bluuebunny/crossref_metadata_2025_split huggingface dataset:
An Enhanced Timed Elastic Band Method for Autonomous Navigation and its Collision Avoidance Reliability Analysis
Authors: Xi Zhimin 
Published: November 2023

Abstract
The timed elastic band (TEB) method is an optimization-based navigation algorithm which uses the idea of an elastic band to connect an agent’s current pose to its goal position. The method is composed of three main levels: i) a feasible global path, ii) deforming collision-free path in real time, and iii) a control method to move the robot along the path. Static and moving obstacles deform the elastic band but the agent will always be pulled toward the goal position while avoiding obstacles. The key issue is the required computation efficiency to calculate the collision-free path in real time under dynamically changing environment. In literature, various case studies have been implemented to demonstrate the performance considering various constraints. However, its collision avoidance reliability under safety critical navigation scenarios has not yet been studied. The major contributions of this paper are summarized as follows. Firstly, an enhanced version of the TEB (i.e., eTEB) is proposed to improve the computational efficiency by explicitly considering non-holonomic constraints of the TEB method. Secondly, collision avoidance reliability analysis of the eTEB method is systematically conducted considering non-reactive moving obstacles. Results indicate that the eTEB method is much more reliable than the DWA for head-on collision avoidance with a single non-reactive moving obstacle at a time.
Methods for Mapping Empirical Data to Authoritative Definitions for Additive Manufacturing Part Validation
Authors: Milaat Fahad Ali , Witherell Paul , Hardwick Martin , Yeung Ho 
Published: November 2023

Abstract
Traditionally, inspection and geometric dimensioning and tolerancing (GD&T) are deployed at the macroscale, where complete parts are tested to meet geometric and functional requirements. The additive manufacturing (AM) process is unique in that it is a digital process where the fabrication of the part at the macroscale is the result of a series of operations at micro and mesoscales. Subsequently any additively manufactured part is the aggregation of many points of localized fabrication, and these parts uniquely expose themselves to full volumetric inspection during part fabrication and postprocessing. While new methods have been developed for designers to communicate process definitions used for the fabrication of an AM part, methods to validate these specifications are lacking. This work will explore challenges in the validating against advanced part and process definitions. Leveraging the concepts of “authoritative product definition,” “digital twin,” and “time stepped commands,” novel methods, built on a “zero dimension” information model, will be proposed to validate AM parts at the macroscale using mesoscale and microscale measurements and observations. Specifically, new data representations in the Standard for the Exchange of Product model data Numerical Control (STEP-NC) are proposed for discretizing AM geometry and process definitions to achieve point-level controls. Through the facilitation of traceable information in authoritative data models, AM process qualification and part acceptance could be streamlined, and reliable and referential data for Digital Twin frameworks and real-time controls could be realized.
Open Quantum Models and Magnetoencephalography Derived Neural Correlates for Engineering Decision Dynamics
Authors: Mollan Calahan , Bowyer Susan , Pandey Vijitashwa 
Published: November 2023

Abstract
Human decision making affects all aspects of engineering practice and has been extensively studied in the literature. However, it has been observed that our decision-making behavior is not properly modeled using classical probability techniques. Consequently, alternative models have been explored with varying degrees of success. One of these approaches is based on using the mathematics of quantum mechanics to model human decisions, which has successfully explained paradoxes such as Ellsberg paradox, question order effect and violation of the law of total probability. In this paper, we explore the use of Open Quantum Systems to model decision making and propose their use in engineering decision making modeling and prediction. It is also proposed that such models can be better parameterized by analyzing associated brain activity using Magnetoencephalography (MEG). We select MEG as the imaging method of choice as it provides multiple advantages over widely used techniques of EEG and fMRI. The paper provides a framework for possible experiments that can be conducted to investigate the dynamics. We provide preliminary data on how this can be accomplished in practice and provide avenues for future research in this area.
Fifty-Five Prompt Questions for Identifying Social Impacts of Engineered Products
Authors: Mattson Christopher A. , Geilman Thomas B. , Cook-Wright Joshua F. , Mabey Christopher S. , Dahlin Eric , Salmon John L. 
Published: November 2023

