bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparative Study of Approaches for an Ontology of Digital Artifacts
Authors: Šormaz Dušan , Kulvatunyou Boonserm , Drobnjaković Miloš , Seeharit Saruda 
Published: November 2023

Abstract
Modern business, engineering, and manufacturing are supported by many software tools. Those tools not only help create new products for customers but also enable delivering them in increasingly shorter time periods. To achieve both, the ability to transfer, exchange, integrate, and analyze digital data among supply chain participants with diverse software tools is needed. Creating that ability, however, requires a higher level of interoperability with support from semantics tools, such as knowledge graphs and ontologies. As many engineering and business tasks involve digital communication, it is important to develop ontologies for digital artifacts of various fidelity levels and various scopes of the product life cycle to enable the required interoperability. There are ontologies associated with the various stages of the product life cycle. Those ontologies have various fidelity levels that correspond to specific lifecycle stages and that enable the required interoperability within those stages, with very few enabling interoperability between the stages.
To address those ontology needs, the Industrial Ontology Foundry (IOF) has released the first version of its Core ontology. The Core provides a basis to represent information about some digital objects such as plan specification as well as physical objects such as occurrence of a process (that may or may not be proceeding according to the plan). While that is the case, there is still a need for the IOF Core to provide further guidance on how they should be related and how to scale to various types of digital and physical objects.
To address the issues of relations between physical and digital objects, this paper provides an overview of approaches from recent literature and efforts by the authors and collaborators. It then illustrates in more detail four approaches that are more aligned with the Basic Formal Ontology (BFO), on which the IOF Core is based. General and use case specific requirements are used to arrive at our initial observations. Finally, we discuss a plan for further analysis.
A Collaborative Robotic Screw Assembly System Using 6-PUS Parallel Mechanism
Authors: Sun Qiao , Gao Dalong , Chen Xianbao , Sun Jing , Huang Ninjian , Gao Feng 
Published: November 2023

Abstract
Screw fastening is an important process in the automobile assembly line, and it is difficult to be accomplished completely by automation. Collaborative screw fastening is a practical technique in automotive assembly. In this paper, a novel collaborative robotic system for screw tightening operations based on parallel mechanism is proposed, which is more suitable for high-torque screw tightening tasks while improving operator ergonomics. To this end, a 6-PUS parallel mechanism with a central passive limb is designed. In addition, a control framework for collaborative screw tightening tasks is developed. Its main idea is that the human operator is responsible for the error-prone initial mating stage of screws, while the robot is responsible for the screw-driving stage. The two stages can be performed simultaneously, increasing productivity and reducing the labor intensity. To this end, a multi-mode active compliance controller is proposed according to stages of the screw tightening process and the feedback of a 6-axis force sensor. Furthermore, a statistics based method that helps the robot to guess the next screw position is proposed to improve the fluentness of the collaborative process. A prototype of the proposed robotic system is built. Experiments of the collaborative screw installation process of an engine part are conducted to demonstrate the effectiveness of the proposed system.
Can the Attributes of a Waste Bin Improve Recycling? A Literature Review for Sensors and Actuators to Define Product Design Objectives
Authors: Ríos-Zapata David , Rendón-Vélez Elizabeth , Ruiz-Arenas Santiago , Arbeláez-Estrada Juan Carlos , Vallejo Paola , Tabares Marta Silvia 
Published: November 2023

Abstract
Sustainability efforts have experienced a significant boost in recent years, where the investment in designing systems that help people better separate waste is evident. These efforts are centred on developing smart bins that can increase recycling by impacting the correct separation rate. This article presents a literature review of the attributes of smart bins that have contributed to a higher separation rate. The review yielded three key findings. The first is the characterisation of the commercial sensors used to identify waste and the mechanical components used to segregate and separate waste according to their category. The second is an analysis of the implications of selecting components, sensors and actuators, currently used and their impact and influence on the design of a smart bin. The third is a classification proposal of smart bins in four categories: i) full automation: the system classifies and separates the waste automatically. ii) moderate autonomy: the system is able to re-classify waste, when the user fails to correctly dispose, and segregate it automatically. iii) low autonomy: the system identifies the waste and indicates to the user where to dispose. iv) manual: since technological aids are not available, the bin signage will suggest to the user where to dispose.
Finding Chain Nets of Solids for 3D Printability
Authors: Lawrence Matthew , Tomlinson Scott , Khoda Bashir 
Published: November 2023

