bluuebunny/crossref_metadata_2025_split huggingface dataset:
An Application of the Stability Circle to the Design of MEMS Technology Based Systems With Floating Center Rotary Comb Drives
Authors: Belfiore Nicola Pio , Coppola Serena , Muccichini Alessandro , Piroddi Cristiano , Verotti Matteo 
Published: November 2024

Abstract
Based on the classic plane rigid motion theory, this article suggests how to use a well-known locus of points, namely, the inflection (or stability) circle, with specific second order path curvature characteristics, to optimize the positioning of a so-called floating center rotary comb drive (FCRCD) that could be employed in any microelectromechanical system (MEMS) with the aim of improving the actuation force or torque. In the first part of the paper, the new concept FCRCD is briefly described, by underlying its efficacy to improve the actuating torque in the embedding microsystem. In fact, FCRCD actuators have been conceived to increase the available input power that provides motion. The second part of the paper will describe how the inflection circle can be used to detect the most feasible or unfeasible zones where to place the FCRCD in the microsystem. Being only at the second order of the plane curve theory, the inflection circle is considered rather elementary a tool. Nevertheless, this paper will show how such fundamental locus of the plane will be used to identify the critical region where a FCRCD should not be placed. The adopted method recalls very much the reasons why the inflection circle has been also referred to as stability circle.
Monitoring a Pseudouridine 5’-Monophosphate Synthesis Process Using an Interdigitated MEMS Sensor and Electrochemical Impedance Spectroscopy (EIS)
Authors: Zamiri Athena , Sohrabi Neaman , Raasch Kylie , Sumita Mina , Shavezipur Mohammad 
Published: November 2024

Abstract
This work presents a new method to monitor a biochemical enzymatic reaction, the synthesis of pseudouridine 5’-monophosphate (ΨMP) from uracil and ribose 5’-phosphate (R5P). Pseudouridine is a precursor of an important component, N1-methyl pseudouridine in mRNA-based vaccines. Conventional methods of monitoring the process such as thin-layered chromatography (TLC) and high-performance liquid chromatography (HPLC) are expensive and cumbersome. Here, we propose the use of an interdigitated MEMS sensor and electrochemical impedance spectroscopy (EIS) to monitor the progression of the synthesis process. First, we investigate the electrochemical properties of uracil, R5P, and ΨMP. The measurement results show that R5P and ΨMP have relatively similar Nyquist responses, while uracil shows much higher impedance values. Then, we prepared five samples that mimic the actual process where the concentrations of uracil and R5P gradually decrease in the samples and the concentration of ΨMP increases. The EIS test results show that the MEMS sensor and test setup can capture the change in the composition of the samples as the electrochemical properties of the sample solution changes. The method presented in this work can be used in a microfluidic system that is integrated with the synthesis process to monitor the progression and completion of the process in real time.
An Experimental Investigation on the Effect Operating Procedure Has on Tribometer Measurements
Authors: Ngo Anthony , Handschuh Michael 
Published: November 2024

Abstract
Tribological investigations are typically conducted on measurement devices coined tribometers. These simple yet complex measurement devices can be used to evaluate several distinct contact characteristics including friction, wear, heat induced failure modes, etc. The major benefit of using a tribometer is to simulate all the characteristics of a complicated contact mechanics problem with simple machine elements. Related to power transmission, tribometers are often used to produce heuristic traction curves covering operating conditions seen in real world applications of gears and bearings. This study provides insight into the effect operating procedure has on the results of traction (friction) tests using a twin-disk tribometer.
Two sets of traction disks of different surface roughness amplitude are tested for their frictional performance behavior based on operating procedure. One set of disks paired two super-finished specimens against each other, simulating gear contacts typically seen in the aerospace industry. The second set of disks was a combination of a super-finished and as-ground disk, more representative of contact conditions seen in automotive powertrains. Measured deviations based on operating procedure clearly show an impact on frictional performance. For smoother surfaces, undesirable losses contribute more significantly to measured friction. Rough surfaces generate more heat and take much longer to reach steady-state. Misrepresentation of friction measurements can occur if test duration is not long enough. In either case, improper execution of traction tests will certainly alter the accuracy of the measurements.
Since tribometer measurements can be used to develop regressed friction formulae or to directly compare elasto-hydrodynamic (EHL) model predictions, it is crucial that operating procedure does not influence contact characterization.
Monitoring Methods for Metareasoning Approaches for Recovering From Path Planning Failures on Mobile Ground Robots
Authors: Molnar Sidney L. , Herrmann Jeffrey W. 
Published: November 2024

