bluuebunny/crossref_metadata_2025_split huggingface dataset:
An Approach for Predicting Social, Environmental, and Economic Product Impacts and Navigating the Associated Impact Trade-Space in Engineering Design
Authors: Mabey Christopher S. , Dickerson Tevin J. , Salmon John L. , Mattson Christopher A. 
Published: November 2023

Abstract
There is a growing demand for sustainable products and systems. Sustainability encompasses environmental, social, and economic aspects, often referred to as the three pillars of sustainability. To make more sustainable design decisions, engineers need tools to predict the environmental, social, and economic impacts of products and navigate potential impact trade-offs. To capture societal adoption of technology, this article uses agent-based modeling (ABM) to predict impacts across all three pillars of sustainability and how to navigate the multidimensional impact trade space. The approach described in this article is based on three main components for the predictive modeling of product impacts and impact trade-space navigation, i) ABM of product adoption, ii) the assessment of product impacts, and iii) an approach for design decisions in the presence of impact trade-offs. The trade-space navigation uses a visual approach to find the non-dominated solutions in the product impact space. To illustrate and describe how to use the method, a case study is presented that predicts the impact of residential solar panels in a region of the United States under various scenarios. The findings of the case study can help policymakers understand suitable implementation strategies for residential solar panels while considering the impact trade-offs involved.
Predicting the Behavior of Large-Scale, Additively Manufacturable Lattice Structures via the Laws of Similitude
Authors: Rokade Sumant Dilip , Gutierrez Soto Mariantonieta , Meisel Nicholas 
Published: November 2023

Abstract
Additive Manufacturing (AM) use in construction has been on the rise in recent years. This means faster build times, less waste, and increased efficiency. Conventional structural building blocks are not fully leveraging the capabilities offered by AM. For this reason, the authors propose using AM-enabled self-supporting structures as a part of the construction process. Using these unconventional structural members requires testing; however, full-scale testing is not always economically feasible. From an experimental testing standpoint, it would be easier to test these in a controlled environment at a small scale; as the behavior of lattice structures in structural loading is yet unknown. To understand the behavior of these structures, it is imperative to understand whether the existing scaling laws can be applied to these non-homogeneous, discontinuous lattice structures. This paper explores the requirements for performing scaled-down testing on latticed beams with dissimilar materials in a virtual environment. The performance rating of the unit cells tested in order is Diamond, Gyroid, and Fluorite.
Acoustic Emission Damage Parameter for Cumulative Fatigue Damage Prediction in Gear Single Tooth Bending Fatigue
Authors: Anichowski Gabriel , Kahraman Ahmet , Hong Isaac 
Published: November 2023

Abstract
Power delivery in many industrial, automotive, and aerospace applications is subject to duty cycles based on the needs of the equipment over each mission. These blocks of usage at various power levels subjects the drivetrain to cyclic forces and stresses of variable mean and amplitude. Each block of applied cyclic stresses contributes some amount to the overall fatigue damage in the parts. It is critical that the design of the power transmission components such as gears accounts for the overall fatigue damage applied from the expected duty cycle loading. Contemporary cumulative fatigue damage models require experimental data of various proportions to regress required empirical parameters. At minimum, a constant amplitude stress life (SN) evaluation is necessary. Others require expansive multilevel load experimental programs to regress empirical material parameters. It is advantageous for all design engineers from a time and cost perspective to minimize the amount of fatigue testing required to regress empirical cumulative damage parameters. It is proposed here that measurement of Acoustic Emission (AE) in constant amplitude fatigue tests may serve as a suitable metric on which to base the regression of empirical cumulative damage parameters. AE hits, ringdown counts, and absolute energy is monitored during gear single tooth bending fatigue (STBF) tests. Material parameters as defined in the Manson and Halford Damage Curve Approach Model are then regressed using the AE metrics. A prediction of a set of multilevel STBF tests is made and compared to existing experimental results and predictions from other cumulative damage fatigue models.
Analysis of Mechanical Chemical and Thermal Properties of PLA Filaments After Mechanical Recycling
Authors: Shabani Mahsa , Turner Cameron J. 
Published: November 2023