Abstract
3D-printing or additive manufacturing (AM) can be a size-constrained and time-consuming manufacturing method compared to those traditionally used. Printing larger structures as a net could be a possible solution to this limitation. The intent behind this work is to establish a methodology for creating 3D structures with discretized planner segments using smaller printer envelopes. Nets of solids are capable of folding into an enclosed body from coplanar shapes. Nets can be utilized to create larger objects not otherwise possible due to space limitations in manufacturing methods. Chain nets are particularly useful for AM, which can avoid collision between the segmented part net and printer topology. To prevent interference with the printer, the shape must be unfolded in a chain net. Unfolding algorithms of this type can be simplified to a Hamiltonian pathfinding problem. This method can be particularly useful when used in conjunction with printing on a flexible substrate, i.e., fabric. Larger solids can be created by printing each side separately and using the fabric as a joint between the faces. When the sides of the solids are printed, the print bed no longer limits the size of solids that can be manufactured. This would also allow collaborative printing with multiple printer platforms to print different faces of the net simultaneously.
Applying Origami-Based Design Approaches to Foldable Furniture for Children
Authors: Parkinson Bethany , Skinner Mitchel , Halverson Denise , Bateman Terri , Magleby Spencer , Howell Larry 
Published: November 2023

Abstract
Origami-inspired design principles — including thickness accommodation techniques, surrogate folds, and a rare use of nonrigid panels — are applied here in a novel foldable furniture item known as the Hidey Cube for use in a daycare facility. Each of the design principles is applied to develop the Hidey Cube and to help it meet its design objectives, including first: the need to provide a safe space for children, and second: the desire for this safe space to be collapsible, transportable, risk-free, and play-enhancing. Through analysis and consideration of a variety of hinges commonly used in origami-based design, and by undergoing an iterative process, two variations on the Hidey Cube design are presented. These two variations — a wood composite version and a fabric-covered foam version — are described and demonstrated. The advantages and disadvantages of each design are enumerated. The fabric-covered foam version utilizes non-rigid panels in conjunction with membrane hinges, which requires an interesting upholstery-based manufacturing approach to allow for the desired actuation while limiting undesired parasitic motion, also presented here. Non-rigid panels are demonstrated to be viable for use in origami-inspired applications, and specific potential applications and recommended future work are discussed. In applications where some parasitic motion is allowable and where non-rigid panels present other functional benefit — such as in furniture, shrouds for use over a medical device, or in wearable technology — the techniques presented here are particularly beneficial.
Identification of a Lumped Element Modeled Ring Shaped Structure via Peak Picking Method
Authors: Seyrek Pelin , Arısoy Doğan Onur , Karapıstık Gökhan , Erözden Ahmet Kutay , Karataş Hamit Çağlar , Ünver Hakki Özgür 
Published: November 2023

Abstract
This paper presents an application of a well-known modal system identification method (i.e., Peak Picking Method) on ring shaped structures with imbalances in terms of natural frequency and damping ratio mismatches, in-silico. To the best of our knowledge, the application of this method to ring shaped structures is novel in the literature.
The system is modeled at secondary vibration pattern (n = 2) via zero dimension lumped element modeling approach. Hence the model has two orthogonal modes (i.e., 2-DOF) with coupling terms of natural frequency and time constant mismatch azimuth angles in-between. Natural frequencies and damping ratios in modal coordinates are estimated using Peak Picking Method based on Real and Imaginary parts of Direct and Cross Frequency Response Functions (FRFs). For this purpose, sine-sweep force signals are applied to the model and displacement values of the both modes are collected. Additionally, eigenvalue decomposition method is utilized to obtain the system characteristic as well as to derive system parameters and coupling terms in generalized coordinates. This identification approach is applied to model with varying quality factors (ranging between 2,500–1,000,000), natural frequencies (ranging between 500–3501 Hz) with numerous natural frequency differences of 0.2 Hz, 1 Hz, and 5 Hz, and different natural frequency azimuth mismatch angle values of π/6 and π/12 while keeping the time constant azimuth mismatch angle value at π/8 for performance evaluations.
Results show that Peak Picking Method can accurately estimate the natural frequencies in all scenarios, on the other hand damping ratio estimations present higher errors in lower quality factors. Also, both azimuth mismatch angle estimations always converge to the highest value given, which is a shortcoming of this method.
Design of a Robotic Testbed for Spherical Additive Manufacturing
Authors: Kim Christopher D. , DeVries Levi D. , Kutzer Michael D. M. 
Published: November 2023

