bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effects of Wave Energy Converter Array Configurations and Geometries on Wave Field and Power Output
Authors: Vitale Olivia , Haji Maha 
Published: November 2023

Abstract
Wave energy converters (WECs) have the potential to be major players in the renewable energy sphere. WEC arrays are well suited for grid-scale energy, offshore aquaculture, water desalination, coastal communities, and other niche applications. Understanding the effect of an array on the surrounding wave field is crucial to accurate modeling, determining interactions between each individual WEC, and finding power output. Array configuration and WEC geometry can alter power output, either constructively or destructively. This study models different configurations and WEC geometries in a 4-WEC array using a Boundary Element Method (BEM) solver. The geometries considered are heaving sphere and cylinder point absorbers. The software considers the bodies to be fixed while altering the incoming wave field. The calculations analyze the perturbed wave field and power output by determining radiation and diffraction effects from the array as well as body motion. Two power take-off (PTO) schemes are considered to analyze energy extraction. Obtained results show that regular arrays (1 × 4) of all geometric configurations tend to have milder effects on the wave field. When mixing geometries in the 1 × 4 arrays, similar levels of disturbance are found, but the wake lacks symmetry. Arrays with multiple rows (2 × 2) display higher disturbances. Inline array configurations, where WECs are placed directly behind each other relative to the incoming waves, are most sensitive to varying geometries. All regular arrays demonstrated constructive effects on power output, while the staggered configurations displayed the greatest degree of destructive effects. To extract the most energy, an optimized spring-damper is preferred, but this is more challenging to realistically implement. If the designer’s primary goal is increasing array efficiency, the regular array formation is most favorable. However, if the designer looks to decrease ocean space used, potentially share mooring lines, and produce meaningful energy, the inline formation is a reasonable choice.
Kirigami Corrugations: Strong, Modular, and Programmable Plate Lattices
Authors: Parra Rubio Alfonso , Mundilova Klara , Preiss David , Demaine Erik D. , Gershenfeld Neil 
Published: November 2023

Abstract
Plate lattices are high-performance lightweight structures, exhibiting up to twice the yield strength and stiffness compared to truss lattices of similar geometric arrangement and relative density. Although they are of great interest for research and structural engineering applications, their complex manufacturing and assembly processes limit their practical use, with sandwich panels being an exception. This paper presents a novel approach to the design and modular assembly of folded custom 3-dimensional plate lattices as structural corrugations for use in structural engineering and robotics applications. The plate lattice structural corrugation uses a building block strategy and incorporates custom modified unit cells based on the Miura-ori. This transformation involves expanding the top and bottom zig-zag crease lines into facets and orienting them in space. The resulting modified pattern is referred to as the Kirigami Expanded Miura. The unique structure of these lattices not only provides exceptional mechanical performance as static structures, but also allows for the design of anisotropies in their flexural stiffness by alternating between the Maxwell criterion on bending-dominated or stretchdominated cells. These anisotropies can have value differences of up to 24 with the same geometry, making them ideal for robotic morphing applications. We validate our proposed technology by characterizing the mechanical performance of this new building system and comparing it with state-of-the-art corrugations. We demonstrate the potential of this approach by designing, manufacturing, and modularly assembling multiple structures and robots with single and double curvature.
Quantifying the Sustainability and Robustness of Manufacturing Systems Using Energy and Ecological Network Analyses
Authors: Hassan Hadear , Payne Emily , Layton Astrid 
Published: November 2023

Abstract
Global issues, such as supply chain disruptions, have increased awareness of the importance of manufacturing systems being able to quickly bounce back from disturbances. This necessary response is in addition to the importance of mitigating climate change, maintaining market competitiveness, and eliminating unnecessary waste. Two analysis types are compared here: 1) a thermodynamic exergy analysis to quantify a manufacturing system’s energy and material efficiency and 2) an ecological network analysis as a quantitative representation of the system’s sustainability and robustness. Several manufacturing structures, including different processes ranging from the traditional to advanced, like injection molding and binder jetting, are examined in terms of the system response to changes. The findings indicate that the thermodynamic approach efficiently evaluates the efficacy of energy and resource conversion to create a final product. The ecological network approach was also found to provide useful insights on both the environmental efficiency of the systems as well as the resilience. These results are useful when combined for suggesting system layouts and operations that holistically improve a manufacturing system’s design. The findings indicate that existing manufacturing infrastructure needs to be redesigned to better withstand and recover from unforeseen disruptions. Introducing features such as recyclability and combining multiple types of manufacturing processes can enhance the overall resilience of the system. The work suggests that the bio-inspired systems analysis approach when coupled with connectivity and energy-related factors can lead to enhanced manufacturing system designs.
Early Prediction of Human Intention for Human-Robot Collaboration Using Transformer Network
Authors: Zhang Xinyao , Tian Sibo , Liang Xiao , Zheng Minghui , Behdad Sara 
Published: November 2023

