bluuebunny/crossref_metadata_2025_split huggingface dataset:
Extending Usage Context-Based Design to Coupled Usage Contexts: A Vehicle Design Case Study
Authors: Arezoomand Mojtaba , Austin-Breneman Jesse 
Published: November 2021

Abstract
Engineers must consider the usage context of a product in order to both predict its technical performance and model customer preferences. An emerging body of work in Decision-based Design (DBD) has elaborated various approaches for modeling the usage context in order to better predict customer choice behavior and select optimal product attributes. Building on this prior work, this study proposes a new method for formulating DBD problems in which product attribute values can change contextual factors. Results from a vehicle design case study demonstrate the utility of the proposed method for understanding how phenomena such as the rebound effect and induced travel demand connect system-level outcomes to design changes. This study suggests that the Design for Coupled Usage Contexts framework is a promising tool to further explore as a way to support designers making decisions which involve these types of mechanisms. Further exploration should include additional case studies to investigate other coupling mechanisms and design tasks.
AgBug: Agricultural Robotic Platform for In-Row and Under Canopy Crop Monitoring and Assessment
Authors: Manish Raja , An Ze , Habib Ayman , Tuinstra Mitchell R. , Cappelleri David J. 
Published: November 2021

Abstract
This paper focuses on the development of a small scale agricultural robotic platform with advantages over the current agricultural phenotyping platforms that lack the size-scale and sensor resolution needed to study hard to reach under-canopy row crops. The AgBug utilizes a sensor suite consisting of a LiDAR and RGB camera for crop monitoring on a 12″ × 9″ footprint platform. The main challenge for this platform design is not only its compact size and portability, but its ability to navigate and obtain geo-referenced and time-tagged data in the GNSS-denied environment that exists under the crop canopy. LiDAR and RGB sensors typically rely on inputs from GNSS data. Therefore, a new approach was developed here fusing direct feedback from the RGB camera and a visual-inertial tracking camera supplying robot odometry data. Indoor and outdoor tests were conducted to demonstrate the AgBug’s ability for in-row and under canopy crop monitoring along with the efficacy of sensor fusion approach.
Mathematical and Computational Modeling of Resistance Spot Welding Solidification Process
Authors: Sun Ruiji , Higgins Matthew , Zhang Haiyan H. 
Published: November 2021

Abstract
The paper presents a mathematical model and simulation of resistance spot welding (RSW) of a binary alloy during the final solidification process. After analyzing the heat distribution and heat transfer process, the authors established assumptions for a planar front solidification model. Following the application of initial and boundary conditions, the model is developed numerically to describe the thermal distribution in the resistance spot welding system as a function of position and time. For each value in the time array, a heat transfer profile through the welding sheet is calculated as a piecewise function. The model is further executed with Python, which allows customized inputs of resistance spot welding parameters. Finally, the heat transfer model is simulated with COMSOL Multiphysics in the specific example of 1050 mild steel. Heat transfer module is applied to the proposed mathematical model, and simulation of temperature profile and thermal gradient of the welding zones are developed. The simulation further confirms the mathematical model and provides a demonstration that the temperature decreases through both the water-cooled welding tips and thermal diffusion to the surrounding metal sheets, leading to adjacent heat-affected zones. The model is intended for predicting the resistance spot welding nugget solidification process, as well as to help analyze the effects of welding parameters to achieve a weld nugget of optimal size.
Selection Method of Molding Condition for Self-Adhesive Products Using Only Bamboo Fibers Extracted With a Machining Center Based on Bayesian Optimization
Authors: Tauchi Daigo , Hirogaki Toshiki , Aoyama Eiichi , Ogawa Keiji , Nobe Hiromichi 
Published: November 2021