Abstract
This paper introduces 55 prompt questions that can be used by design teams to consider the social impacts of the engineered products they develop. These 55 questions were developed by a team of engineers and social scientists to help design teams consider the wide range of social impacts that can result from their design decisions. After their development, these 55 questions were tested in a controlled experiment involving 12 design teams. Given a one-hour period of time, 6 control teams were asked to identify many social impacts within each of the 11 social impact categories identified by Rainock et al. [1], while 6 treatment groups were asked to do the same while using the 55 questions as prompts to the ideation session. Considering all 1271 social impacts identified by the teams combined and using 99% confidence intervals, the analysis of the data shows that the 55 questions cause teams to more evenly identify high quality, high variety, high novelty, impacts across all 11 social impact categories during an ideation session, as opposed to focusing too heavily on a subset of impact categories. The questions (treatment) do this without reducing the quantity, quality, or novelty of impacts identified, compared to the control group.
Characterizing Designs via Isometric Embeddings: Applications to Airfoil Inverse Design
Authors: Chen Qiuyi , Fuge Mark D. 
Published: November 2023

Abstract
Many data analysis and design problems involve reasoning about points in high-dimensional space. A common strategy is to embed points from this high-dimensional space into a low-dimensional one. As we will show in this paper, a critical property of good embeddings is that they preserve isometry — i.e., preserving the geodesic distance between points on the original data manifold within their embedded locations in the latent space. However, enforcing isometry is non-trivial for common Neural embedding models, such as autoencoders and generative models. Moreover, while theoretically appealing, it is not clear to what extent enforcing isometry is really necessary for a given design or analysis task. This paper answers these questions by constructing an isometric embedding via an isometric autoencoder, which we employ to analyze an inverse airfoil design problem. Specifically, the paper describes how to train an isometric autoencoder and demonstrates its usefulness compared to non-isometric autoencoders on both simple pedagogical examples and for airfoil embeddings using the UIUC airfoil dataset.
Our ablation study illustrates that enforcing isometry is necessary to accurately discover latent space clusters — a common analysis method researchers typically perform on low-dimensional embeddings. We also show how isometric autoencoders can uncover pathologies in typical gradient-based Shape Optimization solvers through an analysis on the SU2-optimized airfoil dataset, wherein we find an over-reliance of the gradient solver on angle of attack. Overall, this paper motivates the use of isometry constraints in Neural embedding models, particularly in cases where researchers or designer intend to use distance-based analysis measures (such as clustering, k-Nearest Neighbors methods, etc.) to analyze designs within the latent space. While this work focuses on airfoil design as an illustrative example, it applies to any domain where analyzing isometric design or data embeddings would be useful.
Tailored Imaging and Sensing Technologies Enabled by 3D Rapid Micro- to Nano-Fabrication
Authors: Salerni Anthony , Ruiz-Cadalso Daniel , Furlong Cosme 
Published: November 2023

Abstract
Micro- to nano-scale additive manufacturing has enabled the realization of 3D microstructures at sub-micron resolution for complex miniaturized optical and mechanical systems. There is a growing demand for integrable technologies and miniaturization in applications from biomedical diagnostics and environmental sensing to high-precision industrial tasks. The ability to rapidly prototype facilitates the development of custom-tailored components for such specialized applications. This paper presents three case studies highlighting developments utilizing two-photon direct laser writing (TP-DLW). The aim of this work is to provide a comprehensive overview of the current state-of-the-art fabrication tool for the purpose of miniaturizing imaging and sensing technologies.
Preliminary Design of a Deployable Optical Space Array Based on a Thickened Origami Flasher Pattern
Authors: Hunter Jared , Roubicek Clark , Stephen Mark , Magleby Spencer , Howell Larry 
Published: November 2023

Abstract
Performance of space-based optics could be greatly enhanced by using deployable origami-based arrays, which can offer a large aperture size relative to their stowed volume when compared to traditional technology, thus improving imaging quality.
In this work, we select, develop, and adapt the origami flasher pattern to serve as the foundation for a deployable array that shows promise for meeting stringent optical requirements. We apply a novel thickness accommodation technique, outline an approach for implementing the technique, improve stability by adjusting geometric characteristics of the pattern, and create an array of frames for housing optical elements in a co-planar configuration. Problems of non-rigid foldability in the flasher pattern are addressed. A prototype is created and tested. We find that the deployable flasher shows promise as an optical array. By following the guidelines in this work, more efficient and powerful optical arrays can be developed.
Experimental Modal Analysis of Half-Scale Business Jet Aft Fuselage Section
Authors: Sekijoba John M. , Mechefske Chris K. 
Published: November 2023

