bluuebunny/crossref_metadata_2025_split huggingface dataset:
Quantitative CAD Archetype Framework Evaluation With Professional User Data
Authors: Zhang Kaiwen , Cheng Kathy , Olechowski Alison 
Published: November 2024

Abstract
In a previous work completed by Zhang et al., a framework for analysing computer-aided design (CAD) user “archetypes” was created, based on the types of user data that can be collected from modern multi-user CAD (MUCAD) user analytics. In this paper, the previously developed CAD archetype framework will be applied to a professional engineering dataset. Various ratios are calculated and presented which show the distribution of users in various dimensions of interest, with evidence of the framework creating separation between users in areas of the CAD workflow. The paper concludes with discussions on future development work to improve the framework robustness.
Understanding the way that users interact with each other in a MUCAD setting will be critical for increasing productivity and enabling companies and professionals to optimise their workflows; research has shown that there are a number of approaches to designing in CAD, and one might imagine that these differences could be understood and classified such that compatibility can be improved, especially in a collaborative context.
Data Driven Metareasoning for Defending a Perimeter Against Cooperative Intrusion
Authors: Namala Prannoy , Vaidya Arjun , Herrmann Jeffrey W. 
Published: November 2024

Abstract
Perimeter defense problem is a type of differential game, where a team of attacker agents try to score by intruding a perimeter region while a team of defenders try to defend the perimeter by intercepting the attackers. There are many approaches to the multi-agent perimeter defense problem proposed over the last decade with varying assumptions. However, even the most general approach cannot generate an optimal solution for every situation. In some situations, a heuristic-based solution might work better than a complex one. Choosing the right solution will help in higher performance and lower computation burden due to the simplicity of the solution. This paper discusses a metareasoning approach for the multi-agent perimeter defense problem. We introduce two data-driven algorithm selection policies, one based on exploratory data analysis and the other based on machine learning, that determine which algorithm to use given the initial state of the perimeter defense game. We generated a large set of perimeter defense instances, simulated different solution approaches, and used the results to derive metareasoning policies. The analysis of the solution approaches and the basis for metareasoning to determine the solution approach were based on both the performance of the approach and the computation time taken. The results from our simulation study show that using these metareasoning policies leads, on average, to better solutions than existing algorithms used separately.
Cam-Shaped Soft Gripper With PVDF/Graphene Piezoelectric Sensor
Authors: Chen Liang-Chi , Chiou Jeng-Yu , Hsu Kuan-Lan , Huang Tsung-Han , Hung Wei-Song , Patel Brijesh , Lin Po Ting 
Published: November 2024

Abstract
In the era of growing integration of intelligent sensors in automated systems, propelled by advancements in industrial automation and the Internet of Things (IoT). This paper presents an innovative polyvinylidene difluoride (PVDF)/graphene piezoelectric membrane sensor specifically designed for direct force measurement. The remarkable properties of the piezoelectric material enable direct utilization without polarization. With a flexible design for easy installation at contact points between components, the sensor is tailored to the geometric shapes of components, finding application in soft grippers for object manipulation. The proposed novel piezoelectric sensor undergoes systematic evaluation through standardized experiments, obtaining measurements of different force magnitudes and corresponding voltage signal variations. The collected data is utilized to establish fitting models, thereby enhancing the reliability of the sensor’s data. The coefficient of determination (R squared) is employed as a metric to assess the goodness of fit of the models. Additionally, experimental results provide insights into the applicable range and voltage variations of the piezoelectric sensor. This research integrates a piezoelectric membrane sensor into an innovative cam-shaped soft gripper made of polydimethylsiloxane (PDMS) and demonstrates its application in agricultural automation by measuring the clamping force in plant seedlings. This cam-shaped gripper incorporates a novel self-alignment concept in its mechanism, enhancing its efficiency and mitigating potential damage to plant seedlings during the clamping process. Experiments with cam-shaped soft grippers demonstrate minimal deviation in gripping force.
Combining TRIZ and NLP to Identify Promising Problem-Solving Approaches for Emerging Technologies: A Preliminary Study
Authors: Bhattacharya Abhijeet , Mollan Calahan , Gupta Shreya , Pandey Vijitashwa 
Published: November 2024

