bluuebunny/crossref_metadata_2025 huggingface dataset:
Multi-Scale Model Predictive Control for Laser Powder Bed Fusion Additive Manufacturing
Authors: Hong Gisuk , Kim Jaehyuk , Yang Zhuo , Lu Yan , Lane Brandon , Yeung Ho
Published: November 2024

Additive manufacturing (AM) process stability is critical for ensuring part quality. Model Predictive Control (MPC) has been widely recognized as a robust technology for controlling manufacturing processes across various industries. Despite its widespread use, there has been limited exploration into the application of real-time MPC for controlling the laser powder bed fusion (LPBF) AM process through in-situ process monitoring. This paper presents a new framework designed to develop MPC strategies for real-time LPBF control. The framework accommodates various multi-scale approaches, including pointwise, trackwise, layerwise, and partwise methods. This framework considers the diverse needs for material state representation when formulating predictive models, constraints, and objective functions while allowing for predictive control implementation at different scales and frequencies. The utility of this framework is demonstrated through three trackwise MPC case studies, all employing high-speed co-axial melt pool imaging. Simulation results indicate that LPBF systems enhanced with MPC achieve superior performance compared to those governed by open-loop control systems. Additionally, we find that MPC implementations that utilize feedback control at finer scales provide improved process stability, albeit at the expense of increased computational demands. This framework serves as a guide for industrial practitioners, outlining how the implementation of MPC in AM process control can be optimized based on available in-situ sensing capabilities and data acquisition techniques.
Modeling Demand Spikes in Continuously Evolving Products Using Dynamic Customer Preferences
Authors: Walter Ian , Paré Philip E. , Panchal Jitesh H.
Published: November 2024

As the digital marketplace continues to evolve with rapid changes in consumer preferences and the frequent introduction of product updates, traditional models for estimating demand are often unsuitable for use. These models often fail to account for the dynamic nature of product development and the complex decision-making processes of consumers, particularly in the face of product updates. To bridge this gap, this study extends a demand model based on decision field theory (DFT), focusing on a phenomenon largely unexplored in the existing literature: the update engagement spike (UES). Being able to capture this phenomenon, where demand surges following a product update, is crucial for accurately modeling demand around product updates. We extend the DFT-based demand model to capture consumer responses to updates more effectively. We then validate this extended model using real-world data from competing software products, demonstrating its practical use. Furthermore, we outline avenues for future research, including methods for distinguishing between customer segments and attributes with greater accuracy.
Augmenting Bayesian Inference-Based Damage Diagnostics of Miter Gates Based on Image Translation
Authors: Zeng Yichao , Zeng Jice , Todd Michael D. , Hu Zhen
Published: November 2024

Structural monitoring data is essential for the assessment of structural integrity of large civil infrastructures, such as miter gates studied in this paper. For some applications, the amount of monitoring data is limited due to various reasons including but not limited to the availability of sensors, changing states of structural health, inaccessibility of measurement location, etc.. This limited amount of monitoring data could result in uncertainty in structural health assessment. This paper overcomes this challenging issue by proposing a data augmentation method based on image translation for Bayesian inference-based damage diagnostics of miter gates. In particular, we translate the monitoring data of one miter gate to that of another, thereby increasing the volume of monitoring data available for assessing the structural health of a target miter gate. This translation starts with converting the monitoring data of different miter gates into images. After that, Cycle Generative Adversarial Networks (CycleGAN) are employed to accomplish the task of image translation among different miter gates. The translated images (i.e., synthetic monitoring data) are then used together with the true monitoring data for damage diagnostics. Two types of CycleGAN architectures are investigated and compared using a case study. Results of the case study show that the proposed data augmentation method can effectively improve the accuracy and confidence of damage diagnostics of miter gates. It demonstrates the potential of integrating synthetic data generation with probabilistic model updating in structural health monitoring.
Design of a Hollow Flexible Hinge for an Atmospheric Dispersion Corrector
Authors: Kamphof Wilbert E. , Nijenhuis Marijn , Kragt Jan W. , Lewińska-Maresca Mirka A. , Brouwer Dannis M.
Published: November 2024