Abstract
Structure borne noise caused by the engine is a significant source of discomfort for business aircraft passengers. Aircraft with fuselage mounted engines have increased structure borne noise levels in the cabin due to the shortened vibration transmission path. The research objective is to reduce structure vibrations in the aircraft cabin, yielding reduced noise output and increased passenger comfort. Experimental modal testing can identify the dynamic properties of a system (i.e. natural frequencies, mode shapes) which can be used to validate finite element models. This information can be used to guide structural dynamic modifications, reducing vibration levels in the structure. This research involved using modal testing with an electromechanical shaker to excite a half-scale model aft fuselage section at the engine support yolks and measuring the response at the bulkhead. A half scale model was used to reduce the complexity and time cost of experimentation. The results from excitation at each driving point were compared. This paper details the fuselage construction, methodology of excitation, validation techniques, and results. Experimental modes within the engine operating range (50 Hz to 400 Hz) were identified. The RESF-RESF and FESF-FESF measurements yielded an average of 63% more correlated mode pairs than the RESF-FESF measurements. This was expected due to structural symmetry about the longitudinal mid-plane. The results provide a basis for verifying and updating a finite element model in future work.
Generating Large Datasets of Simplified Automotive Body-in-White Structures to Predict Springback Using Machine Learning
Authors: Bolar Abhishek Lokesh , Alawadhi Ibraheem , Ramnath Satchit , Kumar Prakash , Korkolis Yannis , Davidson Joseph K. , Shah Jami J. 
Published: November 2023

Abstract
Automotive structures are primarily made of flexible sheet metal assemblies. Flexible assemblies are prone to manufacturing variations like springback which may be caused due to non-isotropic material properties from cold rolling, springback in the forming process, and distortion from residual stresses when components are clamped, and spot welded. This paper describes the curation of a large data set for machine learning. The domain is that of flexible assembly manufacturing in multi stages: component stamping, configuring components into sub-assemblies, clamping and joining. The dataset is generated by nonlinear FEA. Due to the size of the data set, the simulation workflow has been automated and designed to produce variety and balance of key parameters. Simulation results are available not just as raw FE deformed (sprung back) geometries and residual stresses at different manufacturing stages, but also in the form of variation zones and fits. The NUMISHEET 1993 U-draw/bending was used a reference for tooling geometry and verification of the forming process. Additional variation in the dataset is obtained by using multiple materials and geometrical dimensions. In summary, the proposed simulation method provides a means of generating a design space of flexible multi-part assemblies for applications such as dataset generation, design optimization, and machine learning.
Multi-Fidelity Modeling for Dynamic Power Control and Optimization of Nuclear-Renewable Hybrid Energy Systems
Authors: Chung In-Bum , Wang Pingfeng 
Published: November 2023

Abstract
The attempt to utilize renewable energy that are not as stable as carbon-based power has led to hybrid energy systems (HES), where multiple generation sources are combined to supply the target sector. Nuclear-renewable hybrid energy system (N-R HES) is a promising technology that couples nuclear power plant to the renewable energy sources to send the generated power to the grid. Due to the fluctuations in the generation as well as the demand, an industrial process is typically connected to the system to utilize the produced surplus or byproducts. However, it is important to build a control strategy that can manage the power distribution between the grid and the industrial process. This study focuses on creating a multi-fidelity model to predict the appropriate control state for satisfying each demand on the given timescale of the nuclear renewable HES. A high-fidelity model was constructed using Simulink and a forward calculation was used as a low-fidelity model, where the data generated from the high-fidelity model are a nested set of the low-fidelity model’s input domain. The multi-fidelity surrogate model was trained using co-kriging method and the surrogate was tested on a synthesized example problem to validate its practicality in controlling a dynamic energy system.
A Multi-Objective Bayesian Optimized Human Assessed Multi-Target Generated Spectral Recommender System for Rapid Pareto Discoveries of Material Properties
Authors: Biswas Arpan , Liu Yongtao , Ziatdinov Maxim , Liu Yu-Chen , Jesse Stephen , Yang Jan-Chi , Kalinin Sergei , Vasudevan Rama 
Published: November 2023

Abstract
Optimization for different tasks like material characterization, synthesis, and functional properties for desired applications over multi-dimensional control parameter and function spaces need a rapid strategic search through active learning. However, in all cases prior to optimization, the target material properties are assumed known and fixed, which mostly deviates from real-world scenarios in material synthesis. This can be critical for running expensive experiments on new materials, when the experimental results are fuzzy for any scientific outcomes due to improper target setting, ultimately wasting time and cost. The failure rate and cost are even higher over exploring on multi-target space, where we want to learn the pareto among multiple properties, to jointly optimize during material synthesis for desired applications. To address the challenge, here we introduce the human-operator attempt flexibility in the active learning based automated experiment framework, with generating multiple human assessed targets through a voting-based recommender system during real-time microscope measurements over the large material image space, sequentially learn/update multiple desired targets through a weighting system, and adaptively search in multiple material properties functional space for non-dominated pareto discoveries to maximize the custom structural similarity based acquisition function. We term this a multi-objective Bayesian optimized human assessed multi-target generated spectral recommender systems (MOBO-HAM-SRS). The approach has been demonstrated to peizoresponse force spectroscopy of a ferroelectric thin film, exploring with different kernels and acquisition functions. This work shows an advancement towards human-AI collaborated automated experiments, steering optimization trajectories through human overpowering AI at the early stage when uncertainty is high and AI overpowering human at the later stage with rapid exploration towards optimal goal, following human-assessed multiple targets properties.
Enhancing Design Guidelines for Metal Powder Bed Fusion: Analyzing Geometric Features to Improve Part Quality
Authors: Bushra Jannatul , Budinoff Hannah D. , Luna Falcon Pablo , Latypov Marat 
Published: November 2023

