bluuebunny/crossref_metadata_2025 huggingface dataset:
Force Reduction of 3-RPS Parallel Manipulator Using Mechanical Springs
Authors: Nurahmi Latifah , Nguyen Vu Linh
Published: November 2024

This paper presents the force reduction of a 3-RPS parallel manipulator using mechanical springs. Two design methods are proposed: one attaches a linear spring from the base to each cylinder-piston pair of P-joint, and the other implements a torsional spring to the R-joint of each leg. The significance of the proposed methods lies in their structural simplicity and ease of implementation. In these methods, the spring parameters are acquired using particle swarm optimization (PSO) to minimize the total actuator force of the manipulator when it tracks along the centerline of the workspace. In this work, a numerical example of a 3-RPS parallel manipulator is given to demonstrate the effectiveness of the proposed methods. Through four different trajectories, it is shown that the two methods can significantly reduce the actuation forces of the manipulator. The force reduction rate (FRR) of the manipulator can be achieved up to 95% with a linear-spring design and 88.2% with a torsional-spring design when it tracks along the centerline of the workspace. The lowest FRR for the linear-spring design is 70.8% and that for the torsional-spring design is 66.2%. The obtained results demonstrate the high performance of the proposed methods, along with design simplicity, making them have great potential applications.
Low-Cost Melt Pool Temperature Prediction Using Visible Light Camera and Machine Learning in Laser Hot-Wire Directed Energy Deposition
Authors: Rahmani Dehaghani Mostafa , Sajadi Pouyan , Tang Yifan , Wang G. Gary
Published: November 2024

Laser wire directed energy deposition (DED-LB/w) offers notable efficiency in metal additive manufacturing (AM), allowing rapid component fabrication with high material utilization. Despite its advantages, challenges such as anisotropy and uneven mechanical properties arise from unregulated heat application, highlighting the need for precise temperature control during deposition. This study evaluates the use of visible light images to predict melt pool temperature, leveraging Convolutional Neural Networks (CNN), Gaussian Process Regression (GPR), and Artificial Neural Networks (ANN). While CNN is trained directly on images, GPR and ANN utilize extracted features such as melt pool dimensions. The CNN model notably excels, achieving an R-squared value of 0.981, root mean square error of 44.46, and mean absolute percentage error of 2.74%, demonstrating the superior capability of visible light imaging in accurately predicting the melt pool temperature. This success illustrates the considerable potential of integrating predictive models with visible light imaging as a cost-effective alternative to traditional sensory systems. Such integration not only offers a pragmatic solution to the high costs and complexities associated with thermal imaging but also opens new avenues for combining other measurement techniques such as pyrometers to further enhance the prediction accuracy for AM process control. Future efforts will concentrate on implementing these models in real-time metal printing, aiming to enhance microstructure control and advance process automation.
We’re All in This Together: Evaluating the Impact of Designers’ Mood Dynamics on Collaborative Design Project Outcomes
Authors: Harris Meg , Toh Christine
Published: November 2024

This study extends prior work to investigate the relationship between team mood dynamics and design outcomes of novice designers. In this paper, we investigate how divergence is related to both project performance and quality of collaborative work. Prior research has studied team dynamics but has not thoroughly investigated mood in the context of an extended design engagement. This limited knowledge leaves a gap in our understanding of how the complex interactions of emotional states among team members affects the ability of the team to work together effectively, and ultimately, achieve expected design outcomes. Research that fills this gap can help improve design outcomes and educational strategies. Thus, a study was conducted using the self-reflections of student teams collected from three different points over the course of a semester-long design project, in order to understand how the variation in designers’ moods during collaborative design activities influence project outcomes and quality of collaborative work. Our results show that divergence of team members’ moods with respect to tone, positive tone, and negative emotion were negatively related to project outcomes. These results provide a foundation for future systematic investigations of team-based affect on design outcomes and show how design can be viewed through the lens of Symbolic Interactionism, evident in how team members create meaning and shared understanding within the design context.
Electrostatically Actuated BIO-MEMS Circular Membrane Resonator Sensors: Voltage Response of Primary Resonance
Authors: Caruntu Dumitru I. , Alipi Marcos
Published: November 2024