Abstract
Activity recognition is a crucial aspect in smart manufacturing and human-robot collaboration, as robots play a vital role in improving efficiency and safety by accurately recognizing human intentions and proactively assisting with tasks. Current human intention recognition applications only consider the accuracy of recognition but ignore the importance of predicting it in advance. Given human reaching movements, we want to equip the robot with the ability to predict human intent not only with precise recognition but also at an early stage. In this paper, we propose a framework to apply Transformer-based and LSTM-based models to learn motion intentions. Second, based on the observation of distances of human joints along the motion trajectory, we explore how we can use the hidden Markov model to find intent state transitions, i.e., intent uncertainty and intent certainty. Finally, two data types are generated, one for the full data and the other for the length of data before state transitions; both data are evaluated on models to assess the robustness of intention prediction. We conducted experiments in a manufacturing workspace where the experimenter reaches multiple scattered targets and further this experimental scenario was designed to examine how intents differ, but motions are only slightly different. The proposed models were then evaluated with experimental data, and further performance comparisons were made between models and between different intents. Finally, early predictions were validated to be better than using full-length data.
A Geometric Method for Static Balancing of Spatial Mechanisms Using Springs
Authors: Wang Jieyu , Kong Xianwen , Tian Yingzhong 
Published: November 2023

Abstract
This paper presents a geometric method for static balancing of spatial mechanisms using only springs. The method applies to mechanisms composed of revolute (R) joints with arbitrary parameters. This method leads to systems with constant potential energy, including the potential energy associated with gravity and the elastic potential energy stored in the springs, hence make the systems more energy-efficient. In the literature, the static balancing of the 1-link manipulator and spatial manipulators without the consideration of the intermediate link weights has been discussed, using the algebraic method which needs lots of derivations or auxiliary parallelograms. This paper is to provide an efficient way to construct statically balanced spatial mechanisms with all the link weights considered. The proposed method is a geometric approach and can achieve static balancing without or with few derivations. In addition, an auxiliary mechanism with 3-degree of freedom (DOF) translational motions is introduced for the accommodation of the springs. In addition, the method can be applied to mechanisms with multiple modes. An 8R linkage and a 3-4R parallel mechanism with multiple motion modes are presented as examples to illustrate the proposed method. The method of equivalent virtual center of mass is also adopted for the 3-4R parallel mechanism to reduce the number of springs.
Measuring the Health of Makerspaces During Large Disruptions Such As the COVID-19 Pandemic
Authors: Crose Claire , Blair Samuel , Layton Astrid , Linsey Julie 
Published: November 2023

Abstract
As the popularity of makerspaces and maker culture has skyrocketed over the past two decades, numerous studies have been conducted to investigate the benefits of makerspaces for university students and how to best establish an inclusive, welcoming environment in these spaces on college campuses. However, unprecedented disruptions, such as the COVID-19 pandemic, have the potential to greatly affect the way that students interact with makerspaces and the benefits that result. In this study, a survey asking about prior makerspace involvement, tool usage, and student demographics was administered to students who use academic makerspaces at two large public universities. Survey data was collected for three semesters (Fall 2020, Spring 2021, and Spring 2022) and spanned both during and after the height of the COVID-19 pandemic. To quantify the differences between the semesters, nestedness and connectance metrics inspired by ecological plant-pollinator networks were utilized. These ecological metrics allow for the structure of the interactions of a network to be measured, with nestedness highlighting how students interact with tools and connectance with the quantity of student-to-tool interaction. The network analysis was used to better gauge the health of the makerspace and the type and frequency of interactions between tools. The raw survey data combined with the ecological metrics provided unique insight into the struggles the makerspaces encountered throughout the pandemic. It was found that nestedness, a measure of system stability, decreases with a decrease in tool usage. Additionally, the higher the connectance the more students interacted with the space. Utilizing metrics such as these and better understanding student tool interactions can aid makerspaces in monitoring their success and maintaining a healthy and welcoming space, as well as tracking the current health of the space. In combination with the survey results, a deep understanding of what challenges the space is facing can be captured.
Finite Element Contact Analysis and Redesign of Rotorcraft Planetary Gear Sets With Asymmetric Profiles
Authors: Marambedu Karthikeyan , Hurrell Michael , Vijayakar Sandeep , Krantz Timothy , Valco Mark 
Published: November 2023

