the set geometric requirements. Contact modeling involves describing the behavior of contacting bodies. In non-rigid variation simulation, contact modeling is used to avoid the virtual penetration of the components in the adjacent areas, enhancing the simulation accuracy. Variation simulation and contact modeling is based on the Method of Influence coefficients (MIC). Compared to the traditional MIC, the contact model imposes non-linear behavior on the deformation calculation. In this approach, the MIC matrix is utilized as the reduced stiffness matrix in the contact model. For frictionless contact models, numerical errors and convergence issues due to the deformation behavior of the interacting surfaces are still limiting the computational efficiency of solving this optimization problem. The optimization problem associated with a contact model is often large-scale, and in practice, fast and robust methods for achieving convergence are required. Previous implementations of contact modeling for non-rigid variation simulation have been prominently based on the Iterative or Penalty Methods, and Lagrangian Multipliers Method through Interior Point Solvers. In this paper, a quadratic programming approach has been introduced based on the Lagrangian multiplier method for robust contact modeling in non-rigid variation simulation, and the performance of the proposed approach has been compared to the previously applied Iterative and Interior Point Method. The methods have been compared on three industrial reference cases, and the convergence, and time-efficiency of each method are compared. The reference models have been chosen from different problem sizes, small, medium, and large-scale. The results show that robust optimization of the quadratic program associated with the contact model is highly dependent on the assemblies’ condition, composed of the reduced stiffness matrix condition. Furthermore, it has been shown that robust and efficient contact modeling in non-rigid variation simulation is achievable through the proposed quadratic programming method. Improvement in the convergence is achieved while maintaining the accuracy level to the predefined tolerance intervals.
Digital Twin Approach to Support Preventative Maintenance in a Robotic Application
Authors: Clounie Joshua , Guerra-Zubiaga David A. , Voicu Razvan Cristian 
Published: February 2024

Abstract
The cost-effectiveness and efficiency of any manufacturing system are now more critical than ever. Therefore the embracing of Industry 4.0 is underway. Interconnectedness and smart automation with the integration of the Manufacturing Execution System (MES) are the key concepts of Industry 4.0. The fast-paced industrialization 4.0 requires a good insight into existing concepts and methods. There are many ideas and concepts that are integrated into the advancement of Industry 4.0. This paper explores the integration of preventive maintenance concepts and methods used by the fourth industrial revolution, ideas like the Industrial Internet of Things (IIoT), Cloud Manufacturing (CM), Machine Learning (ML), Product Lifecycle Management (PLM), Digital Manufacturing (DM), and Digital Twins (DT). This paper will introduce additional concepts into the conference paper, Intelligent Process Control Following Industry 4.0 Trends, with Siemens PLM product, Tecnomatix.
Finite Element Analysis of Brain Damage Induced by the Impact of Hit-by-Pitch
Authors: Shimomura Kohei , Terai Motoharu , Sakuma Atsushi , Zhang Yuelin 
Published: February 2024

Abstract
Concussion due to hit-by-pitch can be life-threatening in baseball, and it requires effective countermeasures. Concussion lesions are microscopic and imaging diagnosis is generally difficult. Recently numerical modeling of brain injury mechanics has been proposed. This study aims to quantitatively analyze and evaluate the effect of helmets on head injury using numerical analysis of brain injury models. First, in the analysis under the condition of direct impact of the ball on the head, residual strain corresponding to damage occurred near the collision and center points of the brain. This result was used as a criterion for evaluation of brain injury. Then, the effect of ball rotation was observed, and the effect was relatively small. Furthermore, in case of impact with helmet on, residual strain was smaller near the collision point, larger near the center point when the helmet was fixed. The residual strain was smaller for impacts to temporal region than to parietal region under conditions of different collision positions. The residual strain was smaller than that of the fixed helmet regardless of collision position when the helmet was not fixed. The results indicate that the effect of the ball rotation on brain damage at hit-by-pitch is small, and that the impact position and fixation of the helmet have a relatively significant effect.
Identification Method of Constitutive Material Parameters for Additively Manufactured Structures Using an Inverse Optimization Strategy
Authors: Andrikopoulos Konstantinos-Ioannis , Voerakos Georgios , Tsainis Andreas Marios , Papazafeiropoulos George , Stergiou Constantinos , Vairis Achilles 
Published: February 2024

Abstract
The parameters of a constitutive model of a fused deposition modeling (FDM) additively manufactured part were calculated with a novel computational tool of inverse optimization. The finite element (FE) model was used to calibrate the printed part’s properties, incorporates a composite shell section of linear elastic orthotopic material under plane stress, various design configurations following a Taguchi L18 orthogonal array, were modelled in Abaqus CAE 2021 following the classical laminate theory (CLT). The material studied was PLA. The plastic mechanical behavior was taken into account with various stress-based failure criteria normally used in fiber-reinforced composite materials. Initially an investigation based on experiments of 3D printed FDM specimens determined stress-strain curves for various types of geometry and loading. Then, inverse optimization was performed with the interior point (IP) method, which calculates material parameters of the constitutive model of the parts. It was shown that the experimental response of 3D printed FDM processed parts can be estimated accurately numerically with material parameters calculated with the new inverse optimization tool proposed.
Histological Study of Tissue Damage due to Composite-Coated Needle Insertion
Authors: Patel Kavi , Hutapea Parsaoran 
Published: February 2024

