bluuebunny/crossref_metadata_2025_split huggingface dataset:
Stress Evaluation at the Root Fillet in Cylindrical Gear: A Semi-Analytical Approach Compared With Standard and Numerical Methods
Authors: Bruzzone Fabio , Rosso Carlo 
Published: November 2024

Abstract
The evaluation of the fatigue life in gears due to bending loads is still an important task due to the necessity to increment the power density of the gearboxes but reducing the weight. The usage of Finite Element Method allows to properly evaluate the stress distribution in the root fillet, also taking into account the actual contact ratio and the load sharing factor. However, such procedure is time consuming because it requires a final geometry of the gear pair and long time for preparing and solving the model. On the other hand, the standards provide a quick and precautionary insight of gears, but loosing info regarding the actual force sharing trend among teeth. The aim of this paper is suggesting a strategy for evaluating stress and fatigue behavior of the gear during the predesign phase, where the geometry is not yet finalized, but the choices are easier and less costly. The paper analyzes the ISO 6336 standard in the 2019 version, compares it to the FEM solution and proposes a semi-analytical method that maintains a good accuracy and a low computational cost. The proposed strategy also offers the possibility to correctly estimate the gear performance, because the trend of stress can be easily considered along the meshing cycle.
Design Guidelines for Repurposing: A Comprehensive Methodological Literature Review
Authors: Dörnbach Adrian , Luttmer Janosch , Nagarajah Arun 
Published: November 2024

Abstract
Considering that 80% of the environmental impact of a product result from their design, aligning products for circularity is a very promising approach to enable a Circular Economy. Most of the today’s efforts of circular product design are focusing on recycling, which is a low valuable circular strategy as it does not maintain the value added to products and requires a high investment of energy and CO2-emission for the recycling and the production of new products. Recent published works have shown promising results by applying sustainable design aspects into the product engineering process. Repurposing products seems to have a huge potential with closing the gap between reuse and recycling but seems not well researched. As a foundation for further research, we provide an extensive literature review on the design for repurpose. Following the PRISMA guidelines on methodological literature search, we defined search criteria, keywords, and queries to map the scientific landscape of the last decade. The results were filtered and grouped into different design aspects for repurposing. The analysis indicates that there are fundamental design aspects for products with multiple life cycles that can be applied to repurposing. Additionally, various specific design aspects for repurposed products were identified. Furthermore, major research aspects that allow for improvement were identified. Firstly, there is limited data on general design aspects for products with multiple life cycles. Secondly, there are currently no applied design methods for repurposing.
Model Updating of Constitutive Relation of Ti64 Alloy Based on Gleeble Testing
Authors: Xu Dong , Frame Lesley D. , Kim Jeongho , Tang Jiong 
Published: November 2024

Abstract
This study investigates the mechanical behavior of Ti64 alloy at high temperature (750°C) using digital twinning technology based on finite element analysis in combination with Gleeble testing. In the realm of high-temperature alloy materials, empirical constitutive models, such as the Johnson-Cook constitutive relation, have conventionally been utilized to delineate the mechanical behavior of metallic substances under conditions of elevated temperatures and strain rates. This research applies a modified Johnson-Cook model tailored specifically for characterizing the unique strength hardening mechanisms inherent to Ti64 alloy. Subsequently, computational simulation is conducted to implement the modified Johnson-Cook model within finite element analyses. Furthermore, to address the computational challenge inherent in finite element simulation based inverse analysis through optimization, a multi-response Gaussian process model is constructed as a surrogate model to replicate thermomechanical analyses, thereby facilitating efficient simulation. Finally, the parametric identification is cast into an optimization problem, that minimizes the difference between Gleeble testing results and the model prediction in the parametric space, to determine the model parameters of the modified Johnson-Cook model. This framework is successfully applied to the model updating of Ti64 alloy. The algorithms, including the custom user subroutines and the parameter identification process, provide valuable tools for exploring the constitutive relations of various alloys.
Integrating Cost, Reliability, and Renewable Energy for Robust Microgrid Design and Operations
Authors: Ashmore Samuel , Odonkor Philip 
Published: November 2024