Abstract
Additive manufacturing (AM) part quality relies on many factors, including part geometry that impacts both the manufacturability and resulting dimensional accuracy of the part. To improve the dimensional accuracy of AM parts, data-driven approaches can be utilized to explore the effect of different process parameters on both simple and complex geometries. However, to provide general design guidelines, it is necessary to develop models and tools that accurately predict geometry-driven distortion across a broad range of geometries, while also being user-interpretable. Identifying and analyzing common part features that contribute to geometrical deviations and using them to design better parts could improve AM part quality. In this paper, a Gaussian process regression surrogate model was trained using 21 geometric features (selected from a set of 92 shape descriptors) from 324 different axisymmetric parts to predict maximum part distortion and identify the features that impact part distortion the most. Validated high-fidelity finite element analysis simulations were used to determine the maximum distortion corresponding to each part. Our results show the surrogate model approach can accurately predict part distortion, with a predictive error of approximately 0.07 mm for the testing set. The findings of this study can have implications for the exploration of new part designs by adjusting these identified features or incorporating them as design rules in AM product designs.
Design of a Biologically Inspired Active Visual Communication Strategy for Robotic Applications
Authors: Cochran Bryan , Bras Bert 
Published: November 2023

Abstract
This research considers the development of a biologically inspired active visual communication strategy for use within Multiple Robot Systems. Robotic systems currently communicate using radio frequency (RF) broadcasts, but they cannot transition from one communication modality to another unlike biological organisms. Given the extent in which robotic platforms employ machine vision systems, robotic systems could benefit from implementation of a visual communication modality. Humans commonly employ visual and auditory communication modalities and actively use a variety of different methods of visual communication, but none have been applied to robotic communication. Within biology however, there are numerous active visual communication strategies that may be better suited for robotic applications as compared to current human systems. Biological organisms employ a variety of different communication strategies to allow communication to occur in different environments, and some organisms are capable of transitioning from one communication strategy to another if a situation necessitates it. in a variety of different environments. The work detailed in this article investigates biological visual communication methods to be used for the development of an active robotic visual communication system suitable for use within a Multiple Robot System. This research in this article will highlight an investigation and the subsequent functional decomposition of these communication systems. The decomposition will be used to develop the foundation of the robotic visual communication system in later work.
A Framework to Support Multilevel Robust Co-Design of Manufacturing Supply Networks
Authors: Baby Mathew , Guptan Akshay , Broussard Jacob , Allen Janet K. , Mistree Farrokh , Nellippallil Anand Balu 
Published: November 2023

Abstract
Decision-making in the design of Manufacturing Supply Networks (MSNs) is complex owing to the need to account for decisions made by groups at multiple levels and the interactions that include potential conflicts and uncertainties. Decisions are made based on information available from simulations that employ computational models. These models are abstractions of reality and are therefore sources of uncertainty. This necessitates that the focus be placed on design space exploration to identify a robust satisficing solution set. Hence, the need to support the efficient exploration of multilevel design/solution spaces, simultaneously.
Many of the frameworks that facilitate multilevel design employ optimization-based iterative approaches that are computationally expensive and ill-suited for design exploration. Frameworks that support robust design exploration are limited by their capability to support the efficient exploration of multilevel design/solution spaces, simultaneously. Hence, the requirement to facilitate the ‘multilevel robust co-design and solution space exploration’ of MSNs.
In this paper, we present a framework that allows designers to: i) model robust decision-making and their interactions across multiple levels, and ii) visualize and systematically carry out co-design exploration of multilevel design/solution spaces. In the framework, we combine a Preemptive formulation of coupled Decision Support Problem with robust design constructs and interpretable-Self-Organizing Maps (iSOM) based visualization to facilitate multilevel robust co-design. We use a steel MSN problem with decisions being made at two levels to test the framework. Using the problem, we demonstrate the utility of the framework in supporting designers in modeling multilevel decision-making under uncertainty and their interactions, and efficient co-design exploration of the robust design/solution spaces in MSNs. The proposed framework is based on information and decision flows, making it generic and capable of supporting designers in the robust co-design of multilevel systems.