Abstract
With the ever-expanding scope and increasing specialization of science research, more novel and niche issues are increasingly being addressed. This invariably leads to subfields developing to the point that they become independent areas of investigation by themselves. An example of such a phenomenon is the emergence of the many subareas stemming from the concept of Industry 4.0. All new such disciplines run into previously seen and unseen issues as they gain prominence and, as a result, practitioners. In this paper, we are focused on the ‘seen’ issues. Problem solving techniques, such as TRIZ, have enjoyed enormous success in engineering practice. The underlying premise behind TRIZ is that there is a small, finite number of methods which can be used to solve any problem and that most such problems have already been encountered and solved possibly in a different setting. Originally intended for physical inventions, TRIZ methods have since been applied to other fields such as patents to great effect. This paper uses natural language processing, machine learning, and abstracts from publicly available scientific articles to find if there exist common problem-solving methods in the engineering research of augmented and virtual reality, digital twins, and additive manufacture. We then present preliminary results and identify avenues for future research.
Techno-Economic and Environmental Impact Assessment of Phytomining for Extracting Rare Earth Elements in the Northwestern United States
Authors: Struhs Ethan , Mirkouei Amin 
Published: November 2024

Abstract
Demand for rare earth elements on a global scale is increasing daily due to the surging demand from various sectors (e.g., renewable energy and consumer electronics). Bio-ore, a product from pyrolysis conversion of biomass feedstocks obtained through phytomining, is a promising solution to address challenges faced by sustainable rare earth extraction, such as minimal land disturbance and soil erosion. This study explores the sustainability benefits of producing bio-ore at a critical materials rich site via a portable refinery unit and transporting it to an extraction plant. Techno-economic and environmental impact assessments are performed on a case study in the Northwestern United States. Total bio-ore production cost is estimated utilizing a life cycle costing model as part of the techno-economic analysis. Life cycle assessment is used to explore the global warming potential and other environmental impacts of the proposed bio-ore production pathway. Total cost of bio-ore production through phytomining ranges from $156–$197 per metric ton, depending on key parameters (e.g., mobile refinery capacity and hyperaccumulator water content). Total greenhouse gas emission is 11–35 kg CO2 eq. per metric ton of bio-ore. On-site production rate and biomass water content are the two key parameters that are examined in a sensitivity analysis to reduce bio-ore unit price and environmental impacts. This study concluded that the proposed phytomining-to-bio-ore approach provides a cost effective and economically feasible rare earth element extraction pathway.
The CCA Missing Data Completion Algorithm for Machine Learning Purposes
Authors: Chełchowski Adam , Jarzebowska Elżbieta , Debowski Andrzej 
Published: November 2024

Abstract
The paper deals with multivariate time series classification, a widely used concept of supervised machine learning for automation in data analysis. Many algorithms have been proposed to deal with such classification, yet a majority of them require equal length data inputs, which often cannot be primarily satisfied. For this purpose different techniques of data adjustment are applied, such as truncation, padding or forecasting. In this paper we present a novel approach to data recovery, which yields an algorithm allowing to align variable-length time series, basing on cross-correlation. The paper presents the proposed algorithm architecture with its advantages and possible tasks to be adapted for. The novel algorithm is illustrated on a use case, which is a sequence of car-like vehicles driving through different road conditions. Two independent analyses using a specific time series classifier are derived to test it. The first one covers validation on the open-access data, while the second one focuses on verification on experimental data collected by ourselves. In both cases the incomplete data is recovered with the use of a few chosen techniques including the one proposed in this paper. The results are thereafter analysed and compared using fundamental statistical factors.
An Experimental Investigation of P-Bifurcation, Energy Harvesting and Vibration Suppression Using a Nonlinear Inerter Device
Authors: Cosner Joel A. , Tai Wei-Che 
Published: November 2024