Abstract
This paper presents a comprehensive framework for micro-grid design and operation, achieving a balance between economic efficiency and reliability. Employing Monte Carlo simulations, it examines various operational scenarios to elucidate the balance between cost-effectiveness and outage resilience. The study considers key factors such as renewable energy integration (solar), battery storage sizing, grid interconnection, and fluctuating building loads. The findings underscore the substantial advantages of integrating solar photovoltaic systems and deliberately oversized battery storage, which collectively enhance microgrid dependability and reduce lifetime operational expenses. The research also emphasizes the pivotal role of grid stability in dictating optimal microgrid investments — unstable grids necessitate greater reliance on renewables and storage for enhanced resilience, while stable grids permit cost-effective strategies through more conservative investments. The paper quantifies the trade-offs between constraints and performance indicators, including battery safety limits, depth-of-discharge, reliability, cost, and energy deficits. Moreover, the framework captures the effects of demand variability on microgrid performance. This comprehensive, multi-faceted framework offers critical insights for making informed microgrid investment decisions, promoting a harmonious approach that aligns with sustainability objectives by integrating environmental considerations, economic feasibility, and energy security mandates.
Mathematical Modeling of Rigid Folding in Hamiltonian Circuits Using Denavit-Hartenberg Parameters
Authors: Edralin Brenon McKaelin , Coleman Nathan , Halverson Denise 
Published: November 2024

Abstract
The aerospace industry places significant emphasis on designing deployable mechanisms that optimize both stow volume and deployed surface area. This paper introduces a novel approach leveraging Denavit-Hartenberg (DH) parameters to mathematically characterize the folding process of interconnected tiles, forming a Hamiltonian circuit. This circuit serves as a blueprint to ensure rigid foldability from a planar configuration into a double stack.
The research outlined in this paper accomplishes several key objectives. Firstly, it delineates the fundamental components required to establish a Hamiltonian circuit for a set of tiles, outlining a folding pattern conducive to a double-stack configuration. Second, it identifies tile pairs suitable for occupying the top and bottom positions within the folded stack, optimizing the arrangement for efficient deployment. Last, the paper meticulously defines DH parameters tailored to the specific circuit under consideration.
The proposed methodology is applied and validated using both square and hexagon tiles, demonstrating its versatility across different geometries. By employing this mathematical model, researchers and engineers can effectively assess and ensure the rigid foldability of deployable structures, offering an accurate and effective means of design evaluation within the aerospace industry. Through this innovative approach, advancements in deployable mechanism design can be achieved, contributing to enhanced performance and functionality in aerospace applications.
Understanding Complex Sketch Recognition Strategies for Intelligent Sketch-Based Design Tools
Authors: Baudoux Gaëlle , Goucher-Lambert Kosa 
Published: November 2024

Abstract
Despite recent advances in multi-modal AI tools (e.g., tools leveraging text-to-image models), there is a significant gap in the ability of such systems to be incorporated into complex design and engineering work. This gap is further exacerbated in contexts where sketch-based inputs are desirable due to the difficulty in recognizing freehand sketches or interpreting underlying human intent. To better surface requirements for emerging sketch-based AI systems for complex design context, we consider a case study involving architectural design; this is a domain for which, to our knowledge, there have been no architectural sketch-based AI tools that recognize freely produced plans or perspectives for downstream applications, including generating inspirational images. Using a Wizard of Oz experimental paradigm, we substitute the “tool” with human agents and conduct a lab-based study in which professional architects complete a design brief using this “tool”. Results demonstrate that human agents not only rely on visible sketch elements (i.e., lines) and architectural drawing codes, but also on their memory of previous lines and their knowledge of the design brief to comprehend perceived lines. In addition to gradually developing an understanding of the designed artifact, human agents also construct an understanding of the designer’s intentions. These activities are crucial for the agent to obtain a functional model of the designed object, beyond a purely topological and geometric perception model. Insights about this human workflow bring new potential techniques of sketch recognition for design tasks, informing the inclusion of new resources and software within AI tools.
Exploring Opportunities for Adopting Generative AI in Automotive Conceptual Design
Authors: Damen Nicole B. , Seo Voho , Wang Ye 
Published: November 2024

Abstract
For AI to responsibly enable and enhance innovative design processes it is necessary to form an understanding of what processes and tools designers currently use, and why. This work employed remote interviews and an in-person workshop (respectively 8 and 6 different designers) to investigate the challenges and opportunities professional automotive designers anticipate towards generative AI tools in the conceptual design phase. The findings indicate that these designers prioritize novelty and value efficiency. Key challenges are finding relevant inspirational images and documentation due to fixation concerns. Although current AI lacks explainability, designers respect its potential to streamline design processes if its outputs can support the collaborative development of innovative automotive designs.
Inverse-Prediction of Material Property in Fused Filament Fabrication/Fused Deposition Modeling
Authors: Lee Junghun , Yusuf Hadiza , Tucker Conrad 
Published: November 2024