Abstract
The use of additive manufacturing (AM) has become commonplace across a growing number of disciplines. Despite a wide variety of AM methods ranging from polymer extrusion to laser sintering of metals, the use of flat layers or “slices” is common for deposition planning and execution. While effective, the capabilities of AM can be expanded to improve the surface finish, inertial symmetry, and physical properties of fabricated parts with the introduction of conformal or “wrapped” layering. This paper presents the design of a robotic testbed to demonstrate conformal AM using spherical layers. Using this spherical layer constraint, we detail methods for 1) specifying hardware parameters to maximize print volume given an articulated industrial manipulator; and 2) mapping deposition trajectories into joint coordinates using an inverse kinematic solution. Results demonstrate the application of methods to a UR5 industrial robotic manipulator and documentation for the design of a physical testbed.
Selective Amplification and Suppression of Strain in a Multi-Axis Force Sensor Using Topology Optimization
Authors: Sung Myung Kyun , Lee Soobum , Burns Devin E. , Persia Jude Thaddeus 
Published: November 2023

Abstract
This paper proposes an innovative conceptual design of a wind tunnel balance axial section using topology optimization. A wind tunnel balance is a sensor that measures six force/moment components from a wind tunnel model. It is also a structural link between the wind tunnel model and supporting hardware. An axial section, one of the six measurement sections in the balance, is difficult to design because it is often required to resolve an axial force which is much smaller than other force components. Topology optimization is used in this paper to obtain a non-intuitive conceptual design of an axial section. To realize the design requirements, a new top-down symmetric design formulation is suggested to amplify the gauge reading under a small axial loading and to suppress the gauge reading under nonaxial loadings. The formulation assumes the use of a conventional full Wheatstone bridge circuit. The projection method is extensively used to consider manufacturing uncertainties. Then, a postprocessing strategy is used to generate a manufacturable geometry. The postprocessing leverages the Multi-Objective Genetic Algorithm function in ANSYS Workbench to ensure the design requirements of the balance structure are met while maintaining a smooth geometry profile. Satisfactory sensing performance is verified from the postprocessed model using commercial FEM software.
Efficient Airfoil Geometric Uncertainty Quantification Using Neural Network Models and Sequential Sampling
Authors: Koratikere Pavankumar , Leifsson Leifur 
Published: November 2023

Abstract
In this paper, a new and unique surrogate-based forward propagation algorithm for aerodynamic geometric uncertainty quantification (UQ) is proposed. The proposed algorithm extends the recent efficient global optimization with neural network (NN)-based prediction and uncertainty (EGONN) algorithm which was created for unconstrained optimization problems. The proposed extended EGONN algorithm for UQ (uqEGONN) constructs a global surrogate model of the aerodynamics characteristics using a NN based on data sampled from physics-based computational fluid dynamics (CFD) simulations. The NN model is adaptively enhanced using a NN model of the prediction uncertainty. In each sampling cycle, the prediction NN is used to compute the summary statistics with Monte Carlo simulations (MCS). The algorithm terminates when the absolute relative change in the summary statistics reach a specified tolerance, or when a specified maximum number of samples is reached. The algorithm is demonstrated on the UQ of the RAE 2822 airfoil at Mach 0.734, angle of attack 2.89 deg, and Reynolds number of 6.5 million. The geometric uncertainty is represented using twelve normally distributed parameters. The results show that the proposed algorithm yields comparable results as those obtained from direct CFD-based MCS with less than 100 CFD samples. The mean and standard deviation of the airfoil drag coefficient are within 0.45 drag counts, for the lift coefficient it is within 0.04 lift counts, and for the pitching moment coefficient it is within 0.11 (× 10−2).
Machine Learning-Based Model Bias Correction by Fusing CAE Data With Test Data for Vehicle Crashworthiness
Authors: Zeng Jice , Zhao Ying , Li Guosong , Gao Zhenyan , Li Yang , Barbat Saeed , Hu Zhen 
Published: November 2023

Abstract
Physics-based simulation and analysis have emerged as promising techniques for optimizing the number of physical prototypes for vehicle crashworthiness evaluation in frontal impact with rigid barriers. Nonetheless, one of the hurdles for vehicle crashworthiness virtual certification is the potential differences between the computer simulation predictions and physical test results. In this regard, this study aims at improving the prediction capability of the Computer-Aided Engineering (CAE) model for crashworthiness performance evaluation at speeds beyond those defined by current regulations and public domain testing protocols. One way of achieving this is by integrating data from a number of physical crash tests with the CAE data using machine learning models. A novel approach is proposed in the displacement domain (deceleration vs. displacement) to enable data fusion to help recover missing physics associated with the CAE model. A nonlinear spring-mass model is used in this study to simulate rigid-barrier vehicle frontal impact. The deceleration response is transformed from a function of time to a function of displacement, and a Gaussian process regression (GPR) model is applied to capture the model bias of the nonlinear spring constant under a dynamic analysis scheme. The training data for the GPR model are split into multiple clusters by a Gaussian mixture model to capture bias patterns under different speed regimes. After clustering a GPR model is trained for each group of data. The optimal GPR model, trained by a specific cluster exhibiting the highest probability of new data belonging to it, is utilized for prediction. This selected GPR model is integrated with the original CAE model to predict vehicle deceleration under a new crash speed during a vehicle deceleration dynamic analysis. The proposed approach is validated using physical vehicle crash tests and demonstrated improved accuracy of CAE model results and predictions.
AutoSurf: Automated Expert-Guided Meshing With Graph Neural Networks and Conformal Predictions
Authors: Heyrani Nobari Amin , Rey Justin , Kodali Suhas , Jones Matthew , Ahmed Faez 
Published: November 2023