Abstract
This work investigates the vibration suppression and energy harvesting capabilities of an inerter pendulum vibration absorber (IPVA) when applied to a single degree of freedom structure subjected to Gaussian broadband base excitation which was analytically treated as white noise. An IPVA which was previously studied by the authors was attached to a DC generator to experimentally investigate the energy harvesting and vibration mitigation capabilities of the device in connection with P-bifurcation. A formal characterization of the system’s unknown parameters was done and all measured, calculated, and experimentally determined parameters were presented along with four resistive load considerations including the open circuit configuration. Next, an analytically derived P-bifurcation boundary in the noise intensity versus electrical and friction damping plane was presented and the boundaries were verified with an experiment. The power spectral densities for electric power and relative suspended mass velocity were determined for the system with the IPVA and compared with that of the best tested linear system. A correlation between poor energy harvesting and a monomodal PDF was made apparent, while the mean and resonant peak power was enhanced by a factor of as much as four and two, respectively. The peak value for the relative velocity power spectral density was improved by as much as a factor of four near resonance and the mean square relative velocity by a factor of two.
Geometry-Driven Design of Morphable Surface Structures Using Topology Optimization and Circle Packing
Authors: Gao Lingfeng , Zhu Xiaoping , Tanaka Masato , Song Yuyang , Zhou Yuqing , Gu Xianfeng David , Chen Shikui 
Published: November 2024

Abstract
This paper presents a new computational framework for the co-optimization and co-control of morphable surface structures using topology optimization and circle-packing algorithms. The proposed approach integrates the design of optimized compliant components and the system-level control of the overall surface morphology. By representing the surface shape using circle packing and leveraging conformal mapping, the framework enables smooth deformation between 2D and 3D shapes while maintaining local geometry and global morphology. The morphing surface design problem is recast as designing circular compliant actuators using level-set topology optimization with displacements and stiffness objectives. The optimized component designs are then mapped back onto the circle packing representation for coordinated control of the surface morphology. This integrated approach ensures compatibility between local and global geometries and enables efficient actuation of the morphable surface. The effectiveness of the proposed framework is demonstrated through numerical examples and physical prototypes, showcasing its ability to design and control complex morphable surfaces with applications in various fields. The co-optimization and co-control capabilities of the framework are verified, highlighting its potential for realizing advanced morphable structures with optimized geometries and coordinated actuation. This integrated approach goes beyond conventional methods by considering both local component geometry and global system morphology and enabling coordinated control of the morphable surface. The general nature of our approach makes it applicable to a wide range of problems involving the design and control of morphable structures with complex, adaptive geometries.
Utilizing Reinforcement Learning to Improve Gait Training Performance and Development of Gravity-Compensating Control System for RoboREHAB Device
Authors: Anthony Jacob , Ballard Chad , Goh Chung-Hyun , Carr Jacob , Jeon Woohyoung 
Published: November 2024

Abstract
As a patient gets older, the chances of sustaining a motor disability increase. Such disabilities impair the patient’s ability to perform daily activities and undergo rehabilitation to regain mobility. Exoskeleton devices and aqua therapy have been used to make this process easier. However, most exoskeletons do not replicate the natural gait pattern accurately and there does not exist a lower-limb rehabilitation device that provides gait training with zero-gravity assistance. The objective for this study is to design two control systems that govern motor control for the natural gait pattern using a redesigned leg assembly and create a weight-compensation control system for a traditional leg assembly. Reinforcement learning (RL) was used in Simulink to develop the gait pattern control system, and a physical prototype was created to test the zero-gravity control system using Arduino. The RL control system with the redesigned leg showed a X- and Y-deviation of 45.622 mm and 17.164 mm at the knee joint compared to the original two-segment design, which achieved an X- and Y-deviation of 133.333 mm and 38.832 mm at the knee joint. Zero-gravity was achieved via DC Motors for the knee and foot sections, but the motors were not strong enough to support the load at the hip joint. Using Servo Motors, a sit-to-stand movement was created, with only a slight reduction in the range of motion of the hip due to the 5V motors limitations. Finally, a walking motion was created using Servo Motors, achieving another method of zero-gravity. The zero-gravity controller worked for the walking and sit-to-stand transfer but the hip joint needs further improvement.
Modal Sensitivity Analysis of the In-Plane Rigid-Elastic Coupled Tire Model
Authors: Alobaid Faisal , Taheri Saied 
Published: November 2024