Abstract
This article explores the use of asymmetric gear teeth for rotorcraft main rotor planetary gearsets. Two baseline designs having symmetric gear teeth were selected for redesign studies. Finite element-based contact analyses were used to compare designs. Bending stress predictions for the two existing baseline designs were compared to strain gage measurements to confirm the suitability of the modeling. The same modeling method is then used to assess design changes to improve the bending stress and contact pressure magnitudes using asymmetric tooth profiles. For the first redesign study, the selected gearset is mildly asymmetric, with a 4° difference in pressure angles between the two sides of a tooth. For the second redesign study, the selected gearset is strongly asymmetric with a 18° difference in pressure angles. The results demonstrate that the asymmetric tooth approach provides additional design flexibility toward balancing the contact stresses and bending stresses of the sun, planet, and ring members.
Theoretical Piezoelectric Energy Generation From Thoracic Expansion During Respiration for Smart Wearables
Authors: Carlson Sam , Martini Angela K. , Clarke-Sather Abigail R. , Alves Paulo H. 
Published: November 2023

Abstract
Energy generation and storage is vital to power many sensors used in research and industry. The process by which energy is generated and stored can have critical impacts on the environment. Piezoelectric energy harvesting is one method of renewable energy generation that can have reduced environmental impacts compared to other non-renewable forms of electricity generation. Additionally, it is capable of being portable, which is ideal for powering smart wearables. The respiratory rate monitoring garment is a smart wearable that monitors the breathing rate of the wearer. Piezoelectric energy harvesting was explored as a power source for the respiratory rate monitoring garment by initial theoretical calculations of potential maximum voltage output from the wearer’s chest motion during respiration to improve the portability of the design. The constitutive equations in combination with a simple circuit representation of the piezoelectric element in series with the respiratory rate sensor was used to generate equations to calculate the maximum voltage available during normal respiration. The maximum voltage during one minute of simulated respiration was determined to be 3.3 mV, which is much lower than the necessary 6.8–7.15 V needed to power the respiratory rate monitoring garment. To make piezoelectric energy harvesting suitable for the respiratory rate monitoring garment, the voltage demands of the microcontroller and LoRa wireless transceiver would have to be significantly lowered, or a new method of data collection would have to be developed. While it was determined that piezoelectric energy harvesting could not provide enough voltage for the existing design of the respiratory rate monitoring garment, it may be suitable for other low voltage sensors or applications.
Effect of Reflection and Incubation on Artificial-Intelligence-Assisted Design Space Exploration
Authors: Song Hyeonik , Viros-i-Martin Antoni , Selva Daniel 
Published: November 2023

Abstract
Design space exploration is a method for gaining insights into a space of design alternatives to make important design decisions. Current research in design space exploration is studying the use of Artificial Intelligence (AI) agents (called cognitive assistants) to provide cognitive support to human designers and help them obtain insights from large datasets. This work explores the collaborative exploration of design spaces between human designers and AI cognitive assistants. A human subject study was conducted to 1) investigate whether cognitive interventions during a design task, specifically a question-based reflection period or an incubation period, influence the way designers collaborate with cognitive assistants; and 2) characterize the effects of those interventions on the design space exploration process and outcomes (performance and designer learning). The study aims to determine whether cognitive interventions that prompt designers to reflect on their collaboration with the AI agent could improve the quality of their collaboration. The results indicate that the two intervention methods have opposing effects on design performance. Participants who had an incubation period amid design space exploration improved their design performance significantly more than participants who had a self-reflection period amid design space exploration. Participants who had different interventions during the design task exhibited different forms of human-AI collaboration which possibly explain the opposing effects of reflection and incubation periods on their design performance. The findings could serve as a basis for the development of AI-assisted design tools that guide designers toward more effective human-AI collaboration.
Study the Impact of Cross-Sections on the Energy Absorption of Architected Materials
Authors: Wang Xinnian , Rastegarzadeh Sina , Pan Yayue , Huang Jida 
Published: November 2023

Abstract
Advances in additive manufacturing (AM) facilitate the fabrication of as-designed architected metamaterials with complex cellular structures. The mechanical properties of these materials can be tailored by their design, i.e., the geometries of the cellular structures, which results in a design-by-demand paradigm and can be employed in a broad range of tasks such as lightweight and energy absorption applications. Despite the extensive studies on designing the geometries of cellular structures, limited works have investigated the impact of cross-sections on the final properties of architected materials. The cross-section is critical since the cellular structures are connected with repeated unit cells, and detrimental stress concentrations are found at all intersections of structural members, which leads to low strength and poor recoverability. In this work, we investigate how the cross-sections of cellular structure affect its energy absorption performance. Specifically, we studied both bending and stretching-dominated cellular structures: five typical strut-based cells were chosen, and each structure is designed with four different types of cross-sections — circular, square, diagonal-square, and rectangular. The structure specimens are printed with lab-developed stereolithography (SLA) printer. Physical compression tests are conducted, and the impacts of cross-sections on final properties are compared and discussed. Designed strategies based on the experimental results are summarized.
Gearbox Fault Detection via Physics-Informed Parallel Deep Learning Model Architecture
Authors: Zhou Qianyu , Tang Jiong 
Published: November 2023