Abstract
Bamboo grows naturally in Japan as an underutilized forest resource, and its fibers have high specific strength and stiffness, so much so that they are called natural glass fibers. In this study, a method for manufacturing a self-adhesive compact material by pressurizing and heating 100% bamboo fiber after extraction using a machining center, without losing the composition of bamboo, is proposed. This molded body is made of a 100% natural material and has a low environmental impact even when landfilled, contributing to the growth of bamboo for use as a raw material.
In this study, the formation conditions for maximizing the tensile properties when forming platelets were investigated. First, an analysis of the variance was conducted using the orthogonal array test design to identify the factors affecting the objective variable and determine the contribution of each factor. In addition, estimates of the objective variable for the molding conditions were obtained from the Gaussian process regression model in a Bayesian optimization. Furthermore, as a result of comparing the maximum values of the properties of the molding conditions between the analysis of variance and the Gaussian process regression model, the values of the tensile properties were improved in the Gaussian process regression model.
System Design Priority Order Considering Uncertainty in Early Stages
Authors: Kuroyanagi Takumi , Yamada Shuho , Hiramatsu Shigeki , Unesaki Hiroshi , Kondo Shuichi , Aoyama Kazuhiro 
Published: November 2021

Abstract
Herein, we have confirmed the importance of formulating product proposals and product-development processes equipped to cope effectively with uncertainty in the early design stage. The objective of this study was to derive the target performance and design priority order taking into account uncertainties in system design.
Following the concept of set-based design, the approach adopted was to secure a set of solutions as design space that satisfy the target variables demands, dividing the design space into several clusters and evaluating each of the clusters, then gradually narrowing the cluster as the design progresses, and finally extracting the solution space that is desirable. Priority order of design was developed based on the strategy of increasing the degree of freedom of the subsequent process.
The effectiveness of the proposed method was verified using the model of a plug-in hybrid vehicle. From the results, we confirmed the existence of a trade-off between design and target variables preference and development risk, that it is possible to determine the extent to which the solution space can be narrowed, that the shape of the solution space determines the design priorities, and we were able to derive a desirable design priority order according to the target performance.
Fractional Active Disturbance Rejection Controller Based on Improved Differential Evolution Algorithm
Authors: Liang Henghui , Yu Wei , Chen Rui , Luo Ying 
Published: November 2021

Abstract
Although the active disturbance rejection controller can obtain good control performance without relying on specific model information, it targets integer-order systems. Fractional-order characteristics are commonly existed in practical systems. For fractional-order systems, it is more targeted to use the order information of the fractional-order model to design the active disturbance rejection controller, so as to obtain better control performance. A fractional active disturbance rejection controller composed of FOESO and FOPID (IDE-FOPID-FOESO) is proposed in this paper. The fractional-order extended state observer (FOESO) is designed based on the order information and the nonlinear state error feedback is replaced by the fractional-order PID controller (FOPID) whose parameters are obtained by the improved differential evolution algorithm (IDE). For IDE algorithm, the basis vector is randomly selected from the optimal individual population in the mutation strategy, and the scaling factor and cross-probability factor are adaptively adjusted according to the information of the successfully mutated individual in the search process to improve the exploration and mining capabilities of the algorithm. The simulation results show that the IDE algorithm can obtain the better parameters of FOPID faster compared with traditional DE algorithm and the IDE-FOPID-FOESO controller can be better applied to fractional-order systems with better control performance.
Finite Element Modeling of Geometrically Exact Shell With Large Deformation and Rotation
Authors: Wang Jielong 
Published: November 2021

Abstract
This paper developed a new geometrically exact shell element to model the relatively thin structures with large deformations and arbitrary rigid motions. The deformations were well decoupled from rigid motions by using the direct modeling approach. The rotation-free Green-Lagrange strain tensor described the large deformations together with geometrical nonlinearities. Meanwhile, the Wiener-Milenković parameter was applied to vectorial parameterize the arbitrary rotations of the fiber avoiding the singularities usually occurred in the classical shell formula. This paper also designed a new interpolating algorithm without losing objectivity to discretize the vectorial parameters, which improves the robustness of new shell element. The application of Mixed Interpolation of Tensorial Components with 9 nodes (MITC9) makes the shell element shear-locking free and with second-order accuracy. Each node contains five degrees of freedom, three for translations and two for rotations, achieving a minimal set representation of arbitrary motions. These innovations contribute to a new shell formula featuring high computational efficiency with good accuracy. Finally, two flexible multibody dynamic models are discretized by this new shell element. The numerical simulation results of the new shell element have been verified to demonstrate the capability of new shell element dealing with large deformations and arbitrary motions of thin structures.
Nonlinear Finite Element Based Contact Modeling for Bolted Joints in Composite Laminates
Authors: Wang Jielong 
Published: November 2021