Abstract
This study uses the in-plane rigid elastic tire model and modal sensitivity analysis to explore the tire properties and its modal characteristics. The tire model integrated the Euler Bernoulli beam as a tire tread with sidewall lumped masses to extend the frequency range to 300 Hz. The finite Difference Method (FDM) is used to discretize the PDE’s equation of motion into ODE’s equations, create mass, stiffness, and damping matrix, and then build the tire modal model. The sensitivity analysis was applied to the tire modal model to identify the properties significantly impacting the tire’s natural frequencies in-phase and out-phase. Pretension and two sectional sidewall stiffnesses were chosen for sensitivity evaluation. Percentage variations of ±25% are imposed on each property to estimate their effects on the tire’s modal characteristics. The findings emphasize that the two sectional sidewall stiffness impact on the natural frequencies depends on the natural frequencies range (radial mode number) and whether it is in the in-phase or out-phase regions. However, the pretension generally significantly impacts natural frequencies linearly in all radial modes. These results offer valuable insights for understanding noise, vibration, and harshness and enhancing tire and vehicle performance for ride comfort over short-wavelength road profiles.
Static and Dynamic Behavior of Planetary Gears With Diametrically Opposite Planet Gears
Authors: Ryali Lokaditya , Talbot David 
Published: November 2024

Abstract
The majority of planetary gears in the industry have equally spaced planets, it’s very rare to find designs with unequally spaced planets unless there are assembly limitations or specific application needs. The literature on unequally spaced and diametrically opposite planet gear configurations is also quite sparse. Therefore, the current study presents a theoretical investigation into the impact of planet gear spacing on the performance of Epicyclic gear sets, with a design exploratory intention to understand if there are some benefits associated with diametrically opposite planet designs. To achieve these objectives the three-dimensional planetary load distribution model of Ryali and Talbot [1] is employed to investigate the quasi-static and dynamic behavior of diametrically opposite 4 planet gear sets. Various key planetary gear outputs such as planet load sharing, natural modes, overall transmission error, radial vibrations, and gear mesh force will be monitored and summarized in this paper.
Design of Rotating Nonlinear Pendulum Vibration Absorbers for Electrified Machinery
Authors: Singh Pratibha , Monroe Ryan , Geist Bruce 
Published: November 2024

Abstract
Centrifugal pendulum vibration absorbers (CPVAs) are passive devices and a proven technology for reducing torsional vibrations in rotating systems, including helicopter rotors and crankshafts of internal combustion engines. CPVAs consist of pendulums mounted on a rotor, driven by system rotation, and tuned to counteract engine-order fluctuating torques acting on the rotor, thereby smoothing vibrations. In this study, a unifilar CPVA configuration is proposed to address torsional vibrations in electric machines (EMs). A principal challenge in this application is the high-orders of torsional vibration inherent in current EM operation. As order increases, the path radius of curvature that the absorber mass is required to follow (for proper tuning) diminishes, which presents machining challenges. A dynamic model for a unifilar CPVA is developed and then linearized to compute the tuning orders of the system. A quadratic formula is derived whose roots govern the two natural orders of the system and initial results show a desirable large separation between these orders in a prototype design. The developed model will facilitate future simulation studies of the system forced vibration response to characterize the stability and vibration control performance of this design.
Time-Embedding GRBFNN: Learning Transient Response of Stochastic Dynamical Systems
Authors: Xing Siyuan , Sun Jianqiao 
Published: November 2024