Abstract
A great body of research has focused on developing effective methods for predicting the mechanical functionality of 3D-printed products from the printing process and material data. However, the material description often relies on test results on a small number of specimens. Specimen test results are influenced by both the material and the printing process. Therefore, if the printing process differs, mechanical properties obtained from specimen tests cannot be directly applied to 3D-printed parts. However, the relation between mechanical functionality and material data becomes bijective when The geometry and printing process are fixed. In this work, we propose an intermediate process of predicting the material properties from the specimen’s test results, printing process, and geometry. The predicted material can be used, in conjunction with the product’s printing process and geometry, to predict the mechanical functionality of printed products. The authors of the work propose (1) data synthesis on various printing conditions and its specimen test results and (2) a multi-layer perceptron model that predicts the material properties. The proposed model achieved an R-squared value of 0.93 on the prediction of Young’s modulus and 0.96 on the tensile yield strength. Also, the model is evaluated to predict Young’s modulus and tensile yield strength of the material from the specimen test results achieved by finite element analysis on specimen CAD models. The average prediction accuracy for Young’s modulus is 95%, and tensile yield strength is 90%.
Low-G In-Plane Multi-Directional MEMS Inertial Switch With Multiple Acceleration Thresholds
Authors: Algoos Yousef , Xu Qiu , Alahmdi Raed , Feron Eric , Younis Mohammad I. 
Published: November 2024

Abstract
This paper reports a low-g in-plane MEMS inertial switch with multiple acceleration thresholds in multiple sensing directions. Designed for essential monitoring, this device targets low-g accelerations for health and safety applications. The device design utilizes the standard silicon-on-insulator micromachining process (SOIMUMPs) to simplify both fabrication and packaging. Through extensive testing, we demonstrate the switch ability to detect accelerations ranging from 45g to 88g. Despite minor discrepancies due to fabrication imperfections, the experimental results closely match the finite-element simulation results.
Evaluation of Educational Modes in Virtual Reality: A Pilot Study
Authors: Ward Walter , Kado Corey , Kames Elisabeth 
Published: November 2024

Abstract
Engineering education requires diverse pedagogical approaches. Virtual Reality (VR) has been used lately due to its lower entry cost and the ability to display information in unique ways. However, despite VR being a research interest in the last decade, its use in education is comparatively unexplored. In addition, a large portion of existing studies have not incorporated learning theories into the creation of their test environments. This pilot study seeks to explore whether the method of presentation in VR affects the ability of students to understand and recall procedural or conceptual information during training.
To best measure training efficacy and quality, the study is separated into a pre-survey, training, examination, and post-survey. The pre-survey gathered information on VR experience, prior knowledge of the subject matter, and demographics. Participants were separated into three groups: written mode of instruction, video mode of instruction, or an interactive mode of instruction. All modes were presented within a VR environment to maintain consistency across training modes. After the training, participants were tested with a physical assembly evaluation and post-test. Finally, a post-survey is administered to assess training quality and gather self-reported knowledge.
This study suggests that VR was an effective training solution and showed an increase in self-reported understanding of the participants. Additionally, trends between educational mode and performance might become apparent if the study were to be repeated with a larger sample size. Post-test results suggest that learners who spent longer practicing in the environment performed better on conceptual knowledge questions.
Gender Inclusivity: Exploring Design Dynamics and Psychological Safety in Virtual Engineering Design Dyads
Authors: Maar Aliaa , Roy Debrina , Menold Jessica 
Published: November 2024

Abstract
Despite increasing efforts to promote diversity and women’s inclusion in STEM, women’s participation in engineering remains low, with many leaving the field due to negative experiences in academia and the workplace. As a first step towards understanding this discrepancy, we investigated how gender and gender composition within expert engineering design dyads affect the design interactions, team dynamics, and perceived psychological safety in a virtual collaborative environment. Through statistical, stochastic, and network analyses, distinct gender-based patterns was found in design interactions and communication. Our findings indicated that mixed-gender dyads exhibited more action-oriented behavior. Discrepancies were found in perceived psychological safety between male and female participants and interactions that influenced it. Significant differences were found in self and partner perceptions of contribution, especially regarding design decisions. This study highlights the impact of gender dynamics on collaboration and psychological safety in engineering design, emphasizing the need for more gender-based analysis to foster environments that enhance innovation and creativity.
A Continuously Variable Stiffness Mechanism for Tendon-Driven Robots Using Decoupled Stiffening Cables and Hertzian Contact Mechanics
Authors: Molaei Parsa , Legier Terrilyn A. , Gilbert Hunter B. 
Published: November 2024