Compliant mechanisms often have the advantage of high precision at the cost of a limited range of motion. In this research, a single Degree of Freedom hollow compliant hinge is developed for use in an Atmospheric Dispersion Corrector (ADC) found in the Extremely Large Telescope (ELT). The ADC contains two sets of optical prisms to correct for the unwanted dispersion effect that occurs when light meets the atmosphere at an angle. These prisms must perform a near-pure rotation around the optical axis, which must not be obstructed by the geometry of the ADC. This study strives to achieve the required deflection range while having sufficiently high unwanted eigenfrequencies, support stiffness, and low stress levels while bearing a relatively large load. The eigenfrequencies should be sufficiently high because of the vibrations originating from underlying systems in the ELT. This paper presents a comparison of various concepts, optimized with 3D nonlinear numerical finite element and multibody analysis software. Both high kinematic and dynamic performance is achieved through the addition of a kinematic coupler, or synchronizer. The existing concept adapted to the requirements of this work is called the Large Angle Flexure Pivot (LAFP), which is analyzed and expanded upon with a newly developed synchronizer to align with the requirements of the ADC.
Investigating Sustainable Production Planning of an Industrial Progressive Die Stamping Process Using Discrete Event Simulation
Authors: Borbolla Adrián , Aboujarra Mohamed Nezar , Ramanujan Devarajan
Published: November 2024

This paper investigates the use of discrete event simulation (DES) as a potential decision-support tool for environmentally sustainable production planning. We propose a comprehensive methodology that integrates DES design, dynamic CO2 intensity calculations, and in-depth scenario analysis. This approach facilitates simulation-driven production planning with an emphasis on evaluating and reducing the environmental impacts of manufacturing. Our methodology is applied to the evaluation of an industrial progressive die stamping process (PDSP). The PDSP is a part of a production line in a large Danish manufacturing company. The performance of the PDSP was simulated and evaluated for two working days: 09 Jan 2024 and 10 Jan 2024. Key performance indicators like utilization rate and lead time are assessed alongside environmental sustainability metrics such as process CO2 emissions and CO2 emissions per product. Building upon this baseline, we then simulate and compare two alternative production scenarios: i) increased production capacity and ii) variations in planning orders. These scenarios are then contrasted with the baseline model. Our results demonstrate the potential of DES in identifying new production strategies. The first scenario demonstrates a 7% increase in production capacity, corresponding to a 2.6% increase in process CO2 emissions. The second scenario demonstrates a 5% reduction in process CO2 emissions while retaining the same production capacity as the baseline model. These findings illustrate how DES can uncover production plans that prioritize sustainable manufacturing.
Design and Optimization of a Long Travel, Two-Axis Flexural Nanopositioning Stage
Authors: Watts Tyler , Cullinan Michael
Published: November 2024

Multi-axis nanopositioning stages based on compliant mechanisms are a common topic within modern precision engineering due to their high accuracy, repeatability, and low cost compared to industry positioning stage alternatives. This paper details the design, computational optimization, and result evaluation of a two-axis flexural nanopositioning stage based on a modified version of the classic double parallelogram flexure (DPF) bearing (e.g. an underconstraint-eliminating DPF (UEDPF)). The two-axis flexural nanopositioning stage was optimized using response surface model with seven inputs and two outputs. The seven input parameters were the most sensitive geometric parameters for the UEDPF identified in previous work, and the two outputs were the flexure’s peak stress at maximum deflection and the force reaction felt by the 25mm displacement boundary condition. This paper shows that through design optimization, the first resonant mode of a long travel, two-axis flexural nanopositioning stage that has previously been reported in literature can be improved by a factor of two while still maintaining the higher-order resonant modes to be at least an order-of-magnitude higher than the fundamental mode of the stage. This is critical because increasing the fundamental mode without sacrificing the higher order modes will allow for a higher bandwidth controller to be implemented on this nanopositioning stage. The end goal of the positioning stage detailed in this paper is to be implemented within a micro-SLS 3D printer.
Exploring CausalWorld: Enhancing Robotic Manipulation via Knowledge Transfer and Curriculum Learning
Authors: Wang Xinrui , Jin Yan
Published: November 2024