Abstract
Reducing the force required during needle insertion is vital to minimize tissue damage in percutaneous procedures. A composite coating of Polydopamine, Polytetrafluoroethylene, and Activated Carbon materials was applied to the needles to address this challenge. The coating reduces needle surface friction, which eventually helps to decrease the insertion force and minimize tissue damage. In this study, measuring the insertion and extraction forces inside a bovine kidney showed that the coated needles decreased the insertion force by 49% and the extraction force by 30%. In addition, a histological analysis was conducted to compare the tissue damage caused by coated and bare needles. The results revealed that coated needles insertion reduced tissue damage by 39.6% compared to bare needles. These findings highlight the potential of this composite coating approach to improve the safety and precision of percutaneous procedures.
Wake Dynamics of Complex Turning Vanes Using Time-Resolved Particle Image Velocimetry Measurements
Authors: Hayden Andrew P. , Gillespie John , Hefner Cole , Lowe Todd , Untaroiu Alexandrina 
Published: February 2024

Abstract
The use of turning vanes spans multiple engineering disciplines such as aerospace, ocean, and biomedical to effectively turn an otherwise uniform flowfield and achieve desired downstream flow angles. The presented work investigates the wake dynamics generated by sets of complex turning vanes which contained nonaxisymmetric geometries, spanwise variations in turning angle, and multiple vane junctions. Time-resolved particle image velocimetry (TR-PIV) measurements were performed to collect three-component velocity data downstream of the vane pack geometries. As the vanes contained blunt trailing edges, large-scale periodic structures (von Kármán vortices) dominated the unsteady wakes. Two post-processing methods, Proper Orthogonal Decomposition (POD) and Dynamic Mode Decomposition (DMD), were employed to extract the wake energy or enstrophy content, corresponding spatial modes, and associated frequencies. This was completed for various parameters such as Reynolds number, vane turning angle, and vane trailing edge thickness. Spatial POD analyses showed that zero-turning vanes contained similar dynamics to that of a circular cylinder, and the total wake energy distributions were affected by freestream velocity. A spectral POD analysis in the wake of vane junctions found that the junction flow contained significant coherent content, and gave some insight into the mean flow. Lastly, vane parameters such as turning angle and TE thickness were found to play a large role in modifying the enstrophy content of the large-scale shedding modes.
Finite Element Analysis and Process Parameters Optimization of AA2024 – T351 Alloy Machining Under Different Cooling Environments
Authors: Pervaiz Salman , Kannan Sathish , Ali Shafahat 
Published: February 2024

Abstract
Aluminum alloys are popular in the industrial applications after steel and cast iron. The lower strength of Aluminum alloys can be improved by the addition of alloying elements and heat treatment related operations. AA 2024 – T351 is the alloy of aluminum with copper, and in addition tempering and stress relieving is performed as well to improve the strength related characteristics. AA2024-T351 is a high-strength aluminum alloy that has a combination of properties that make it particularly well-suited for use in aerospace and aircraft structural components. It is relatively lightweight, which makes it an attractive material for aircraft design, but it is also exceptionally strong and can withstand high stresses and loads. This combination of strength and lightness is particularly important in aerospace applications where weight is a critical factor. In addition to its strength and weight properties, AA2024-T351 is also highly resistant to corrosion. This makes it an ideal material for aircraft structures, which are exposed to a range of environmental conditions, including high altitude, extreme temperatures, and exposure to moisture and chemicals. There are several machining related challenges available when it comes to the machining performance of aluminum alloys. These challenges are linked with the chip formation due to sticky nature of material, strain hardening behavior and low thermal conductivity. Aluminum alloys can be difficult to machine due to the formation of long, stringy chips that can clog or damage cutting tools. This is because aluminum has a tendency to adhere to the cutting tool, which can lead to built-up edges and poor chip evacuation. Machining performance can be enhanced by the application of cutting fluids. The machining of AA2024-T351 can be carried out using various cooling methods, including dry, flood, and cryogenic cooling. Each of these methods has its advantages and disadvantages, and the choice of cooling method depends on various factors, such as the machining process parameters, tooling material and geometry, and workpiece material properties etc. The current study investigated the machining performance under the influence of different cooling environments such as dry, flood and liquid nitrogen based cryogenic. In this work, finite element based numerical modeling has been utilized to capture the behavior of machining AA2024-T351. The study varies cooling method, cutting speeds and feed levels using Taguchi design of experiments. The output responses of cutting forces, cutting temperature, cutting power were calculated. The findings were found in good agreement with the experimental data available in the literature.
Multiphysics-Informed Machine Learning for Mechanical-Induced Degradation of Silicon Anode
Authors: Bansal Parth , Li Yumeng 
Published: February 2024