Abstract
Polylactic Acid (PLA) is a popular biopolymer used in additive manufacturing. In additive manufacturing, there are rafts, berms, and unfortunately, failed parts and support structures that will be discarded and therefore may end up in a landfill. While PLA is biodegradable, this is true only in certain circumstances. The degradation of PLA is naturally slow in the landfill, and it would need more methods (such as chemical hydrolysis) than the natural process to increase the degradability rate. In order to reduce the environmental impact of additive manufacturing, recycling of PLA is therefore desirable. In this paper, PLA was mechanically recycled into new filaments. Mechanical recycling is attractive as it can be readily performed even by individual small scale users. Suggested material preparation techniques found in the relevant literature were tested and a new material preparation process is presented that seems to resolve many issues occurring from using other techniques. Recycling was tested by three different extruders. The extruders were compared to find the most effective choice. Finally, virgin PLA, virgin PLA after additive manufacturing, recycled PLA, and recycled PLA after an additive manufacturing and recycling process were mechanically, thermally, and chemically tested to understand the changes experienced by the material during recycling process. The results demonstrate that the samples do not experience significant changes during recycling, and therefore, the recycled material has material properties comparable to virgin PLA without any impurities or significant property changes induced by the printing and recycling process.
State of the Art: A Review of AI Art Generation Methods for Rigorous Design
Authors: Bertelsen Lauren , Short Ada-Rhodes 
Published: November 2023

Abstract
Over the past several years, humans have developed various new AI art tools for artistic and conceptual design purposes — this paper aims to review the development of AI’s abilities to generate original, rigorous designs for technical applications. First, this paper examines three modern AI art methods: Generative Adversarial Networks, Convolutional Neural Networks, and stable diffusion. Then, we review the main concepts of each method and test a representative AI on its abilities for rigorous design. Various prompts are used, including simple, formulaic, AI-generated, and human-generated prompts from an architectural engineering class assignment.
On Selecting and Reconstructing Lyapunov Functions for Stability Analysis of Nonlinear Dynamical Systems
Authors: Wei Bin 
Published: November 2023

Abstract
Lyapunov theorems did not tell us how to choose the Lyapunov function to analyze the stability of equilibrium states for nonlinear systems, and currently there is no particular way or formula that can guide us on how to choose the right Lyapunov function when using Lyapunov theorems and LaSalle theorems. Typically, a Lyapunov candidate function is chosen based on the trial and error approach. In this paper, the author presents an approach that is able to guide us to choose the right Lyapunov function based on the initial random Lyapunov candidate function when using LaSalle theorems to study the equilibrium states’ stability for the nonlinear systems. Difference case studies are given in the paper in order to show the effectiveness of the approach.
Using Velocity Partitioning in the rA Formulation to Solve the Equations of Constrained Multibody Dynamics
Authors: Kissel Alexandra , Fang Luning , Negrut Dan 
Published: November 2023

Abstract
In mechanical systems, formulating the equations of motion in absolute coordinates results in a set of index-3 differential algebraic equations (DAEs). In this work, we present an approach to solving the DAE problem by partitioning the velocities in the system into dependent and independent coordinates, thereby reducing the problem to an ordinary differential equations (ODEs) problem. The independent coordinates are integrated directly, while the dependent coordinates are recovered through the kinematic constraint equations at the position and velocity levels. Notably, Lie group integration is employed to directly obtain the orientation matrix A at each time step of the simulation. The approach builds on a method presented in [1], which combines coordinate partitioning with an Euler parameter formulation. Herein, we outline the numerical method and demonstrate it in conjunction with three mechanisms: a single pendulum, a double pendulum, and a four-link mechanism. We report on the convergence order behavior of the proposed method. The open-source Python software developed to generate the reported results is available in a public repository for reproducibility studies [2].
Ship-D: Ship Hull Dataset for Design Optimization Using Machine Learning
Authors: Bagazinski Noah J. , Ahmed Faez 
Published: November 2023

Abstract
Machine learning has recently made significant strides in reducing design cycle time for complex products. Ship design, which currently involves years-long cycles and small batch production, could greatly benefit from these advancements. By developing a machine learning tool for ship design that learns from the design of many different types of ships, trade-offs in ship design could be identified and optimized. However, the lack of publicly available ship design datasets currently limits the potential for leveraging machine learning in generalized ship design. To address this gap, this paper presents a large dataset of 30,000 ship hulls, each with design and functional performance information, including parameterization, mesh, point-cloud, and image representations, as well as 32 hydrodynamic drag measures under different operating conditions. The dataset is structured to allow human input and is also designed for computational methods. Additionally, the paper introduces a set of 12 ship hulls from publicly available CAD repositories to showcase the proposed parameterization’s ability to accurately reconstruct existing hulls. A surrogate model was developed to predict the 32 wave drag coefficients, which was then implemented in a genetic algorithm case study to reduce the total drag of a hull by 60% while maintaining the shape of the hull’s cross section and the length of the parallel midbody. Our work provides a comprehensive dataset and application examples for other researchers to use in advancing data-driven ship design.
Optimization of Masking Parameters for Volumetric Material Manufacturing Using Inverse Methods
Authors: Graber Benjamin D. , Iliopoulos Athanasos P. , Michopoulos John G. , Steuben John C. , Birnbaum Andrew J. , Apetre Nicole A. , Gorzkowski Edward P. , Patterson Eric A. , Geltmacher Andrew B. 
Published: November 2023