This study explores a learning-based tri-finger robotic arm manipulating task, which requires complex movements and coordination among the fingers. By employing reinforcement learning, we train an agent to acquire the necessary skills for proficient manipulation. To enhance the efficiency and effectiveness of the learning process, two knowledge transfer strategies, fine-tuning and curriculum learning, were utilized within the soft actor-critic architecture. Fine-tuning allows the agent to leverage pre-trained knowledge and adapt it to new tasks. Several variations like model transfer, policy transfer, and across-task transfer were implemented and evaluated. To eliminate the need for pretraining, curriculum learning decomposes the advanced task into simpler, progressive stages, mirroring how humans learn. The number of learning stages, the context of the sub-tasks, and the transition timing were found to be the critical design parameters. The key factors of two learning strategies and corresponding effects were explored in context-aware and context-unaware scenarios, enabling us to identify the scenarios where the methods demonstrate optimal performance, derive conclusive insights, and contribute to a broader range of learning-based engineering applications.
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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.
Pareto Front Exploration for Hypoid Gear Multi-Objective Optimization Model
Authors: Wei Xinqi , Wang Yawen , Zhang Weiqing , Lim Teik C.
Published: November 2024

Optimizing hypoid gear is crucial in modern gear design to meet the increasing demand for high durability, high efficiency, and low noise. The Pareto front is used to illustrate the trade-off between the conflicting objectives in multi-objective optimization models. However, exploring the Pareto front optimization space for hypoid gear is challenging due to its non-convex nature and non-linearity, primarily caused by the complex contact behavior. These complexities often result in larger computational costs and can lead to the convergence of optimization into a local solution rather than a global one. In this study, a multi-objective optimization model is established to optimize hypoid gear contact performance such as the peak-to-peak value of loaded transmission error and maximum contact pressure. The Pareto fronts of optimization objectives are obtained based on the Pattern Search method. To explore the optimizable space of the Pareto front, the various initial values and optimization objectives range are specified. Several Pareto front and corresponding solution distributions are obtained and analyzed. Also, the best trade-off optimization solutions are reported to demonstrate the effectiveness and correctness of optimization. The results show that the optimization space of the local Pareto front has been explored effectively and gives insight for gear designers to decision-making.
Development of a Rapid Response Online Product Marketplace
Authors: Elhabashy Ahmed E. , Günay Elif Elçin , Sim Jaemun , Clements Mark W. , Benaggoun Hamza , Kabiri Mahdi , Haapala Karl R. , Okudan Kremer Gül E. , Kim Kyoung-Yun
Published: November 2024

The coronavirus pandemic has exposed deficiencies of global supply chains in rapidly responding to urgent medical needs and demonstrated the challenge of promptly acquiring medical supplies during emergencies. Even under policy tools and using existing online alternatives, being able to promptly acquire medical supplies, including personal protective equipment and medical devices, is still a concern for future emergency situations. Hence, the details for an open, extensible, service-oriented cloud platform that enables the rapid matchmaking between consumers and manufacturers in emergency situations is presented in this work. More specifically, the system architecture deployed for the platform, along with the details of its main components are discussed. In addition, the results of implementing a simplified prototype of the platform, where a healthcare worker is using the online product marketplace to make an order for a replacement part of a respirator mask, are also briefly demonstrated. By using this online product marketplace, manufacturers will benefit from improved accessibility to their products, services, and manufacturing resources (even under normal operations), while consumers will gain unprecedented access to existing products and manufacturing resources. The platform also has the potential to improve the nation’s ability to prevent, prepare for, and respond to future pandemics or other emergency events.
Analyzing Bifurcations of a Three-Degree-of-Freedom Gear Transmission Considering Multi-Piecewise Linear Functions
Authors: Liao Fulin , Huang Jianliang , Zhu Weidong
Published: November 2024