Abstract
The planning algorithms that autonomous systems use sometimes fail to generate effective solutions, which prevents mission success. Metareasoning, which is “thinking about thinking,” is one way to mitigate planning failures. To give an autonomous robot the ability to recover from these failures, we implemented a novel metareasoning approach in the ARL ground autonomy stack known as Phoenix. The metareasoning approach controls the global and local path planners in that stack by stopping and restarting the associated ROS nodes. In any metareasoning policy, there are methods of control and methods of monitoring. This study investigates the implementation of a meta-level that uses select monitoring methods that identify path planning failures for autonomous ground robots. The two monitoring methods proposed are the fixed radius methods, which are based on the mission goals, and the expected progress methods, which are based on the robot’s movement capabilities. In this study, we evaluated how using these methods affected navigation performance for the path planning algorithms that are in the ARL ground autonomy stack. The results of our simulation study showed that some policies helped the robot recover from path planning problems, and the robot could get closer to its final goal. In other cases, however, the metareasoning policies disrupted the robot’s navigation process and degraded performance. Additional research is needed to understand when metareasoning policies can improve performance.
Predicting Human Context-Aware Action Modifications for AI Assistance in Visually Demanding Tasks
Authors: Dalland Kristian , Distefano Joseph P. , Esfahani Ehsan T. 
Published: November 2024

Abstract
For a plethora of humanitarian and commercial applications such as medical imaging, air traffic control, driving, and supervisory control, artificial intelligence is assisting humans in increasing performance in visually demanding tasks. To design an effective AI team, AI must provide the proper level of assistance predicated on human cognition and performance. To achieve this, it is essential to monitor and predict human performance in real-time rather than relying solely on overall task performance. Reaction and decision time can be utilized to predict the mental workload of humans in visually demanding tasks. In this paper, we utilize the benchmark Atari environment to remove domain expertise and provide emphasis on context-aware human decision-making. We extract connected component labeling features from the environment and human eye gaze to predict when a human performs a context-aware action modification for both frame-by-frame and time-domain applications. We identify specific environmental scenarios where eye gaze provides a wealth of information and increases real-time workload classification. The classification results are analyzed for specificity, sensitivity, and F-score to illustrate the mitigation of misclassified information. The results demonstrate the effectiveness of utilizing environment features in combination with eye gaze to predict when the human needs AI assistance during a visually demanding task.
Improvements to a Novel Autonomous Personal Rapid Transit (PRT) Prototype Vehicle
Authors: Hariharan Deveshwar , Patil Nikhil , Issa Jamal , Hollar Seth 
Published: November 2024

Abstract
The North Carolina State University research team is exploring the future of urban mobility through the EcoPRT project, which focuses on autonomous micro-transit systems. In this paper, the team presents the latest generation of prototype vehicles developed to improve the previous mechanical and software designs. A brief design review of the old prototype vehicle is presented, and the limitations of this design are addressed with the proposed solutions applied in the new prototype vehicles. The new prototypes are constructed to be lightweight with a lower center of gravity. They have custom-designed steering for improved turning capability. The suspension components are also optimized for additional ride comfort and lateral stability. Lastly, a modified spring-loaded braking system has been developed for precise control over braking force. Additionally, the team has integrated advanced IMUs, GNSS-RTK, stereo cameras, and LiDARs to upgrade the system. The control software framework has been migrated to the state-of-the-art Robot Operating System (ROS), enabling the team to leverage the new hardware and improve localization, perception, path following, obstacle detection and recognition, and navigation capabilities. The paper also describes the different simulation tools available, such as Gazebo, Simulink, and CARLA, which work with ROS and their respective advantages and limitations in simulating various aspects of vehicle development. This paper provides a glimpse of the future of autonomous micro-transit systems, which are safer, more efficient, and reliable than ever before.
Experimental Study on Dynamic Characteristics of a Tensegrity Structure Using a 3D SLDV System
Authors: Yuan Ke , Yuan Sichen , Zhu Weidong 
Published: November 2024

Abstract
Tensegrity structures have emerged as important components of various engineering structures due to their high stiffness, light weight, and deployable capability. Existing studies on dynamic analyses of tensegrity structures mainly focus on responses of their nodal points while overlook deformations of their cable and strut members. This study aims to propose a non-contact approach for experimental modal analysis of a tensegrity structure to identify its three-dimensional (3D) natural frequencies and full-field mode shapes, which include modes with deformations of its cable and strut members. A 3D scanning laser Doppler vibrometer (SLDV) is used with a mirror for extending its field of view to measure full-field vibration of a three-strut tensegrity column with free boundaries. Tensions and axial stiffnesses of cable members of the tensegrity column are determined using natural frequencies of their transverse and longitudinal modes, respectively, and used to build a numerical model of the tensegrity column for dynamic analysis and model validation purposes. Modal assurance criterion (MAC) values between experimental and numerical mode shapes are used to identify their paired modes. Natural frequencies and mode shapes of the first 15 elastic modes of the tensegrity column are identified from the experiment, which include modes of the overall structure and its cable members. These identified modes can be classified into five mode groups depending on their types. Five modes are paired between experimental and numerical results with MAC values larger than 78%. Differences between natural frequencies of paired modes of the tensegrity column are less than 15%. The non-contact 3D vibration measurement approach presented in this work can measure responses of nodal points, as well as deformations of cable and strut members, of the tensegrity column, and allows accurate estimation of its 3D full-field modal parameters.
Multi-Scale Model Predictive Control for Laser Powder Bed Fusion Additive Manufacturing
Authors: Hong Gisuk , Kim Jaehyuk , Yang Zhuo , Lu Yan , Lane Brandon , Yeung Ho 
Published: November 2024

