bluuebunny/crossref_metadata_2025_split huggingface dataset:
Typology Development for Synthetic Chemistry Sub-Tasks: Towards Human-Robot Collaboration Task Design in the Wet Lab
Authors: Jung Diane N. , Bruns Carson J. 
Published: November 2024

Abstract
Chemical manufacturing is a growing field that contributes to many industries and employs tens of thousands of researchers in wet labs. Automation tools for synthetic chemistry are of interest not only for their potential impact on efficiency and productivity, but also on human resources and safety, since synthetic chemistry poses a number of occupational risks and is largely inaccessible to researchers with physical disabilities. Currently, most automation tools for synthetic chemistry are either designed to perform highly specialized tasks or they are designed as closed-loop systems with minimal interaction between human and machine during a synthesis procedure. We are pursuing an alternative, human-centered approach to robotic tools for synthetic chemistry, in which general-purpose collaborative robots (cobots) offer diverse forms of support to human researchers in the lab. In order to design frameworks for productive scientist-cobot collaborations, we need a deep understanding of the task space in synthetic chemistry labs and the impact of these various activities on the researchers. Based on observations and surveys from a group of experimental scientists, we have identified and analyzed 10 manual tasks commonly performed by researchers in the wet lab, each of which may be broken down into a sequence of sub-tasks. We conducted an in-depth analysis of the two most frequently performed sub-tasks: liquids dispensing and solids handling. Through subcoding, we identified 40 liquid dispensing typologies and 18 solid handling typologies, and evaluated the burden associated with each of these sub-tasks using the NASA TLX. These data will be of value for the design of human-centered automation tools that support, rather than displace, researchers performing manual tasks in the lab, in order to foster a safer and more accessible lab environment.
Method for Constructing a Compact Component Mode Synthesis Model for Analyzing Nonlinear Normal Modes of Structures With Localized Nonlinearities
Authors: Chen Xuan-Hong , Tien Meng-Hsuan 
Published: November 2024

Abstract
Research on nonlinear dynamics analysis is a crucial topic in mechanical and aerospace engineering. The analysis of nonlinear normal modes (NNMs) provides us with an effective mathematical tool for interpreting complex nonlinear vibration phenomena. Unlike the invariant normal modes of linear systems, NNMs often exhibit frequency-energy dependence and cannot be computed using the traditional eigen-decomposition method. Calculating NNMs relies on numerical methods that involve expensive iterative computations especially for systems of a large number of degrees of freedom. To relieve computational costs, this paper proposes a mode selection method for the component mode synthesis technique to enable compact reduced-order modeling of structures with localized nonlinearities. The reduced-order model is then combined with a numerical continuation scheme to establish a low-cost NNM analysis framework. This framework can efficiently predict the NNMs of high-dimensional finite element models. In this work, the proposed framework is demonstrated on an I-shaped cantilever beam with localized nonlinear stiffness. The results show that the proposed approach can identify the key modes in the reduced-order modeling procedure. The NNMs of the nonlinear I-shaped beam structure can then be analyzed with significant reduction in computational costs.
HairTouch: Investigating the Design Space of Hair-Based Tactile Output
Authors: Zhou Yi , Li Yan , Lu Yujia 
Published: November 2024

Abstract
This paper proposes a innovative notification method leverages hair-based tactile cues, HairTouch. This study explored the intensity and diversity of this tactile sense, HairTouch provided subtle and effective reminders in hands-free and eyeless interaction scenarios, comparing to vibration and electrical stimulation. The study focused on five body locations: forehead, ear contours, cheeks, side of the neck, and back of the neck. The experiments were conducted subsequently to explore the design space of HairTouch. The aim of the experiment was to determine the suitable intensity of the hair touch stimuli at these locations and to evaluate the user’s acceptance of this new method of interaction. The results of the study showed that users perceived significant differences in the intensity and locations of touch, suggesting that the touch of the hair could offer a new method of reminding of information. The study also found that the angle of hair’s swings significantly affects the user’s perception, suggesting that this parameter can be manipulated to express different information.
In summary, HairTouch showcases the potential of utilizing hair movement as a innovative and unique method of haptic interaction, offering a new dimension to interactive design. This approach can enhance the privacy of notifications and expand the types of available sensory in immersive technologies such as VR and AR. Future work will investigate the effect of hair movement speed on perception and explore more precise equipment to refine the instrument results.
Sandpiper Food Search Algorithm: A New Optimization Algorithm With Limited Knowledge
Authors: Wira Hadipoernomo Jessica Christa , Watson Bryan C. 
Published: November 2024