Bifurcations of a gear transmission system subjected to forced excitations considering multi-piecewise linear functions are investigated in this work by using the incremental harmonic balance (IHB) method. The nonlinear motion ordinary differential equations of the gear transmission system are formulated by employing the Newton’s second law. Analytical results reveal abundant bifurcation phenomena, including period-doubling bifurcations, saddle-node bifurcations, and Hopf bifurcations, which have not been exhibited in existing investigations on nonlinear behaviors of the gear transmission system. The bifurcation types are determined with the ways leaving the unit circle of Floquet multipliers.
Multi-Lattice Topology Optimization With Lattice Representation Learned by Generative Models
Authors: Chen Jiangce , Baldwin Martha , Narra Sneha , McComb Christopher
Published: November 2024

Additive manufacturing (AM) technologies are often capable of fabricating geometries that are more complex than traditional manufacturing methods. A notable innovation enabled by AM is the fabrication of multi-lattice structures, an advanced design concept featuring an array of heterogeneous lattices in the mesoscale that are arranged to achieve a diverse distribution of material properties at the macroscale. Compared to uniform lattice structures, multi-lattice structures permit greater design freedom and a larger design space, which makes it possible to achieve superior structure performance. However, the expanded design space introduces a substantial increase in the complexity of multi-lattice structure design. There is still lack of an optimization framework that can maximize the physical properties of the macro-structures through fully exploiting lattice diversity while ensuring lattice connectivity.

To solve these challenges, this paper introduces a multi-scale topology optimization (TO) framework for multi-lattice structures which simultaneously optimizes the structure topology at macroscale and the lattice heterogeneity at mesoscale. The distribution of the pseudo-densities and lattice parameters are represented by neural networks (NNs) whose weights and biases are the design variables. The spatial gradients of NN over the physical domain reflect the dissimilarity of adjacent lattices. So, the connection between the lattices can be implicitly constrained by restricting the spatial gradients of NNs. The diversity of the lattices is guaranteed through a generative lattice model which is trained over a large lattice dataset and is embedded into the optimization framework. The performances of various NN types are compared, and we found that Fourier Neural Operators (FNOs) have the best flexibility in balancing the lattice diversity and local connectivity. In the design problems of structural compliance minimization under complex loading conditions, our results show that the multi-lattice TO structures achieve a higher stiffness-to-weight ratio than normal TO structures.
Beyond the Paper: Exploring the Relationship Between the Participation of Women and the Social Relevance of Research Topics in Engineering Design
Authors: Running Ivyann O. , Parkinson Bethany , Magleby Spencer , Toh Christine
Published: November 2024

The increase in participation of women in Science, Technology, Engineering, and Mathematics (STEM) fields has not been uniform. In mechanical engineering, the proportion of women who participate in the profession at all levels is disproportionately low. One factor identified by education researchers that has the potential to draw higher numbers of women to the discipline is the presence of socially relevant and impactful applications of science concepts. To better understand this relationship, the gender distribution of researchers participating in sub-conferences within the mechanical engineering International Design Engineering Technical Conferences & Computer and Information in Engineering (IDETC-CIE) was investigated. The social relevance of publications within two sub-conferences with differing levels of women participants, the “Design Theory and Methodology” (DTM) and “Mechanisms and Robotics Conference” (MR), was compared. Our results showed that both sub-conferences had comparable numbers of socially relevant papers, but differed in terms of which categories of social relevance were emphasized and if specific populations were explicitly mentioned in the studies. These results indicate that the relationship between the participation of women in engineering research communities and the focus on socially relevant applications is complex and multifaceted, potentially affected by not just if socially relevant concepts are studied, but also how these concepts are integrated into the shared values and norms of each community.
A Framework to Support Co-Design Exploration of Manufacturing Supply Networks for Resilience
Authors: Baby Mathew , Mukundan Pooja , Nellippallil Anand Balu
Published: November 2024