Abstract
Additive manufacturing (AM) process stability is critical for ensuring part quality. Model Predictive Control (MPC) has been widely recognized as a robust technology for controlling manufacturing processes across various industries. Despite its widespread use, there has been limited exploration into the application of real-time MPC for controlling the laser powder bed fusion (LPBF) AM process through in-situ process monitoring. This paper presents a new framework designed to develop MPC strategies for real-time LPBF control. The framework accommodates various multi-scale approaches, including pointwise, trackwise, layerwise, and partwise methods. This framework considers the diverse needs for material state representation when formulating predictive models, constraints, and objective functions while allowing for predictive control implementation at different scales and frequencies. The utility of this framework is demonstrated through three trackwise MPC case studies, all employing high-speed co-axial melt pool imaging. Simulation results indicate that LPBF systems enhanced with MPC achieve superior performance compared to those governed by open-loop control systems. Additionally, we find that MPC implementations that utilize feedback control at finer scales provide improved process stability, albeit at the expense of increased computational demands. This framework serves as a guide for industrial practitioners, outlining how the implementation of MPC in AM process control can be optimized based on available in-situ sensing capabilities and data acquisition techniques.
Modeling Demand Spikes in Continuously Evolving Products Using Dynamic Customer Preferences
Authors: Walter Ian , Paré Philip E. , Panchal Jitesh H. 
Published: November 2024

Abstract
As the digital marketplace continues to evolve with rapid changes in consumer preferences and the frequent introduction of product updates, traditional models for estimating demand are often unsuitable for use. These models often fail to account for the dynamic nature of product development and the complex decision-making processes of consumers, particularly in the face of product updates. To bridge this gap, this study extends a demand model based on decision field theory (DFT), focusing on a phenomenon largely unexplored in the existing literature: the update engagement spike (UES). Being able to capture this phenomenon, where demand surges following a product update, is crucial for accurately modeling demand around product updates. We extend the DFT-based demand model to capture consumer responses to updates more effectively. We then validate this extended model using real-world data from competing software products, demonstrating its practical use. Furthermore, we outline avenues for future research, including methods for distinguishing between customer segments and attributes with greater accuracy.
Augmenting Bayesian Inference-Based Damage Diagnostics of Miter Gates Based on Image Translation
Authors: Zeng Yichao , Zeng Jice , Todd Michael D. , Hu Zhen 
Published: November 2024

Abstract
Structural monitoring data is essential for the assessment of structural integrity of large civil infrastructures, such as miter gates studied in this paper. For some applications, the amount of monitoring data is limited due to various reasons including but not limited to the availability of sensors, changing states of structural health, inaccessibility of measurement location, etc.. This limited amount of monitoring data could result in uncertainty in structural health assessment. This paper overcomes this challenging issue by proposing a data augmentation method based on image translation for Bayesian inference-based damage diagnostics of miter gates. In particular, we translate the monitoring data of one miter gate to that of another, thereby increasing the volume of monitoring data available for assessing the structural health of a target miter gate. This translation starts with converting the monitoring data of different miter gates into images. After that, Cycle Generative Adversarial Networks (CycleGAN) are employed to accomplish the task of image translation among different miter gates. The translated images (i.e., synthetic monitoring data) are then used together with the true monitoring data for damage diagnostics. Two types of CycleGAN architectures are investigated and compared using a case study. Results of the case study show that the proposed data augmentation method can effectively improve the accuracy and confidence of damage diagnostics of miter gates. It demonstrates the potential of integrating synthetic data generation with probabilistic model updating in structural health monitoring.
Design of a Hollow Flexible Hinge for an Atmospheric Dispersion Corrector
Authors: Kamphof Wilbert E. , Nijenhuis Marijn , Kragt Jan W. , Lewińska-Maresca Mirka A. , Brouwer Dannis M. 
Published: November 2024

