bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparative Analysis of Smoothed Particle Hydrodynamics (SPH) for Fluid-Structure Interaction: Computational Performance of Lagrangian SPH and Structural Modeling Strategies
Authors: Tagliafierro Bonaventura , Martínez-Estévez Iván , Capasso Salvatore , Domínguez José M. , O’Connor Joseph , Altomare Corrado , Viccione Giacomo , Crespo Alejandro J. C. , Rogers Benedict D. , Serban Radu , Negruţ Dan 
Published: November 2023

Abstract
In this contribution, we discuss the pros and cons of using the Smoothed Particle Hydrodynamics (SPH) method in providing support to multiphysics simulations. The Lagrangian SPH method is proposed to address a fluid-structure interaction (FSI) problem in which the response of a flexible baffle with elastic behavior is solved by using three modeling strategies: I) a dynamic structural solver of 3D bonded rigid bodies with rotations, based on a Lumped-Parameter Method (LPM); II) a non-linear Finite Element Method structural solver (FEM), based on the Euler-Bernoulli beam model; and III) a unified SPH-based for continuum mechanics based on a Total Lagrangian formulation. The present work uses the open-source code DualSPHysics as the fluid solver and different options now available in this code to represent flexible structures. The FSI simulated process starts with a gravity-driven fluid mass that hits a flexible plate experiences wide motion excursions (comparable to its length) in a relatively short time. The fluid phase is consistently solved with a weakly compressible SPH-based solver, whereas the three solution strategies are used to model the flexibility of the solid element. By discussing the the computational performance, and the solvers particle resolution sensitivity, the outcome of this research enables understanding the requirements of each software implementation to optimize the performance scalability.
Cutting and Folding Thick-Panel Miura-Ori With One DoF
Authors: Yang Jingyi , You Zhong 
Published: November 2023

Abstract
Miura-ori or Miura fold is a rigid foldable and flat foldable origami pattern that is tessellated by identical parallelogram facets. The parallelograms can rotate about the creases without twisting or stretching the facets. A Miura-ori demonstrates a bidirectional folding process that can transform a large thin sheet into a compact volume with a single degree of freedom (DoF). However, large gaps along the hinges or uneven surfaces in deployed states are inevitable when thick panels are used. In this paper, the authors add uniform thickness to a Miura-ori tessellation and connect the thick panels by revolute joints on either the top or the bottom facets of the panels for valley creases or mountain creases, respectively. The thick-panel assembly is then a structure with no mobility. Regular slits are made on some facets of the tessellations not only to remove the excess constraints, but also to accommodate the panel thickness during the folding process. Having made the cuts, a thick-panel assembly is demonstrated to fold into a package with one DoF. We further notice that although the slits open up during the folding process, they are closed in the deployed state of the assembly, giving two flat and continuous surfaces. The same method can also be applied to fold the derivatives of Miura-ori such as double-corrugated patterns.
Determination of a Predictive Model for Print Quality of Desktop 3D Printers While at Sea
Authors: Radcliffe Eliott J. , Seker Drew P. , Chen Eric Y. , Horne Nina N. , Hill John L. 
Published: November 2023

Abstract
Shipboard vibration has been identified as having the potential to cause degradation of 3D printer performance. A proposed mitigation for this effect is to actively monitor vibration of the ship deck and critical parts on the printer. A series of tests are conducted wherein a MIL-STD-810H derived shipboard vibration profile is used to excite a printer while it builds basic test specimens. Response accelerations and pass/fail data are collected from these tests to train a logistic regression model that calculates a predicted likelihood of failure. A demonstration test is used to show the capability to monitor the vibration in real-time, and display a indicator to the operator with a “red-light / green-light” criterion. Results of this demonstration are indicative that the vibration monitoring system provides a reasonable predictive ability, and motivates future testing and real-world deployment on a naval vessel.
Should I Sketch or Should I CAD? Exploring Design Representation Strategies in Design for Additive Manufacturing Tasks
Authors: Prabhu Rohan , Cass Madison 
Published: November 2023

Abstract
The emergence of additive manufacturing (AM) processes has resulted in the subsequent development of digital design tools and methods. These design tools and methods are intended to help designers leverage the capabilities of AM through design for AM (DfAM). The rapid advancement of these digital design tools calls for designers to rethink their use of various design representations in the design process. For example, designers can now automate concept generation through tools such as generative design, potentially reducing designers’ reliance on conceptual sketches. However, little research has explored how designers use design representations in the face of constantly evolving DfAM methods. In this research, we aim to explore this gap through an experimental study with fourth-year engineering students. Specifically, participants were given one hour to complete a DfAM task, and their use of the various design representations was assessed using eye-tracking. Their design representation strategies (e.g., transitions between different representations) were qualitatively compared between designers that generated solutions of high vs low creativity and high vs low performance. From our results, we see that designers who generate solutions of high creativity tend to spend a significant amount of time generating CAD models of their solutions, especially in the later stage of the design task. We also see that designers who generate high-performing solutions (i.e., low build time and material) tend to spend more time sketching their solutions. These findings suggest that designers must employ different design representation strategies depending on the desired outcomes (i.e., high creativity vs high performance). These results provide an essential first step toward creating a generalized framework for designers to employ different design representations when designing for AM.
Design for Implementation: A Medical Device Development Design Process
Authors: Bravo Elisa , Austin-Breneman Jesse 
Published: November 2023