Abstract
Cable-driven continuum robots that consist of a flexible backbone and are driven by applying tension and displacement on the cables are of interest for use in unstructured environments. Previous work has explored methods to alter the stiffness along the length of the robot via the introduction of additional cables beyond those used for actuation. The introduction of these tendons on the robot would allow the operator to adjust and increase the stiffness value at different locations by prescribing and removing a fixed pretension on the stiffening tendons. This paper presents a continuum mechanism for robotics applications with continuously variable output stiffness. The new method introduces a nonlinear compliance at one side of the tendons that can be adjusted using a lead screw. The nonlinear compliance is provided by a soft hemispherical contact surface that is inspired by the Hertzian contact theory. Through the provided adjustment mechanism, the mechanism output stiffness can be continuously varied without any active control loop. The stiffening cables and adjustable stiffness mechanism allow for the stiffness to be adjusted between a range of 6x to 11x of the stiffness in the case of only actuating cables. The stiffening of a higher-order mode showed a reduced effect, allowing for stiffnesses in the range of 1.5x to 2.2x of the stiffness in the case of only actuating cables.
Reading Users’ Minds from What They Say: An Investigation into LLM-based Empathic Mental Inference
Authors: Zhu Qihao , Chong Leah , Yang Maria , Luo Jianxi 
Published: November 2024

Abstract
In human-centered design, developing a comprehensive and in-depth understanding of user experiences — empathic understanding — is paramount for designing products that truly meet human needs. Nevertheless, accurately comprehending the real underlying mental states of a large human population remains a significant challenge today. This difficulty mainly arises from the trade-off between depth and scale of user experience research: gaining in-depth insights from a small group of users does not easily scale to a larger population, and vice versa. This paper investigates the use of Large Language Models (LLMs) for performing mental inference tasks, specifically inferring users’ underlying goals and fundamental psychological needs (FPNs). Baseline and benchmark datasets were collected from human users and designers to develop an empathic accuracy metric for measuring the mental inference performance of LLMs. The empathic accuracy of inferring goals and FPNs of different LLMs with varied zero-shot prompt engineering techniques are experimented against that of human designers. Experimental results suggest that LLMs can infer and understand the underlying goals and FPNs of users with performance comparable to that of human designers, suggesting a promising avenue for enhancing the scalability of empathic design approaches through the integration of advanced artificial intelligence technologies. This work has the potential to significantly augment the toolkit available to designers during human-centered design, enabling the development of both large-scale and in-depth understanding of users’ experiences.
Design of Compliant Mechanisms Approximating Straight and Circular Paths
Authors: Verotti Matteo 
Published: November 2024

Abstract
Compliant mechanisms are extensively employed in many fields due to their advantages over their rigid-body counterparts. They transmit force, motion, and energy through the elastic deformations of their flexible parts, that often consist of beams with uniform or variable cross-section, with straight or initially-curved axis. Although extensive efforts have been made in the analysis of the deflections, fewer studies dealt with the kinematic aspects associated to the deflection problem. Recently, some investigations focused on the behavior of the pole of the displacements, which characterizes the rigid displacements or on the inflection circle for developing pseudo-rigid-body models. In this paper, the instantaneous geometric invariants are applied to the description of the motion generated by straight flexures. The developed analytical formulation gives kinematic insights on the geometric characteristic of motion up to the fourth order. The invariants lead to the determination of fundamental geometric entities, that are the inflection circle, the cubic of stationary curvature and its derivative, the Ball’s point, and the Burmester’s points. In particular, Ball’s and Burmester’s points identify the special points on the plane that approximate straight paths to the third order, and circular paths to the fourth order, respectively. A possible implementation of the geometric invariants to the design of compliant mechanisms is briefly discussed.
Heterogeneous Transfer Learning for Design and Manufacturing Process Modeling Through Input Mapping and Multi-Fidelity Gaussian Process
Authors: Ravi Sandipp Krishnan , Pandita Piyush , Sun Changjie , Wang Liping 
Published: November 2024

Abstract
Data-driven modeling and optimization have become a cornerstone in several engineering design and modeling endeavors. Data-driven modeling frameworks enable faster optimization cycles and push the boundaries on the performance envelope of the system under study. Even with extended access to computational or manufacturing capabilities, the time\economic cost of evaluating design points are high when the simulation\experiment get closer to ground truth representation. The domain of transfer learning seeks to elevate this constraint on data requirements by leveraging data from other relevant or legacy data sets. However with the strong differentiation in design and manufacturing process, situations arise where the inputs of the target and source domain are heterogeneous in nature. Towards solving this problem, a heterogeneous transfer learning modeling framework is identified and implemented based on input mapping and multi-fidelity Gaussian processes. The framework is applied on three cases studies — (1) cantilever beam (2) ellipsoidal void and (3) friction stir welding. Each of the three case studies represent a different facet of the design engineering community at large (1) Design Differentiated (2) Complexity Differentiated (3) Manufacturing Modality. Key insights are provided on the input mapping transformation between the source\target domain and the performance of the integrated model. The results demonstrate adding source domain data facilitates a more accurate model.