Abstract
Volumetric material manufacturing involves forming a solid shape within a precursor powder volume as a whole instead of the traditional layer by layer technique used for most additive manufacturing methodologies. This work presents an inverse technique for designing masking parameters in order to generate a predetermined shape within the precursor powder volume. The model utilizes a microwave cavity for inducing heat in BaTiO3 precursor powder via Joule heating. Material sintering and microwave propagation is modeled by coupling electromagnetics and heat conduction using finite element discretization. This interaction is complicated by the nonlinear thermal dependence of dielectric properties of BaTiO3 (i.e. density, specific heat, dielectric constant and loss tangent), which are considered here. Heat can be localized within the powder volume using one or multiple conductive masks that modulate incident energy. Utilizing both 3D and 2D models, it is demonstrated that an arbitrary nonuniform mask can create nonuniform heating in the powder volume. An experimental framework is then established to conduct a forward experiment parameterized with five masking and cavity design variables causing different powder heating patterns. Then an inverse problem is solved by specifying a desired shape and outputting a mask and cavity design capable of generating said shape. It is demonstrated that mask parameters can be optimized in order to produce a desired shape by this inverse method.
Multi-Input Multi-Output Vibration Model for Motorbike Excited by Ride Test
Authors: Cavacece Massimo , Figliolini Giorgio , Lanni Chiara 
Published: November 2023

Abstract
This research proposes a multi-input multi-output model (MIMO) to identify the modal properties of a motorbike during the riding test. The MIMO process verifies that a linear combination of all inputs causes each output, and there are no causal relationships between the inputs. If there are causal relationships between one of the outputs, the MIMO process redefines the input signals. The process verifies the connection between input-output and the presence of noise extraneous to the inputs and outputs. The MIMO process represents a multiple-input system with a single output. The peculiar aspect is the decoupling of individual information. In the presence of coupling between the separate inputs, the authors propose the principal component analysis (PCA) to decouple the inputs.
Drag Coefficient Characterization of the Origami Magic Ball
Authors: Chen Guanyu , Chen Dongsheng , Weakly Jessica , Sung Cynthia 
Published: November 2023

Abstract
The drag coefficient plays a vital role in the design and optimization of robots that move through fluids. From aircraft to underwater vehicles, their geometries are specially engineered so that the drag coefficients are as low as possible to achieve energy-efficient performances. Origami magic balls are 3-dimensional reconfigurable geometries composed of repeated simple water-bomb units. Their volumes can change as their geometries vary and we have used this concept in a recent underwater robot design. This paper characterizes the drag coefficient of an origami magic ball in a wind tunnel. Through dimensional analysis, the scenario where the robot swims underwater is equivalently transferred to the situation when it is in the wind tunnel. With experiments, we have collected and analyzed the drag force data. It is concluded that the drag coefficient of the magic ball increases from around 0.64 to 1.26 as it transforms from a slim ellipsoidal shape to an oblate spherical shape. Additionally, three different magic balls produce increases in the drag coefficient of between 57% and 86% on average compared to the smooth geometries of the same size and aspect ratio. The results will be useful in future designs of robots using waterbomb origami in fluidic environments.
Data Fusion Cognitive Computing for Characterization of Mechanical Property in Friction Stir Welding Process
Authors: Hoang Danny , Chen Ruimin , Mishra Debasish , K. Pal Surjya , Imani Farhad 
Published: November 2023

Abstract
High intricacy of the friction stir welding (FSW) process and severe plastic deformation typically result in serious part quality concerns, including thinning, groove, and kissing bond. These defects, in turn, impact mechanical properties (e.g., Young’s modulus and yield strength), thereby deteriorating the performance of a joint part. Machine learning methods such as feature-based support vector machines and end-to-end deep neural networks are recently coupled with in-situ sensing to link process parameters with the final properties, yet the low accuracy and precision, poor sample efficiency, and lack of interpretable learning limit their capabilities for reliable characterization. This research introduces hyperdimensional cognitive computing (HCC) that mimics human brain functionalities to fuse power, torque, and force data to provide robust, sample-efficient, and explainable learning for process-property characterization in FSW. Data augmentation is integrated with HCC to tackle the imbalanced data problem. The experimental results show that HCC seamlessly fuses in-situ data to predict the ultimate tensile strength with an Fl-score of 0.899 ± 0.048, which is 21.579%, 47.779%, 1.933%, and 68.139% better than support vector machine, logistic regression, Naive-Bayes and multilayer perceptron, respectively. Besides the superior prediction capability, HCC symbolic representation reveals that measured force is the critical factor in the characterization of ultimate tensile strength. The proposed HCC is shown to be effective for data fusion and single-pass learning and eliminates the necessity of costly and long retraining in various manufacturing processes such as 3D printing and bio-fabrication.
On the Construction of Confidence Regions for Uncertain Planar Displacements
Authors: Yu Zihan , Ge Qiaode Jeffrey , Langer Mark P. , Arbab Mona 
Published: November 2023