This paper deals with the voltage-amplitude relationship during the primary resonance of electrostatically actuated circular membrane resonator sensors within the context of BIO-MEMS. The motion of these membranes is governed by a partial differential equation, which is nondimensionalized for analysis. This equation incorporates both damping effects and soft electrostatic actuation. The actuation AC frequency is set close to half of the membrane’s first natural frequency, leading to primary resonance. To investigate membrane’s voltage response, two principal methods were employed: the method of multiple scales (MMS) and a reduced order model (ROM) that incorporates two modes of vibration. This research provides insights into how the circular membrane resonator sensors behave under varying actuation voltages and examines the effects of damping and frequency on the voltage response. It was predicted that an increase in damping or detuning of frequency increases the pull-in voltage.
Path Generative Model Based on Conditional β- Variational Auto Encoder for Mechanism Design
Authors: Nurizada Anar , Lyu Zhijie , Purwar Anurag
Published: November 2024

This paper introduces a novel methodology based on Conditional β-Variational Autoencoder (cβ-VAE) architecture to generate diverse types of planar four-bar mechanisms for a given coupler curve. This approach allows for the synthesis of linkages capable of tracing continuous paths, overcoming the overdetermined problem often encountered in linkage coupler-curve synthesis. The proposed deep generative model processes mechanisms alongside their coupler curves and types, generating a latent vector that enables the decoder to produce a variety of mechanisms tailored to specific conditions.

The paper also introduces a comprehensive methodology for assessing the efficacy of the model, proposing three hierarchical metrics focused on reconstruction quality, novelty, and diversity of the predicted mechanisms. The methodology is scalable and applicable to various types of four-bar and higher-order mechanisms, enhancing the model’s utility by including an additional step for computing cognates of predicted mechanisms, thereby generating more efficient solutions.

The key contributions of this work lie in the development of a Conditional β-VAE model with attention layers for synthesizing multiple mechanisms, the creation of a unified representation for different types of four-bar mechanisms, the generation of an extensive dataset, the development of a unified algorithm for computing cognates, and the introduction of quantitative assessment metrics. This approach not only surmounts the limitations of traditional methods but also opens new avenues for mechanism design, providing a data-driven tool for exploring alternative designs and evaluating their performance in real-time.
Decoupled Time-Invariant Forms of Linear Parametrically Excited Structural Systems
Authors: Sharma Ashu
Published: November 2024

In this study, decoupled time-invariant forms of linear parametrically excited structural systems are constructed. First, equations of motion are temporarily written in the state-space form to apply the Lyapunov-Floquet transformation. Next, the resulting time-invariant equation is subjected to a decoupling transformation to yield a new equation that can be expressed as a set of decoupled second-order time-invariant equations. Such equations are generated for linear systems with and without external excitations. Examples are provided, and the results thus obtained are verified by numerical simulation.
Safer and Efficient Factory by Predicting Worker Trajectories Using Spatio-Temporal Graph Attention Networks
Authors: Kasarapu Satya Saravan Kumar , Vasantha Gokula , Corney Jonathan , Hanson Jack , Quigley John , El-Raoui Hanane , Thompson Nathan , Sherlock Andrew
Published: November 2024

Occupational accidents in manufacturing industries pose a significant risk, necessitating advanced strategies to ensure worker safety and enhance operational productivity. The unpredictable nature of worker movements, influenced by varied tasks such as material transportation, machine operation, and collaborative efforts, highlights the critical need for effective trajectory prediction mechanisms. This paper introduces an innovative approach utilizing Spatio-Temporal Graph Attention Networks (STGAT) and Spatio-Temporal Graph Convolutional Neural Networks (STGCNN) to predict worker trajectories with high accuracy and to analyze worker interactions within the manufacturing environment. Our methodology employs qualitative evaluation techniques to reveal intricate worker dynamics during assembly line processes, offering new perspectives on spatial-temporal interplays in a factory setting. By applying this method to movement data from a detailed case study involving six workers on a tribike assembly line, we demonstrate the effectiveness of our proposed algorithm in real-world scenarios. The utilization of advanced Graph Neural Network technologies allows for the precise modeling of complex spatial-temporal relationships, enabling the accurate prediction of worker paths. This research contributes significantly to the fields of occupational safety and industrial efficiency by providing a comprehensive framework for anticipating worker movements and understanding their interactions in intricate manufacturing landscapes. Moreover, it addresses existing challenges in trajectory prediction and outlines potential directions for future research, aiming to broaden the application of predictive analytics in enhancing safety protocols and operational strategies in the manufacturing sector.
Semi-Analytical Periodic Motions in Impact Duffing Oscillator
Authors: Luo Albert C. J. , Zhu Yuzhou
Published: November 2024