Abstract
Innovative design solutions from the medical device industry contribute significantly to the continuous improvement in healthcare [1,2]. However, benefits from the advances made in medical devices are not always realized, as the delivery of care to patients is not always consistent with recommended care [3, 4]. Recent studies highlight the variability in adherence of evidence-based practices by healthcare providers [3, 5]. Relevant research in implementation science asserts that an intervention or treatment will not be effective without being well implemented [6]. Medical Device Design (MDD) processes give engineering teams strategies to reliably deliver higher quality healthcare by increasing the technical performance, safety, usability, and effectiveness of the device. This work presents a new method incorporating concepts from Implementation science into an MDD process in order to improve overall health outcomes. Implementation outcomes, like clinician adherence, and the device performance are related to the health outcomes of the patient population of interest. The alternate approach uses the simultaneous design of the medical device and implementation strategy to inform design decisions by optimizing a health outcome model. Results from an illustrative case study on Acute respiratory distress syndrome (ARDS) patients was used to showcase the utility of the developed approach on medical device design.
Tooth Bending Strength in the VHCF Area
Authors: Zalfen Moritz , Mevissen Dieter , Brimmers Jens , Brecher Christian 
Published: November 2023

Abstract
The use of gears in electrified drivetrains and aircraft turbines leads to higher operating speeds and a higher number of load cycles at the pinion. Load cycles of N &gt; 107 in the Very High Cycle Fatigue (VHCF) area cause a decrease in the endurance strength of case-hardened steels. Furthermore, increasing speeds increase the strain rate in the tooth root, which also has an influence on the fatigue strength known from standard tests. The determination of the strain rate effect requires the evaluation of the test results regarding the statistical significance.
The aim of this report is to analyze the different influencing factors on the tooth bending strength, e.g. contour of the tooth root, gear quality, roughness etc. Test gears are measured and the results are traced back to a FE-based tooth contact analysis for the calculation of the tooth root stress. Furthermore, the uncertainties coming for the testing procedure (e.g. stair case or horizon method) are examined and the most appropriate procedure for the planned tests is selected. In the last step the tooth bending strength in the VHCF area is tested on a 3-shaft back-to-back test rig. The test results are evaluated regarding the failure mechanisms and the statistical behavior. Finally, the minimum number of tests regarding the statistical significance of the results is assessed.
On the Use of Geometric Deep Learning Towards the Evaluation of Graph-Centric Engineering Systems
Authors: Sirico Anthony , Herber Daniel R. 
Published: November 2023

Abstract
Many complex engineering systems can be represented in a topological form, such as graphs. This paper utilizes a machine learning technique called Geometric Deep Learning (GDL) to aid designers with challenging, graph-centric design problems. The strategy presented here is to take the graph data and apply GDL to seek the best realizable performing solution effectively and efficiently with lower computational costs. This case study used here is the synthesis of analog electrical circuits that attempt to match a specific frequency response within a particular frequency range. Previous studies utilized an enumeration technique to generate 43,249 unique undirected graphs presenting valid potential circuits. Unfortunately, determining the sizing and performance of many circuits can be too expensive. To reduce computational costs with a quantified trade-off in accuracy, the fraction of the circuit graphs and their performance are used as input data to a classification-focused GDL model. Then, the GDL model can be used to predict the remainder cheaply, thus, aiding decision-makers in the search for the best graph solutions. The results discussed in this paper show that additional graph-based features are useful, favorable total set classification accuracy of 80% in using only 10% of the graphs, and iteratively-built GDL models can further subdivide the graphs into targeted groups with medians significantly closer to the best and containing 88.2 of the top 100 best-performing graphs on average.
Fairness- and Uncertainty-Aware Data Generation for Data-Driven Design
Authors: Xie Jiarui , Zhang Chonghui , Sun Lijun , Zhao Yaoyao Fiona 
Published: November 2023