Abstract
Silicon (Si) anode based lithium-ion batteries (LIBs) are being developed and used in various portable electronic technologies because of their better life cycle performance and safety. These Si anode based LIBs also provide a better capacity due to the unique intercalating mechanisms of lithium (Li) into Si. However, due to this unique mechanism, volumetric changes upto 300% have been observed in these batteries that leads to the development of internal stresses in the Si anode which ultimately results in cracking and delamination in it. These two cracking and delamination failure modes along with the growth of the solid electrolyte interface (SEI) on the exposed surface of Si anode leads to loss in the overall capacity of the battery. The capacity degradation can be simulated using FE models but these models take a long time to run and are computationally expensive. Hence, in this study, we develop a physics-informed machine learning technique for the capacity degradation of the Si anode based LIBs. 3D finite element (FE) models are built to understand the volumetric stresses induced cracking and delamination along with the capacity loss due to the growth of the SEI layer. The outputs from these FE models are then used to train the Gaussian process regression (GPR) surrogate model which can be used for the design of LIBs towards application-oriented properties such as high energy storage, fast charging or optimal life time by quickly and accurately predicting the capacity degradation in the battery.
Materials Characterization of Recycled and Mixed Acrylonitrile Butadiene Styrene and Polylactic Acid for Use in Additive Manufacturing
Authors: Sang David , Llantero Parker , Eckstein Adam , Nguyen Chi , Nowicki Margaret , McDonald Kenneth 
Published: February 2024

Abstract
This work examines the physical and mechanical properties of composite filaments derived from acrylonitrile butadiene styrene (ABS) and polylactic acid (PLA) using thermogravimetric analysis (TGA), differential scanning calorimetry (DSC) and tensile testing. Composites made up of 0% to 100% in 25% intervals of ABS and PLA were prepared through a filament extrusion process. The composites were characterized using TGA and DSC with unprocessed ABS and PLA for comparison. To model the additive manufacturing and recycling process, each sample was exposed to heating and cooling cycles during DSC measurements. The 25% ABS and 75% PLA composite samples were determined to have the highest crystalline content based on the highest melting temperature. DSC data also indicated that the crystallinity of the materials degraded as glass transition and melting temperatures shifted higher with each repeated heating and cooling cycle. A tensile testing of the composite filaments was performed using a universal testing machine. The samples were pulled with tabs constructed from duct tape. From this analysis, the best-performing mixture was 50% ABS and 50% PLA. This data was compared against the DSC data to correlate the strength of the composites to the crystalline region after several recycling iterations.
Construction of a Strain-Based Bayesian Network for Assessing Pipeline Risk due to Ground Movement
Authors: Schell Colin A. , Lever Ernest , Groth Katrina M. 
Published: February 2024

Abstract
Ground movement events pose a significant threat to buried natural gas and oil pipelines which resulted in an estimated $388 million in damages from 2002 to 2022. Strain-based design and assessment (SBDA) methods are commonly used to manage pipeline integrity in ground movement scenarios but utility companies still face many challenges in applying SBDA to pipeline integrity management. This paper presents the development process of a Bayesian network structure using SBDA methods for managing pipeline integrity in the event of ground movement hazards such as landslides and ground subsidence. The Bayesian network model proposed in this work presents a first-of-its-kind approach to: (1) integrate the multiple risk factors and data sources required to model SBDA and assess pipeline risk; (2) identify the sources of uncertainty that affect pipeline risk estimates; and (3) provide a holistic tool for addressing pipeline risks stemming from ground movement events. Quantification and performance validation of the Bayesian network is an ongoing process, but the model is expected to utilize satellite-based ground movement data, infield strain measurements, in-line inspection defect data, strain accumulation models, metallurgical data, and knowledge of the past loading envelope of the pipeline to perform a quantitative risk assessment for a network of pipelines. In this paper, a novel SBDA taxonomy is presented, analytical strain capacity equations are selected for future use, and the overall model architecture is developed to support the creation of a comprehensive and robust model.
Teaching the Reachable and Dexterous Workspace of Articulated Robotic Manipulators Using MATLAB Animation
Authors: Kutzer Michael D. M. , Rodriguez-Seda Erick 
Published: February 2024

Abstract
In the context of articulated robotic manipulation, reachable and dexterous workspace define regions of space that can be reached by the end-effector or tool center point (TCP) of a robot. This paper presents an approach to teaching the concepts of reachable and dexterous workspace using MATLAB animations. These methods rely on existing MATLAB tools which limit the visualization to two dimensions (2D). Despite this limitation, these tools have produced a qualitative improvement in student understanding. This paper details the visualization approach used including a review of core concepts and algorithms. Results show reachable and dexterous workspace visualizations for three degree-of-freedom (DoF), planar articulated manipulators consisting of various configurations of revolute (rotational) and prismatic (linear) joints. Source code, documentation, and installation functions for the “Visualize Workspace Toolbox” are available at https://github.com/USNA-WRCE/VisualizeWorkspaceToolbox.