In this paper, impact periodic motions in a periodically forced Duffing oscillator with one-side wall are obtained semi-analytically through implicit mappings. The implicit mapping is determined through the discretization of the impact Duffing oscillator. The mapping structures are employed to construct specific impact periodic motions for an impact Duffing oscillator. The impact periodic motions are presented for a better understanding of discontinuous dynamical systems and the grazing impact motions are obtained for impacts adding of impact periodic motions.
Predicting Structural System Behavior via a Deep Surrogate Modeling Framework With Imbalanced Feature Learning
Authors: Lee Marshall , Tao Chenxi , Shah Kushal Jignesh , Choi Seung-Kyum
Published: November 2024

In recent years, deep learning has experienced rapid advancement and has been applied across numerous disciplines. Predicting the structural responses using a deep learning method involves several intricate challenges since structural datasets often suffer from data imbalance, where certain stress responses are more prevalent, potentially skewing the deep learning method’s training process and accuracy. To meet these challenges, we propose a deep surrogate modeling framework for predicting systems behavior of complex engineering systems that integrates imbalanced feature learning. This research develops a deep surrogate modeling framework by using a convolutional neural network (CNN) for predicting structural response behavior, which might have data imbalance issues. To mitigate data imbalance issues, statistical sampling and label transformation methods are examined to improve prediction accuracy. Specifically, stress responses in cantilever beams are considered to highlight the efficiency and applicability of the proposed framework. The proposed deep surrogate modeling framework not only demonstrates enhanced accuracy of predictions in complex engineering systems but also addresses the challenge of imbalanced data distributions. The proposed deep learning framework aims to overcome the challenges associated with predicting structural responses in complex engineering systems.
AutoTRIZ: Artificial Ideation With TRIZ and Large Language Models
Authors: Jiang Shuo , Luo Jianxi
Published: November 2024

Researchers and innovators have made enormous efforts in developing ideation methods, such as morphological analysis and design-by-analogy, to aid engineering design ideation for problem solving and innovation. Among these, the Theory of Inventive Problem Solving (TRIZ) stands out as one of the most well-known approaches, widely applied for systematic innovation. However, the complexity of TRIZ resources and concepts, coupled with its reliance on users’ knowledge, experience, and reasoning capabilities, limits its practicality. Therefore, we explore the recent advances of large language models (LLMs) for a generative approach to bridge this gap. This paper proposes AutoTRIZ, an artificial ideation tool that uses LLMs to automate and enhance the TRIZ methodology. By leveraging the broad knowledge and advanced reasoning capabilities of LLMs, AutoTRIZ offers a novel approach for design automation and interpretable ideation with artificial intelligence. AutoTRIZ takes a problem statement from the user as its initial input, and automatically generates a solution report after the reasoning process. We demonstrate and evaluate the effectiveness of AutoTRIZ through consistency experiments in contradiction detection, and a case study comparing solutions generated by AutoTRIZ with the experts’ analyses from the textbook. Moreover, the proposed LLM-based framework holds the potential for extension to automate other knowledge-based ideation methods, including SCAMPER, Design Heuristics, and Design-by-Analogy, paving the way for a new era of artificial ideation for design innovation.
An Investigation of IMU-Based Tolerance Bands for Evaluating Manual Assembly Processes
Authors: Renu Rahul S. , Johnson Kirk , Dawkins Jerel , Smith Nathaniel , Weeren Caroline , Odom Blakeley , Righter James
Published: November 2024

Manual assembly processes are prevalent in manufacturing facilities despite the proliferation of automation and robotics. Humans are adaptable and our capability to make on-the-fly decisions about assembly quality is not yet replaced by artificial intelligence. Therefore, there is a need to ensure that humans are trained well to perform manual assembly tasks. To this end, the research presented in this paper investigates the use of tolerance bands around accelerometer data for the evaluation of manual assembly tasks.