Abstract
The design dataset is the backbone of data-driven design. Ideally, the dataset should be fairly distributed in both shape and property spaces to efficiently explore the underlying relationship. However, the classical experimental design focuses on shape diversity and thus yields biased exploration in the property space. Recently developed methods either conduct subset selection from a large dataset or employ assumptions with severe limitations. In this paper, fairness- and uncertainty-aware data generation (FairGen) is proposed to actively detect and generate missing properties starting from a small dataset. At each iteration, its coverage module computes the data coverage to guide the selection of the target properties. The uncertainty module ensures that the generative model can make certain and thus accurate shape predictions. Integrating the two modules, Bayesian optimization determines the target properties, which are thereafter fed into the generative model to predict the associated shapes. The new designs, whose properties are analyzed by simulation, are added to the design dataset. An S-slot design dataset case study was implemented to demonstrate the efficiency of FairGen in auxetic structural design. Compared with grid and randomized sampling, FairGen increased the coverage score at twice the speed and significantly expanded the sampled region in the property space. As a result, the generative models trained with FairGen-generated datasets showed consistent and significant reductions in mean absolute errors.
The Kinematic Synthesis of a Spherical Mechanism for Assisting in Wrist Pronation and Supination
Authors: Trotobas Clément , Azevedo Christine , Murray Andrew P. 
Published: November 2023

Abstract
Full use of the upper limb is necessary to carry out most tasks of daily life. Upper limb deficiencies, whether through complete or incomplete paralysis, inevitably lead to a loss of autonomy. Assistive orthoses are a potential method for restoring some autonomy. Pronation and supination, the turning of the wrist relative to the elbow, receives less focus than other joint movements in the arm. First, the utility of this degree-of-freedom in the arm is less obvious. Second, when compared to flexion and extension of the elbow, wrist prono-supination has no clear center of rotation due to the combined movement of the ulna and the radius bones as they cross and uncross in the forearm. This paper presents initial work in the design of a mechanism for a portable assistive orthosis that is expected to include powered prono-supination. The component proposed in this work is based on a spherical mechanism architecture. The capacity of these mechanisms to have a hollow center and to produce paths that follow arcs on spheres make them worth consideration in this application. A MATLAB optimization was used to perform path generation of a single spherical four-bar with the intent of replicating it three times to create the device proposed in this work. The concept was modeled in SOLIDWORKS and printed to gauge its potential.
Approximate Motion Synthesis of Four-Bar Linkages Using Poles: A Bi-Invariant Approach
Authors: Xu Tianze , Myszka David H. , Murray Andrew P. 
Published: November 2023

Abstract
This paper presents a technique for the approximate motion synthesis of planar four-bar mechanisms based exclusively on the locations of displacement poles. Approximation motion synthesis is the same as motion generation or rigid-body guidance except that the mechanism cannot be synthesized to meet all of the kinematic constraints and a best-fit solution must be determined. One challenge in approximate motion guidance methods is that metrics are introduced to identify the best-fit displacement and orientation angle. Thus, the optimal solution provides a match between the task positions and positions generated by the resulting linkage that is conditional on the function used to combine the displacement and orientation. Such solutions are said to lack bi-invariance as a change in reference for the problem changes the values of the metric and, hence, the mechanism.
This work focuses on the locations of the displacement poles, which are sufficient to characterize the relative task positions. Further, the displacement poles are described solely by their locations. The optimization minimizes the distance between the poles of the task positions and the poles of the positions generated by a synthesized linkage. Further, a technique is presented that does not use the entire set of displacement poles, thereby reducing the size of the optimization. The advantage of the method is that the exclusive use of pole locations results in a bi-invariant statement of the problem. The technique is illustrated with several examples and compared to other established methods.
Design and System Identification of an Actuation-Coordinated Mobile Parallel Robot with Hybrid Mobile and Manipulation Motion
Authors: Lin Han , Luo Jiayu , Huang Xiaotong , Yang Haoguang , Fu Jiaming , Voyles Richard M. , Gan Dongming 
Published: November 2023

Abstract
This paper presents the development of a novel Actuation-Coordinated Mobile Parallel Robot (ACMPR), with a focus on studying the kinematics of the mobile parallel robot with three limbs (3-mPRS) comprising mobile prismatic joint-revolute joint-spherical joint. The objective of this research is to explore the feasibility and potential of utilizing omnidirectional mobile robots to construct a parallel mechanism with a mobile platform. To this end, a prototype of the 3-mPRS is built, and several experiments are conducted to identify the proposed kinematic parameters. The system identification of the 3-mPRS mobile parallel mechanism is conducted by analyzing the actuation inputs from the three mobile base robots. To track the motion of the robot, external devices such as the Vicon Camera are employed, and the data is fed through ROS. The collected data is processed based on the geometric properties, CAD design, and established kinematic equations in MATLAB, and the results are analyzed to evaluate the accuracy and effectiveness of the proposed calibration methods. The experiment results fall within the error range of the proposed calibration methods, indicating the successful identification of the system parameters. The differences between the measured values and the calculated values are further utilized to calibrate the 3-mPRS to better suit the experiment environment.
Layer-to-Layer Thermal History Prediction for Thin Walls in Metal Additive Manufacturing
Authors: Tang Yifan , Bakrani Balani Shahriar , Dhalpe Akshay , Rahmani Dehaghani Mostafa , Panicker Suraj , Coatanea Eric , Wu Di , Wang G. Gary 
Published: November 2023