Abstract
Compliant mechanisms often have the advantage of high precision at the cost of a limited range of motion. In this research, a single Degree of Freedom hollow compliant hinge is developed for use in an Atmospheric Dispersion Corrector (ADC) found in the Extremely Large Telescope (ELT). The ADC contains two sets of optical prisms to correct for the unwanted dispersion effect that occurs when light meets the atmosphere at an angle. These prisms must perform a near-pure rotation around the optical axis, which must not be obstructed by the geometry of the ADC. This study strives to achieve the required deflection range while having sufficiently high unwanted eigenfrequencies, support stiffness, and low stress levels while bearing a relatively large load. The eigenfrequencies should be sufficiently high because of the vibrations originating from underlying systems in the ELT. This paper presents a comparison of various concepts, optimized with 3D nonlinear numerical finite element and multibody analysis software. Both high kinematic and dynamic performance is achieved through the addition of a kinematic coupler, or synchronizer. The existing concept adapted to the requirements of this work is called the Large Angle Flexure Pivot (LAFP), which is analyzed and expanded upon with a newly developed synchronizer to align with the requirements of the ADC.
Investigating Sustainable Production Planning of an Industrial Progressive Die Stamping Process Using Discrete Event Simulation
Authors: Borbolla Adrián , Aboujarra Mohamed Nezar , Ramanujan Devarajan 
Published: November 2024

Abstract
This paper investigates the use of discrete event simulation (DES) as a potential decision-support tool for environmentally sustainable production planning. We propose a comprehensive methodology that integrates DES design, dynamic CO2 intensity calculations, and in-depth scenario analysis. This approach facilitates simulation-driven production planning with an emphasis on evaluating and reducing the environmental impacts of manufacturing. Our methodology is applied to the evaluation of an industrial progressive die stamping process (PDSP). The PDSP is a part of a production line in a large Danish manufacturing company. The performance of the PDSP was simulated and evaluated for two working days: 09 Jan 2024 and 10 Jan 2024. Key performance indicators like utilization rate and lead time are assessed alongside environmental sustainability metrics such as process CO2 emissions and CO2 emissions per product. Building upon this baseline, we then simulate and compare two alternative production scenarios: i) increased production capacity and ii) variations in planning orders. These scenarios are then contrasted with the baseline model. Our results demonstrate the potential of DES in identifying new production strategies. The first scenario demonstrates a 7% increase in production capacity, corresponding to a 2.6% increase in process CO2 emissions. The second scenario demonstrates a 5% reduction in process CO2 emissions while retaining the same production capacity as the baseline model. These findings illustrate how DES can uncover production plans that prioritize sustainable manufacturing.
Design and Optimization of a Long Travel, Two-Axis Flexural Nanopositioning Stage
Authors: Watts Tyler , Cullinan Michael 
Published: November 2024

Abstract
Multi-axis nanopositioning stages based on compliant mechanisms are a common topic within modern precision engineering due to their high accuracy, repeatability, and low cost compared to industry positioning stage alternatives. This paper details the design, computational optimization, and result evaluation of a two-axis flexural nanopositioning stage based on a modified version of the classic double parallelogram flexure (DPF) bearing (e.g. an underconstraint-eliminating DPF (UEDPF)). The two-axis flexural nanopositioning stage was optimized using response surface model with seven inputs and two outputs. The seven input parameters were the most sensitive geometric parameters for the UEDPF identified in previous work, and the two outputs were the flexure’s peak stress at maximum deflection and the force reaction felt by the 25mm displacement boundary condition. This paper shows that through design optimization, the first resonant mode of a long travel, two-axis flexural nanopositioning stage that has previously been reported in literature can be improved by a factor of two while still maintaining the higher-order resonant modes to be at least an order-of-magnitude higher than the fundamental mode of the stage. This is critical because increasing the fundamental mode without sacrificing the higher order modes will allow for a higher bandwidth controller to be implemented on this nanopositioning stage. The end goal of the positioning stage detailed in this paper is to be implemented within a micro-SLS 3D printer.
Exploring CausalWorld: Enhancing Robotic Manipulation via Knowledge Transfer and Curriculum Learning
Authors: Wang Xinrui , Jin Yan 
Published: November 2024

Abstract
This study explores a learning-based tri-finger robotic arm manipulating task, which requires complex movements and coordination among the fingers. By employing reinforcement learning, we train an agent to acquire the necessary skills for proficient manipulation. To enhance the efficiency and effectiveness of the learning process, two knowledge transfer strategies, fine-tuning and curriculum learning, were utilized within the soft actor-critic architecture. Fine-tuning allows the agent to leverage pre-trained knowledge and adapt it to new tasks. Several variations like model transfer, policy transfer, and across-task transfer were implemented and evaluated. To eliminate the need for pretraining, curriculum learning decomposes the advanced task into simpler, progressive stages, mirroring how humans learn. The number of learning stages, the context of the sub-tasks, and the transition timing were found to be the critical design parameters. The key factors of two learning strategies and corresponding effects were explored in context-aware and context-unaware scenarios, enabling us to identify the scenarios where the methods demonstrate optimal performance, derive conclusive insights, and contribute to a broader range of learning-based engineering applications.