Previous research has identified methods to assess consistency of manual assembly tasks performed. One such approach is based on accelerometer time series data obtained from wrist-worn inertial measurement units. Consistency is computed as a function of point-to-point matches between time series data from two assembly process cycles. This approach assesses minor task performance variations, which may have been a result of desired human adaptability, as inconsistent.

To account for human adaptability within the context of assembly process consistency, the use of tolerance bands on accelerometer data is investigated in this research. Two experiments were conducted where three assemblers assembled a total of three products multiple times. Wrist-worn inertial measurement unit data from these experiments were analyzed based on tolerance bands and inconsistencies are identified using a statistical process control tool - control charts.

It was found that the proposed method was able to identify inconsistencies in the assembly processes performed. A post-hoc video analysis of the inconsistent assembly cycles showed that there are opportunities to improve workstation layout, product design, and work instructions.
A Generative Graph Neural Network-Based Framework for Designing Connectivity-Guaranteed Porous Metamaterial Units
Authors: Wang Zihan , Bray Austin , Naghavi Khanghah Kiarash , Xu Hongyi
Published: November 2024

Porous metamaterial units filled with fluid have been used in engineering systems due to their ability to achieve desired properties (e.g., effective thermal conductvity). Designing 3D porous metamaterial units while ensuring complete connectivity of both solid and pore phases presents a significant challenge. In this study, we propose a generative graph neural network-based framework for designing the porous metamaterial units infilled with liquid. Firstly, we propose a graph-based metamaterial unit generation approach to generate porous metamaterial samples with complete connectivity in both solid and pore phases. Secondly, we establish and evaluate three distinct variational graph autoencoder (VGAE)-based generative models to assess their effectiveness in generating an accurate latent space representation of metamaterial structures. By choosing the model with the highest reconstruction accuracy, the property-driven design search is conducted to obtain novel metamaterial unit designs with the targeted properties. A case study on designing liquid-filled metamaterials for thermal conductivity properties is carried out. The effectiveness of the proposed graph neural network-based design approach is evaluated by comparing the performances of the obtained designs with those of existing designs in the training database. Merits and shortcomings of the proposed framework are also discussed.
Free Wave Propagation in Two-Dimensional Lattice Model of Textile Metamaterials
Authors: Arena Andrea , Lepidi Marco
Published: November 2024

A parametric lattice model is formulated to describe the dispersion properties of harmonic elastic waves propagating in two-dimensional textile metamaterials. Within a weak nonlinear regime, the free undamped motion of the textile metamaterial, starting from a spatially periodic prestressed configuration, is governed by nonlinear differential difference equations, where nonlinearities arise from the elastic contact between plain woven yarns. Using Bloch’s theorem for periodic systems, the linear dispersion properties characterizing the regime of small oscillation amplitudes are obtained. Parametric analyses are carried out to study the influence of the mechanical parameters on wavefrequencies, waveforms and group velocities, respectively. As major spectral features, two distinct passbands are found to cover the low- and the high-frequency ranges, respectively, while a complete mid-frequency bandgap exists for large parameter regions.
Studying Changes to the Additive Manufacturability of Design Solutions When Prepared and Simulated in Immersive Virtual Reality
Authors: Mathur Jayant , Miller Scarlett R. , Simpson Timothy W. , Meisel Nicholas A.
Published: November 2024