Manufacturing Supply Networks (MSNs) involve group decisions to achieve group goals and network decisions to achieve network goals. These decisions are made across multiple levels of a decision hierarchy. Given the frequent disruptions in MSNs, ‘resilience’ — the ability to maintain satisfactory network functionality despite disruptions, is a vital network goal. When designing MSNs for resilience, the resilience and group goals often conflict, requiring simultaneous consideration of network and group decisions. Limited information in the early stages of MSN design necessitates focusing on design exploration. Hence, facilitating ‘co-design exploration’ — a simultaneous exploration of network and group solution spaces is crucial.

Current approaches for designing MSNs for resilience do not support simultaneous consideration of network and group decisions. To bridge this gap, we present the Co-Design Exploration of MSNs for Resilience (CoDE-MR) framework to facilitate co-design exploration of the network and the groups.

CoDE-MR framework allows designers to model multilevel network and group decisions and their interactions, manage disruptions, and visualize and simultaneously explore the multilevel network and group solution spaces. In the framework, we integrate a combination of Preemptive and Archimedean formulations of the coupled-compromise Decision Support Problem construct with Resilience Index metric and interpretable Self-Organizing Map (iSOM)-based visualization to facilitate co-design exploration of MSNs for resilience. The framework’s efficacy is demonstrated using a steel MSN test problem, considering network and group decisions across two levels. The use of information flow and generic constructs makes the framework generic and well-suited for co-design exploration of multilevel systems to ensure resilience.
Trajectory Following Control of a Cable Driven Parallel Robot on Planes With Various Inclined Angles
Authors: AlQaydi Maitha , Yu Ziqing , Jin Lei , AlAttar Ahmad , Taha Tarek , Gan Dongming
Published: November 2024

This paper explores the control mechanisms for Cable-Driven Parallel Robots (CDPRs) operating on inclined surfaces and addresses the challenges introduced by gravity. It provides a comprehensive analysis aimed at evaluating the effectiveness of various control strategies, including Proportional-Derivative (PD) controllers, gain scheduling, and Active Disturbance Rejection Control (ADRC), across different inclinations. The study explores how these approaches contribute to maintaining operational efficiency and precision in trajectory generation. The effectiveness of the proposed control model in generating trajectories is demonstrated through simulations. These simulations reveal the limitations of PD controllers, gain scheduling for CDPRs on inclined surfaces, and show ADRC as a better alternative. This research significantly improves the efficiency and accuracy of CDPRs in trajectory generation, broadening their practical applications across diverse scenarios.
Topology Optimization of Multibody Systems Undergoing Dynamic Loading Using an Equivalent Static Displacement Method
Authors: Gupta Aakash , Tovar Andres
Published: November 2024

Structural topology optimization for vehicle structures under static loading is a well-established practice. Unfortunately, extending these methods to components subjected to dynamic loading is challenged by the absence of sensitivity coefficients: analytical expressions are unavailable and numerical approximations are computationally impractical. To alleviate this problem, researchers have proposed methods such as hybrid cellular automata (HCA) and equivalent static load (ESL). This work introduces a new approach based on equivalent static displacement (ESD). The proposed ESD method utilizes the nodal displacement of the dynamic analysis at various simulation times to define the boundary conditions for corresponding models — one model per simulation time. A scalarized multi-objective function is defined considering all the models. A gradient-based optimizer is incorporated to find the optimal topology. Then, a new dynamic analysis is performed, the new ESD is defined for each model, and a new topology is obtained. The iterative process continues until convergence. Furthermore, this work also demonstrates the extension of the proposed ESD method in the topology optimization of multibody systems. To this end, the result shows an internal combustion engine’s iterative topology optimization of the connecting rod and piston. Additionally, results from multiple load case problems have been presented to prove the effectiveness of the ESD methodology.