Abstract
Computational Fluid Dynamics (CFD) is widely used in different engineering fields, but accurate simulations are dependent upon proper meshing of the simulation domain. While highly refined meshes may ensure precision, they come with high computational costs. Similarly, adaptive remeshing techniques require multiple simulations and come at a great computational cost. This means that the meshing process is reliant upon expert knowledge and years of experience. Automating mesh generation can save significant time and effort and lead to a faster and more efficient design process. This paper presents a machine learning-based scheme that utilizes Graph Neural Networks (GNN) and expert guidance to automatically generate CFD meshes for aircraft models. In this work, we introduce a new 3D segmentation algorithm that outperforms two state-of-the-art models, Point-Net++ and PointMLP, for surface classification. We also present a novel approach to project predictions from 3D mesh segmentation models to CAD surfaces using the conformal predictions method, which provides marginal statistical guarantees and robust uncertainty quantification and handling. We demonstrate that the addition of conformal predictions effectively enables the model to avoid under-refinement, hence failure, in CFD meshing even for weak and less accurate models. Finally, we demonstrate the efficacy of our approach through a real-world case study that demonstrates that our automatically generated mesh is comparable in quality to expert-generated meshes, and enables the solver to converge and produce accurate results. The code and data for this project is made publicly available at https://github.com/ahnobari/AutoSurf.
Perceived Complexity of 3D Shapes for Spatial Visualization Tasks: Humans vs Generative Models
Authors: McShane Lucy , Lopez Christian 
Published: November 2023

Abstract
The objective of this work is to study the perceived complexity of 3D shapes from a human and a large generative model (i.e., ChatGPT) point of view. This work helps to better understand what makes 3D shapes, which are frequently used in spatial visualization tasks, perceived as complex. It also explores how well ChatGPT can capture the consensus of humans as to what makes shapes perceived as complex. Spatial visualization skills are correlated to success in many STEM fields. To enhance Virtual Reality applications aimed at developing spatial visualization skills, models capable of automatically generating shapes of varying complexities could be used to tailor tasks according to users’ skill levels. However, it is important to first understand how humans perceive the complexity of 3D shapes, and how this relates to their performance in spatial visualization tasks. The results of this work indicate that some visual features of 3D shapes, like symmetry, are correlated to their perceived complexity and the performance of individuals on spatial visualization tasks. More importantly, the results show that ChatGPT can generate shapes that are perceived as having different degrees of complexity by humans. The findings support the capabilities of large generative models, like ChatGPT, to capture aspects of human consensus, even in subjective matters such as the perceived complexity of 3D shapes. Hence, these models could potentially be used to automatically generate content, like for VR applications, which are tailored to an individual.
Selection Method of Molding Condition for Self-Adhesive Products Using Only Bamboo Fibers and Powder Extracted by a Machining Center
Authors: Tanaka Kaito , Kitazaki Reo , Hirogaki Toshiki , Aoyama Eiichi , Nobe Hiromichi 
Published: November 2023

Abstract
In this study, we attempt to create self-adhesive moldings using only bamboo fibers extracted by a machining center to solve global environmental problems and ecological destruction caused by bamboo. This molded body utilizes lignocellulose binder-less adhesion. This adhesion has the advantages of having a low environmental impact and of avoiding competition with food crops because it can be made from a large number of plants. However, the mechanical properties of molded products are still insufficient, and improvement of these properties is key to future utilization. In this study, FTIR will be used to determine the differences in composition depending on the size of the chips used as raw material. The results showed that smaller chips (bamboo powder) had more lignin, an adhesive component, and that their tensile strength increased when mixed with larger chips (bamboo fiber) and molded. Further, we attempt to determine the molding conditions that maximize the tensile strength of the molded product by setting the diffusion parameter to PD’, which represents the degree of lignin flow with reference to a manufacturing process model for fiber-reinforced composites. As a result, we were able to create a powder-mixed compact with tensile strength equivalent to that of commodity plastics.