Solving problems with additive manufacturing (AM) often means fabricating geometrically complex designs, layer-by-layer, along one or multiple directions. Designers navigate this 3D spatial complexity to determine the best design and manufacturing solutions to produce functional parts, manufacturable by AM. However, to assess the manufacturability of their solutions, designers need modalities that naturally visualize AM processes and the designs enabled by them. Creating physical parts offers such visualization but becomes expensive and time-consuming over multiple design iterations. While non-immersive simulations can alleviate this cost of physical visualization, adding digital immersion further improves outcomes from the visualization experience. This research, therefore, studies how differences in immersion between computer-aided (CAx) and virtual reality (VR) environments affect: 1. determining the best solution for additively manufacturing a design and 2. the cognitive load experienced from completing the DfAM problem-solving experience. For the study, designers created a 3D manifold model and simulated manufacturing it in either CAx or VR. Analysis of the filtered data from the study shows that slicing and printing their designs in VR yields a significant change in the manufacturability outcomes of their design compared to CAx. No observable differences were found in the cognitive load experienced between the two modalities. This means that the experiences in VR may influence improvements to manufacturability outcomes without changes to the mental exertion experienced by the designers. This presents key implications for how designers are equipped to solve design problems with AM.
Generating Porous Metamaterial Designs Using Variational Graph Autoencoder and Large Language Model
Authors: Naghavi Khanghah Kiarash , Wang Zihan , Xu Hongyi
Published: November 2024

In this paper, we propose and compare two novel deep generative model-based approaches for the design representation, reconstruction, and generation of porous metamaterials characterized by complex and fully connected solid and pore networks. A highly diverse porous metamaterial database is curated, with each sample represented by solid and pore phase graphs and a voxel image. All metamaterial samples adhere to the requirement of complete connectivity in both pore and solid phases. The first approach employs a Dual Decoder Variational Graph Autoencoder to generate both solid phase and pore phase graphs. The second approach employs a Variational Graph Autoencoder for reconstructing/generating the nodes in the solid phase and pore phase graphs and a Transformer-based Large Language Model (LLM) for reconstructing/generating the connections, i.e., the edges among the nodes. A comparative study was conducted, and we found that both approaches achieved high accuracy in reconstructing node features, while the LLM exhibited superior performance in reconstructing edge features. Reconstruction accuracy is also validated by voxel-to-voxel comparison between the reconstructions and the original images in the test set. Additionally, discussions on the advantages and limitations of using LLMs in metamaterial design generation, along with the rationale behind their utilization, are provided.
Estimating the Tire-Road Friction Coefficient by Inertial Measurement Unit
Authors: Cavacece Massimo
Published: November 2024

Accurately modeling vehicle dynamics for simulation, analysis, or control requires a precise tire friction model. The friction forces influence the vehicle’s movement on the road. Significant research considers the mathematical model of pneumatic-road friction, primarily driven by advancements in high-performance Antilock (ABS) braking systems, traction control, and steering systems. This proposed study thoroughly examines recent developments and trends in this area, focusing on providing a broad perspective of multidisciplinary initiatives and techniques for related research. The proposed strategy for estimating the coefficient of friction value is extensively analyzed and tested through simulations and data collected on an instrumented test vehicle.
Multi-Objective Surrogate Optimization of Process Parameters for Additive Manufacturing With Applications in Laser Powder Bed Fusion
Authors: Mengesha Betelhiem N. , Aute Vikrant , McGregor Davis J. , Azarm Shapour
Published: November 2024

Widespread adoption of additive manufacturing (AM) is hindered by challenges in achieving part quality using metrics such as geometry accuracy and porosity. These metrics are affected by the microstructure and mechanical properties of fabricated parts which can be controlled by the AM’s process parameters. Fine-tuning these parameters can enable control over the part quality. In this study, an optimization-based approach for selecting the AM process parameters is proposed for achieving part quality. The proposed approach integrates design of experiments, AM process simulation, surrogate modeling, and multi-objective optimization. While the proposed approach is general and applicable to any AM process, the applicability of the proposed approach is demonstrated through a laser powder bed fusion (LPBF) process. Three LPBF process parameters, namely layer thickness, laser power, and scanning speed are considered for obtaining optimized part quality considering geometric accuracy and porosity. A cylinder and a heat exchanger example are used to demonstrate the effectiveness of the proposed approach with the LPBF process. For these examples, it is shown that with the optimized process parameters, the part has about 17% better geometric accuracy when compared to the unoptimized part while satisfying a porosity requirement. The results also reveal that laser power is the most influential process parameter affecting both the geometric accuracy and porosity.
CFD-Driven Topology Optimization for Personalized Intracranial Aneurysm Implant Design
Authors: Lei Weijun , Sadasivan Chander , Gu Xianfeng David , Chen Shikui
Published: November 2024