Abstract
Optimization problems in mechanical engineering play a crucial role in refining complex system designs, improving performance, and maximizing efficiency across various applications. These problems have become increasingly complex, stretching the limits of conventional methods such as convex optimization or the Newton-Raphson method. To address this challenge, numerous metaheuristic algorithms, often inspired by nature, have emerged including Particle Swarm Optimization, Genetic Algorithm, Bat Algorithm, Firefly Algorithm, and Cuckoo Search. However, most of these existing algorithms have global knowledge, which is unrealistic for some real-world complex systems such as underground mining and spacecraft trajectory. To bridge this gap, this article develops and presents a new biologically inspired optimization algorithm, the Sandpiper Food Search Algorithm. This algorithm is inspired by the food search behavior of sandpipers at the beach where each agent (sandpiper) explores the problem space to find the optimal area by exploiting the local search for candidate solutions around them. Moreover, this algorithm includes the wave action that forces these birds to shift from their current solution to increase exploration of the solution space. The performance of the algorithm is evaluated using four standard benchmark functions in comparison with the Firefly Algorithm. Firefly Algorithm was selected for comparison because it shares similar parameterization characteristics, and its use of decreasing light brightness with distance between fireflies mirrors the limitation of knowledge imposed by the visibility radius in sandpipers. This research provides a conceptual design of the new Sandpiper Food Search Algorithm and its performance when compared to Firefly Algorithm over four benchmark functions. Our work shows that the Sandpiper Food Search Algorithm outperforms the Firefly Algorithm in three of the four benchmark functions with at least 3% improvement in mean best solution and is on average 38% more reliable at finding a solution at least 95% of the optimal.
Influence of Tooth Deflections and Local Contact Deformations on the Tooth-Root Stress of Internal Spur Gears: Definition of a Load-Sharing Coefficient for the AGMA J-Factor
Authors: Pedrero José I. , Sánchez Miryam B. , Fuentes-Aznar Alfonso 
Published: November 2024

Abstract
The extension of the contact interval resulting from loaded tooth deflections and local contact deformations increases the effective contact ratio, which can exceed the value of 2 if the theoretical contact ratio is close to 2, as frequently occurs in internal spur gears. In these cases, the load is shared by at least two pairs of teeth throughout the entire cycle of meshing of one pair of teeth, which results in lower values of the tooth-root stress. This reduction in the root stresses is not adequately considered by the Standard’s calculation methods. This paper presents a study on the influence of load sharing on the tooth-root stress of internal gears. Additionally, a new coefficient to modify the AGMA J-factor is proposed to consider the reduced tooth-root stress in calculations when the effective contact ratio exceeds the value of 2.
Concept Chaining Patterns During Ideation Using Digital Tablets
Authors: Das Madhurima , Meza Jessica , Xu Christine , Yang Maria C. 
Published: November 2024

Abstract
Digital tablets are becoming increasingly popular as a sketching tool, in part because they allow designers to store many sketches in one place and to easily switch between types of sketching tools. These tools include features such as smoothing lines and copy/paste that aim to improve ease and quality of sketching. The ability to copy and paste elements of drawings affords designers the possibility to quickly modify and iterate on sketches, which may in turn influence the way in which designers engage with concept generation. This potential impact of digital sketching tools on concept evolution patterns has been minimally explored in a qualitative manner but not quantitatively. We present the results of a controlled human subjects study that investigates how the use of tablets (iPads) may influence concept evolution patterns when compared to pen and paper sketching during an engineering design concept generation problem.
We explore two main research questions. First, whether or not tablet use for sketching, as opposed to paper and pen sketching, is linked with more concept chaining (sequential iteration on a concept). We hypothesize that ideation on tablets will be linked with more concept chaining due to sketching features on the tablet such as copy and paste. Second, we ask whether or not concept chaining patterns will be different for design prompts that have multiple functional requirements. Here, we hypothesize that the concept chaining patterns for the primary functional requirement of a multi-component prompt will be consistent with the patterns of concept chaining for a prompt with only one requirement, but that secondary functional requirements may not exhibit these clear chaining patterns.
We find that tablet users demonstrate more concept chaining than paper and pen users despite having a similar number of related ideas overall. We also find that concept chaining patterns do change for design prompts with higher numbers of functional requirements especially because not all ideas address all parts of the prompt, but that the primary functional requirement results exhibit the same concept chaining patterns with more chains present on tablet sketching than paper and pen sketching.
Compliant Stepped-Beam Analysis for Deployable Space Structures With Flexible Panels
Authors: Skinner Mitchel , Geyser Andrew , Sargent Brandon , Magleby Spencer , Howell Larry 
Published: November 2024

Abstract
Deployable space structures that incorporate thin, flexible solar cells allow for innovative designs that are capable of high stowing efficiency. The purpose of this work is to present compliant stepped-beam analysis approaches that apply to deployable space structures with flexible panels connected with thin sheets and determine their deflection as a function of their material properties, geometry, and applied moments. Related methods are described. An analytical model and finite element analysis are compared and validated with a physical model. The end angle, coordinates, and radius of curvature of each segment are calculated and the results are compared. Beam thicknesses can be adjusted to increase curvature to intended sections of the beam. The approach is then applied in the preliminary design of a z-fold used as a deployable space array. The compliant-stepped beam deflected in a z-fold pattern provides designers with a straightforward model to analyze the position and curvature of a deployable array and tailor it to meet design requirements. Percent differences of each analysis are presented and discussed. The analytical model agrees well with FEA and physical solution results. Case assumptions are described and validated. Potential for future work related to large deflections of flexible-panel deployable space structures is described.
Force Reduction of 3-RPS Parallel Manipulator Using Mechanical Springs
Authors: Nurahmi Latifah , Nguyen Vu Linh 
Published: November 2024

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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.