Abstract
This paper introduces the Time-Embedding Gaussian Radial-basis Function Neural Network (TE-GRBFNN), a novel architecture designed to learn the time-varying solutions of Fokker-Planck-Kolmogorov (FPK) equations, within the framework of Physics-informed Neural Networks (PINNs). TE-GRBFNN extends the structure of GRBFNN by integrating time-varying centers and widths of Gaussian neurons, determined by two separate time-embedding neural networks. This structural change effectively addresses the challenging unbounded boundary and normalization conditions associated with FPK equations, which have posed difficulties for other deep neural network approaches. As an initial step, we employ TE-GRBFNN to capture the response statistics of first-order nonlinear Stochastic Differential Equations (SDEs). Our results demonstrate strong agreement with Monte Carlo simulations, highlighting the efficacy of TE-GRBFNN. Once trained, the proposed TE-GRBFNN architecture can provide instantaneous predictions of FPK equation solutions at any arbitrary time and location. This feature can offer significant potential for advancing methodologies in structural optimization and control.
Microstructure-Aware Surrogate Models Based on Unbalanced Data for Material Design
Authors: Wu Yulun , Li Yumeng 
Published: November 2024

Abstract
Predicting material responses like strain and energy under various loading conditions is crucial for understanding structure-property relationships and guiding material design. However, this task can be computationally expensive and complex, especially for diverse materials with vast design spaces. Traditional methods like physics-based simulations can be time-consuming and costly, while experimental exploration across large spaces is impractical. Convolutional neural networks (CNNs) and fully connected neural networks (FNNs) offer promising alternative, enabling efficient and accurate material response predictions based on simulations or experimental data. This is particularly beneficial for materials with intricate microstructures that are difficult to characterize with conventional methods. However, CNNs and FNNs often face challenges due to limited training data, leading to poor generalization and low robustness. Additionally, material prediction tasks often encounter unbalanced data where acquiring different responses comes at varying costs. This unbalance can bias model predictions and hinder generalization to unseen material structures. To address these limitations, we propose employing multi-task learning (MTL) to enhance the understanding of material behavior in those deep learning models, specifically targeting the problem of unbalanced data. MTL leverages shared information between multiple interconnected learning tasks, allowing the model to learn from complementary information. In the context of material prediction, MTL can be employed to jointly train CNNs on predicting multiple responses, like displacement and strain energy. This shared learning approach enhances the model’s ability to identify underlying patterns and relationships, leading to more accurate and robust predictions.
A Noise Feedback Control Method for Parametrically Excited Mode-Localized Accelerometer
Authors: Tang Yinghai , Zhao Jian , Kacem Najib , Dong Zeyuan , Liu Pengbo 
Published: November 2024

Abstract
This article analyzes the effects of 1/f noise on the time-domain and frequency-domain responses of a parametrically excited mode-localized accelerometer. The impact of time-delay feedback control on 1/f noise is calculated, and with which, the noise feedback control method is proposed. The proposed method overcomes the drawback of the original time-delay feedback control that reduces the amplitude.
Density Measurement Using a Self-Excited Stainless-Steel Microcantilever With Electrostatic Force Actuation
Authors: Nakamura Takumi , Yabuno Hiroshi , Yamamoto Yasuyuki , Matsumoto Sohei 
Published: November 2024

Abstract
The present study proposes liquid density measurement using a self-excited microchannel resonator made of stainless steel. The sensor is made with metal etching and diffusion bonding. Thus, it enables us to reduce the sample amount compared to macroscale sensors while being easier to fabricate than MEMS. We propose using electrostatic force to excite the stainless-steel microcantilever directly to eliminate the dynamic coupling between a microcantilever and its holder. The actuation is applied under nonlinear velocity feedback control through voltage to employ the dynamics of the Rayleigh oscillator. The proposal’s validity is confirmed by distinguishing distilled water, ethanol, and salty water one by one. We observed distinguishable frequency shifts of self-excitation in the experiments.