bluuebunny/crossref_metadata_2025_split huggingface dataset:
Predicting Structural System Behavior via a Deep Surrogate Modeling Framework With Imbalanced Feature Learning
Authors: Lee Marshall , Tao Chenxi , Shah Kushal Jignesh , Choi Seung-Kyum 
Published: November 2024

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

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

Abstract
Manual assembly processes are prevalent in manufacturing facilities despite the proliferation of automation and robotics. Humans are adaptable and our capability to make on-the-fly decisions about assembly quality is not yet replaced by artificial intelligence. Therefore, there is a need to ensure that humans are trained well to perform manual assembly tasks. To this end, the research presented in this paper investigates the use of tolerance bands around accelerometer data for the evaluation of manual assembly tasks.
Previous research has identified methods to assess consistency of manual assembly tasks performed. One such approach is based on accelerometer time series data obtained from wrist-worn inertial measurement units. Consistency is computed as a function of point-to-point matches between time series data from two assembly process cycles. This approach assesses minor task performance variations, which may have been a result of desired human adaptability, as inconsistent.
To account for human adaptability within the context of assembly process consistency, the use of tolerance bands on accelerometer data is investigated in this research. Two experiments were conducted where three assemblers assembled a total of three products multiple times. Wrist-worn inertial measurement unit data from these experiments were analyzed based on tolerance bands and inconsistencies are identified using a statistical process control tool - control charts.
It was found that the proposed method was able to identify inconsistencies in the assembly processes performed. A post-hoc video analysis of the inconsistent assembly cycles showed that there are opportunities to improve workstation layout, product design, and work instructions.
A Generative Graph Neural Network-Based Framework for Designing Connectivity-Guaranteed Porous Metamaterial Units
Authors: Wang Zihan , Bray Austin , Naghavi Khanghah Kiarash , Xu Hongyi 
Published: November 2024

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

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

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

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

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

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

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

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

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

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

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

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