Abstract
Thermal history has a great effect on the part properties in metal additive manufacturing such as tensile strength and hardness. To study and control the thermal behavior of the AM processes, various data-driven thermal history modeling methods have been developed and tested on simulation data. However, their in-situ application scenarios are rarely explored and discussed. This paper aims to provide a layer-to-layer thermal history prediction model, which enables predicting the thermal history of a yet-to-print layer based on the data measured from the printed lower layers. First, the thermal behavior is analyzed to reveal the similarities in temperature curves of two successive layers. Then four input variables are identified, including the deposition rate, the relative height of the layer, the printing time, and the dwell time of one layer. Based on the selected input variables and the temperature output, a fully connected neural network with residual connection is designed to simplify the training process. Five numerical simulations are designed to collect temperature curves (curve segments of a temperature profile) on each layer, and one experimental study on wire arc additive manufacturing is completed to record the temperature curves. Based on the collected data, three cases are proposed to test the modeling framework, such as (a) dividing all simulation data into the training set and validation set, (b) training the model based on four simulation runs and its validation with the last simulation, and (c) training the model with all simulation data and testing on the experimental data. The former two cases show great prediction accuracies with a relative error of less than 5% in most cases, which indicates its potential in online prediction when trained with data from the same input conditions or same systems. While the applicability of the model trained only with simulation data should be explored further in real experiments.
Heuristics for Solver-Aware Systems Architecting (SASA): A Reinforcement Learning Approach
Authors: Gadi Vikranth S. , Topcu Taylan G. , Szajnfarber Zoe , Panchal Jitesh H. 
Published: November 2023

Abstract
The crowdsourcing literature has shown that domain experts are not always the best solvers for complex system design problems. Novices and specialists in adjacent domains can, under certain conditions, provide novel solutions at lower costs. Additionally, the best types of solvers for different sub-problems are dependent on the architecture of complex systems. The assignment of solvers based on the architecture, referred to as Solver-Aware System Architecting (SASA), expands traditional system architecting practices by considering solver characteristics and contractual incentive mechanisms in the design process and aims to improve complex system design and innovation by leveraging the strengths of domain experts, crowds, and specialists for different parts of the problem. Given the complexity of system design problems and the variety of solvers available, it is desirable to have heuristics to guide solver assignment to different problems. Developing effective heuristics for solver assignments in complex system design is challenging due to a large number of possible combinations of problem-solver pairs. To address this challenge, this paper presents a computational approach using a multi-armed bandit (MAB) formulation to generate heuristics for solver assignment. The approach is demonstrated using a simple and idealized problem of golf, which has characteristics similar to design problems, including how the problem is decomposed into sub-problems, and solved by different solvers. The results show that the proposed approach is effective in deriving a rich set of heuristics for the golf problem, and can be extended in the future to more complex systems design problems.
Multi-Agent Bayesian Optimization for Unknown Design Space Exploration
Authors: Chen Siyu , Bayrak Alparslan Emrah , Sha Zhenghui 
Published: November 2023

Abstract
This paper proposes a multi-agent Bayesian optimization (MABO) framework as a reference model for rational design teams to study the effects of information exchange on a team’s search performance in finding global optimum of complex objective functions with many local optima. The core idea of the framework has three main steps. First, the design space is divided into regions based on the number of agents involved in the search. In each region, only one agent works on the part of the objective function. Second, a global-local communication strategy is developed to allow agents in local searches to share their sampled design points with a global evaluator. The global evaluator computes the posterior mean and variance based on all sampled points from local agents and evaluates the acquisition function (e.g., the expected improvement) to recommend the next sampling decisions for local agents. Third, when making the decision about where to sample next, each local agent only has access to the expected improvement evaluated in its local region and chooses the design that yields the largest value locally. To evaluate how the information exchange between agents and between local and global impact the search results, our framework is compared with a multi-agent model that does not allow information sharing and global-local interaction. Furthermore, we evaluated the performance of the model based on benchmark functions with varying complexities and also investigated the impact of the number of agents on search performance. We observe that when information sharing is allowed and global-local interaction is enabled in all scenarios, there is a significant improvement in convergence speed as well as the success rate of convergence.