Abstract
This paper studies the statistical concept of confidence region for a set of uncertain planar displacements with a certain level of confidence or probabilities. Three different representations of planar displacements are compared in this context and it is shown that the most commonly used representation based on the coordinates of the moving frame is the least effective. The other two methods, namely the exponential coordinates and planar quaternions, are equally effective in capturing the group structure of SE(2). However, the former relies on the exponential map to parameterize an element of SE(2), while the latter uses a quadratic map, which is often more advantageous computationally. This paper focus on the use of planar quaternions to develop a method for computing the confidence region for a given set of uncertain planar displacements. Principal component analysis (PCA) is another tool used in our study to capture the dominant direction of movements. To demonstrate the effectiveness of our approach, we compare it to an existing method called rotational and translational confidence limit (RTCL). Our examples show that the planar quaternion formulation leads to a swept volume that is more compact and more effective than the RTCL method, especially in cases when off-axis rotation is present.
Functional Reasoning of System Architecture in the System Modeling Language (SysML) With XML Representation
Authors: Ganeriwala Parth , Chambers Candice , Sen Chiradeep , Bhattacharyya Siddhartha 
Published: November 2023

Abstract
Formal and structured modeling of system or software behavior enables automated reasoning early in the design process. This permits checking the correctness of the flow of information, consistency among the interacting components, and dependencies among components, to mention a few potential automated reasoning. Presently, research efforts have emphasized the integration of formal analytical capabilities with systems modeling methods. But, the approaches lack formal representations for function-based modeling and reasoning. Prior research in function-based design has produced formal languages, graphical representations, and reasoning algorithms that support model consistency checks, physics-based verification of model correctness, natural-language and qualitative physics-based grammars that grow and decompose given function models, and formal representation of system states that support causal analysis within the function modeling context. We posit that by integrating these capabilities into the system modeling languages, we could significantly augment the languages’ capability to support formal function-based reasoning, which was not possible so far. In this paper, we address this gap with the creation of domain-specific language for function-based modeling. This is illustrated with a coffeemaker example designed in a systems engineering framework along with XML-based reasoning.
Data-Driven Design of High Electron Mobility Transistor Devices Using Physics-Informed Gaussian Process Modeling
Authors: Renteria Anabel , Xu Yanwen , Hamdan Bayan , Li Zhou , Cordero Sergio , Senesky Debbie G. , Wang Pingfeng 
Published: November 2023

Abstract
High electron-mobility transistors (HEMTs) have emerged as an attractive alternative for high-efficiency power systems, due to their good material properties to perform at high voltages, temperatures, and frequencies. For that reason, design optimization of HEMTs becomes imperative to ensure the quality and capability of the device when in service. There have been models derived from experimentation to guide the design of HEMTs. Nonetheless, due to its expensive manufacturing process, the relationship of the temperature channel with respect to the design parameters has not been investigated thoroughly. This paper presents a multiphysics finite element (FE) simulation to predict the HEMT’s device maximum channel temperature when varying different design parameters. Furthermore, Gaussian Process (GP) based surrogate model was developed using the simulation results as the training database with adaptive sampling techniques for the optimization process. The proposed high-fidelity surrogate model effectively predicts the channel temperature of the HEMT device and enables an optimum search over the design space.
Design of a Person-Carrying Robot for Contact Compliant Navigation
Authors: Gonzalez Carlos , Lee Samantha , Montano Francisco , Ortega Steven , Kang Dong Ho , Jaiswal Mehar , Jiao Junfeng , Sentis Luis 
Published: November 2023

Abstract
Navigating human-centered environments is a complex task for robots. It requires having hardware capable of sensing the environment as humans do and requires software that efficiently reasons about this information in real time. Current robotic platforms tend to avoid contacts, while human robot interaction highly depends on this. We believe having platforms and algorithms that combine visual and tactile information is crucial in developing navigation algorithms for human-centered environments. In this work, we present the mechatronic design of our new mobile robot which is capable of carrying a person and that can sense its environment through contact and vision. We detail our design procedure, parting from design specifications to material selection and show its preliminary appearance along with some experimental navigation while carrying a person. We provide detailed analyses on our mechanical design through Finite Element Analysis simulations, present our material seleection for all of the hardware components, and give details on our low-level code controlling our robot. We have set up our robot to work with the ROS framework and have shared our code, which includes the low-level code to communicate with the EtherCAT servo drives. We believe this information will ease development of new robots by the robotics research community.