Intracranial aneurysm rupture causes life-threatening sub-arachnoid hemorrhage. Current endovascular devices like coils, flow diverters, and intravascular implants aim to thrombose the aneurysm but have limitations and varying success rates depending on aneurysm characteristics. We propose a new computational framework integrating CFD and topology optimization to design personalized aneurysm implants. The optimization problem aims to reduce blood flow velocity within the aneurysm while ensuring adequate structural integrity of the implant. The fluid dynamics are governed by the Navier-Stokes equations, while the solid mechanics are described by the linear elasticity equations. A Darcy-Brinkman model is employed to simulate flow through the porous implant in the fluid domain, while the Solid Isotropic Material with Penalization (SIMP) method is used to interpolate between solid and void regions in the structural domain during topology optimization. The objective combines fluid energy dissipation ratio and solid strain energy with spatially varying weights. Global and local volume constraints generate personalized implants with porosity and flow-diverting architectures. The approach is demonstrated on patient-specific aneurysm geometries from rotational angiography. This CFD-driven topology optimization method enables personalized aneurysm implant design to potentially improve occlusion rates and reduce complications compared to current devices. Further studies will validate the optimized designs and investigate their efficacy in vitro and in vivo.
Multi-Stage Optimal Design for Turbulent Pipe Systems by Data-Driven Morphological Exploration and Evolutionary Shape Optimization
Authors: Urata Kazuya , Tsumoto Ryo , Yaji Kentaro , Fujita Kikuo
Published: November 2024

Turbulence fields typically cause cumbersome multimodality in their solution space and the optimal shape of flow paths could be strongly dependent on a huge number of branch patterns. These features hinder gradient-based structural optimization frameworks from finding promising solutions for turbulent pipe systems. In this paper, we propose a multi-stage framework that integrates data-driven morphological exploration and evolutionary shape optimization to address the challenges posed by the complexity of turbulent pipe systems. Our framework begins with data-driven morphological exploration, aiming to find promising topologies as well as shapes for selecting a reasonable number of candidates for the next shape refinement stage. Herein, we employ data-driven topology design, a gradient-free and multiobjective optimization methodology incorporating a deep generative model and the concept of evolutionary algorithms to generate promising arrangements. Subsequently, representative shapes are extracted through a deep clustering strategy. The final stage involves refining these shapes through shape optimization using a genetic algorithm. Focusing on a two-dimensional turbulent pipe system with a min/max objective, our numerical results show the effectiveness of the proposed framework in delivering high-performance solutions for the turbulent flow optimization problem with branching.
Construction of Confidence Regions for Uncertain Spatial Displacements With Dual Rodrigues Parameters
Authors: Yu Zihan , Ge Qiaode Jeffrey , Langer Mark P.
Published: November 2024

This paper follows our recent work on the computation of kinematic confidence regions from a given set of uncertain spatial displacements with specified confidence levels. Dual quaternion algebra is used to compute the mean displacement as well as relative displacements from the mean. In constructing a 6D confidence ellipsoid, however, we use dual Rodrigue parameters resulting from dual quaternions. The advantages of using dual quaternions and dual Rodrigues parameters are discussed in comparison with those of three translation parameters and three Euler angles, which were used for the development of the so-called the Rotational and Translational Confidence Limit (RTCL) method. The set of six dual Rodrigue parameters are used to define a parametric space in which a 6 × 6 covariance matrix and a 6D confidence ellipsoid are obtained. An inverse operation is then applied to first obtain dual quaternions and then to recover the rotation matrix and translation vector for each point on the 6D ellipsoid. Through examples, we demonstrate the efficacy of our approach by comparing it with the RTCL method known in literature. Our findings indicate that our method, based on the dual-Rodrigues formulation, yields more compact and effective swept volumes than the RTCL method, particularly in cases involving screw displacements.
Bifurcation Topology Tuning of 2:1 Internal Resonance in Electrostatically Coupled Resonators
Authors: Sun Rongjian , Zhao Jian , Kacem Najib , Lyu Ming , Dong Zeyuan , Liu Pengbo
Published: November 2024