Abstract
Ensuring the safety and reliability of mechanical system significantly hinges on accurate fault diagnosis of critical components comprising the system. Automating the diagnosis process can enhance efficiency and consistency of accuracy in the diagnosis stage. Deep learning methods tend to be playing increasingly indispensable roles in the establishment of the automated fault detection framework; however, they consistently require abundant data resources to leverage the uninterpretable features for fault detection tasks, which is posing major hindrances especially for situations where data collection for fault scenarios is expensive. Moreover, the physically unexplainable feature extraction process renders the nongenerative data augmentation preferrable by feeding the raw information repetitively without being informed of physics integrated in the data. The opaqueness of model greatly hinders the efficient exploitation of the raw data. To address these challenges, it is proposed in this research that the physics-informed parallel model architecture can accommodate miscellaneous physically meaningful features in addition to the raw information. Specifically, the scalogram extracted by continuous wavelet transform is designated to be input together with raw data to the binary branches respectively in the parallel deep learning model architecture. By incorporating the extra interpretable features and information, our approach can completely leverage the data of restricted availability and render the model training physics-guided and understandable. The proposed algorithm is implemented on gear fault experimental data acquired from laboratory testbed to exhibit the physicsguided training process and the outstanding performances on varying size of data.
Design Verification of a Multi-Modal Compliant Mechanism Intended to Enhance Safety in Handheld Laparoscopic Tissue Grasping
Authors: Aguirre Milton E. , Fernández Montoya Mauricio , Gallego Sanchez Juan Andres , Kommuri Krishna D. 
Published: November 2023

Abstract
This work shows preliminary results on the process of design verification for a multi-modal compliant mechanism using computational and experimental comparisons. The technology solution replaces the conventional stiff-hinged tooltip of a laparoscopic surgical grasper with a monolithic contact-aided compliant mechanism tooltip that exhibits multi-modal stiffness properties for enhancing haptic feedback and safety during soft tissue grasping. A finite element model is developed to predict the force transmission behavior of the compliant mechanism undergoing large deformations, beam buckling, and intentional contact during its operational conditions. The authors define and compare metrics of mechanism performance (actuation force and pinch force as a function of input displacement) to verify the accuracy of predicting these nonlinear behaviors using Finite Element Modeling. The computational predictions show a −8.89% change in actuation force between the first and second stiffness regions and +70.60% change between the second and third stiffness regions, as compared to 3.69% and +90.32% corresponding to the experiments, respectively. The computational predictions of pinch force show a change of −21.57% versus a change of −21.52% for the experiment between stiffness regions 1 and 2; and a change of +17.09% compared to +15.19% between stiffness regions 2 and 3 for the simulation and experiments, respectively. Overall, the computational tool was capable of modeling the nonlinear behaviors of this multi-model compliant mechanism. Future work will refine the computational model to improve prediction accuracy and study the influence of physical and geometric parameters on mechanism performance.
Sensitivity Analysis of Tension Distribution Algorithms for Cable-Driven Parallel Robots
Authors: Di Paola Vincenzo , Caro Stéphane , Zoppi Matteo 
Published: November 2023

Abstract
Cable-Driven Parallel Robots (CDPRs) constitute a class of robots which displace a load by means of cables, pulleys and electric motors. Since cable tensions have to be bounded above and below, their use to guide the end-effector continues to be a challenging task. Indeed, several Tension Distribution Algorithms (TDAs) have been developed so far to properly steer the load. Although comparisons between them have been made, no performance index is established to evaluate numerically such methods. Therefore, this paper aims at defining a performance index establishing the sensitivity of a TDA by looking at the maximum tension variation that stems from a change in the external wrench. This would help both comparing and choosing the proper TDA among the existing ones. The definition of the index pops out naturally from the linearization of the equations defining the optimization problems. Hence, in this paper, commonly used TDAs are reproduced and then compared using the defined index. In order to show an application of it, a planar CDPR with four cables and a point mass load is considered. The simulations aim at characterizing its Wrench-Feasible Workspace (WFW) by means of the sensitivity index hereby defined. This will allow quantitative comparisons between above-mentioned TDAs.