The 2:1 internal resonance of electrostatically coupled resonators is theoretically investigated. A general dynamical model of the coupled resonators is established, and a reduced-order model incorporating the quadratic and cubic coupling terms is derived by the Galerkin method. The safe operating range of the system is determined by static analysis, and the operating voltage satisfying the frequency commensurable relation is determined by eigenvalue analysis. Finally, the dynamic response of the system is solved by a second-order method of multiple scales, and the results are verified by asymptotic numerical method coupled with harmonic balance method. For the case of response symmetry detuning due to cubic nonlinearity, we use non-resonant quadratic terms to adjust the bifurcation topology of this behavior, thus correcting the symmetry of the system and restoring the amplitude saturation at the antiresonance point.
Fabrication of Rotary Comb Drives With Floating Center
Authors: Verotti Matteo , Bagolini Alvise , Lorenzelli Leandro , Crescenzi Rocco , Belfiore Nicola Pio
Published: November 2024

Rotary comb drives have been recently adopted to operate MEMS-Technology based micro systems. In several cases it is convenient to adopt a kinematic structure based on the four-bar linkage, where the coupler link has the guiding function for a generic tool as, for example, one jaw of a gripper. In this case the grasping function is granted by two symmetric jaws that are guided by two symmetric four-bar linkage structures. Since the ordinary kinematic pairs are not suitable for most planar fabrication methods of micro machining, compliant flexures must be used, with the consequence that some flexures must be embedded in the mechanical structure. For example, a microgripper based on two symmetric four-bar linkages needs 8 flexures. With the aim of coping with the problem of providing higher power to inflect all these flexures, this paper presents a new concept design which allows the coupler to be directly actuated by a second cooperating rotary comb drives, in addition to the fixed axis rotary comb drive (RCD) that actuates the crank link. The secondly added comb drive has also the advantage that its rotation center can be adjusted during the design stage with considerable freedom of choice in the plane, including positions that are not physically placed within the wafer. A microgripper prototype has been obtained by applying Deep Reaction Ion Etching (DRIE) on a Silicon on Insulator (SOI) wafer and then herein presented.
Aggressive Driving Behavior Detection Using Integrated I-DBSCAN and LSTM Neural Network
Authors: Chen Hao , Hegde Shailesh , Bonfitto Angelo
Published: November 2024

Lately, a significant rise in traffic fatalities linked to aggressive driving behaviors has been noted, accentuating the imperative need for research in this domain. Hence, the detection of aggressive driving is increasingly advocated as a strategy not only to alert drivers about their perilous behaviors but also to potentially diminish the incidence of accidents. In parallel, the widespread adoption of driving simulators in the automotive sector has unveiled their profound advantages, especially in terms of repeatability in a controlled environment, helping researchers significantly reduce time and cost of development. Furthermore, driving simulators also provide a safe platform for testing new technologies and evaluating driver behavior in various scenarios. The article presents a method for identifying aggressive driving by analyzing vehicle dynamics data (such as speed, acceleration, and steering angle) collected from simulations in an urban setting using the software SCANeR™Studio. The algorithm employs Iterative Density-based spatial clustering of applications with noise, an unsupervised learning technique, to cluster aggressive driving maneuvers with sub-classification in terms of comfort and safety. Further, this labeled data is used to train a Bayesian optimization-based long short-term memory neural network, a pattern recognition model for the detection of driving behavior. The research findings confirm the model’s notable ability to recognize aggressive driving behaviors, as indicated by the confusion matrix and a F-score of 0.869, showing great potential to enhance road safety.
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

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

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

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

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

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

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

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.