Abstract
The paper developed a new numerical joint model based on nonlinear finite element method for the multiple bolted joints in composite laminates. The new joint model consists of the geometrically exact beam and shell elements, as well as the new Hertz contact elements. The geometrically exact elements, beams and shells, are used to model the bolts and laminates with nonlinear deformations, respectively. Meanwhile, the new contact elements apply the Hertz model to simulate the contact, slippage and bolt-laminate interaction. This contact element combined with the multi-point constraints to transfer the shear forces from one shell through beam to another shell element. As an essential part of the bolted joint model, a new type of geometrically exact shell element was developed with second-order accuracy and nine nodes. The strain-displacement relationship of this shell element was formulated according to the rotation-free Green-Lagrange strain tensor. This shell element approximated the higher-order components of strain tensor, ensuring it can accurately describes the real deformation of thin-walled composite laminates. Application of this joint model to a three-bolt, single-shear joint in composite laminates was presented, and its predictions are compared with those of commercial code ABAQUS. Numerical simulation results show that the new joint model is suitable for the design of bolted joints in composite laminates with good accuracy and high efficiency.
Inhomogeneous Folding Modes in Infinite Lattices of Rigid Triangulated Miura-ori
Authors: Lahiri Anandaroop , Pratapa Phanisri P. 
Published: November 2021

Abstract
Infinite two-dimensional tessellations of triangulated Miura-ori with rigid panels are known to exhibit only homogeneous modes of folding, thereby limiting their usefulness in engineering applications. In this work, we show that the corresponding one-dimensional lattices are less restricted and can exhibit inhomogeneous folding modes of deformation. We demonstrate this by looking at the modes in the null space of Bloch-reduced compatibility matrix in a nodal-displacement-based formulation, that is typically employed in the context of origami structural analysis. We compute the deformation modes that vary non-uniformly across the lattice depending on their wavelength, and identify the minimal number of modes that can represent such deformations. We then present a more efficient formulation based on folding-angles to study the deformation modes of infinite one-dimensional rigid triangulated origami lattices. We derive the degrees of freedom of the tessellations in terms of the minimal number of folding-angles that are required to capture the periodic inhomogeneous deformations of the infinite lattices. Within this formulation, we provide the framework to analytically derive the stiffness matrix of the lattice. Finally, we verify the new formulation by comparing the results with the bar-and-hinge model that is based on nodal-displacements. The observations from our work could have implications for the use of rigid panel origami lattices as acoustic metamaterials.
A Contactless Classification Method for Early Detection of Nematodes Using Deep Neural Networks (DNNs) and TensorFlow
Authors: Niu Haoyu , Westphal Andreas , Chen YangQuan 
Published: November 2021

Abstract
Soil-borne plant-parasitic nematodes are microscopic, eellike roundworms. The root-knot nematodes (Meloidogyne spp.) and root-lesion nematodes (Pratylenchus vulnus) are among the most damaging in California, which are difficult to control and can spread easily in soil on tools, boots, and infested plants. Root-knot nematodes can attack many different crops, including nut and fruit trees, usually cause unusual swellings, called galls, on affected plants’ roots. It is not easy to recognize the infestations of these nematodes. For instance, researchers need to dig up walnut trees with symptoms, wash or gently tap the soil from the roots, and examine the roots for galls. The nematode extraction procedures, identification, and enumeration under a microscope are tedious and time-consuming. Therefore, in this article, the authors proposed to use a low-cost contactless radio frequency tridimensional sensor “Walabot,” and Deep Neural Networks (DNNs), to perform the early detection of nematodes in a walnut site. Radiofrequency reflectance of walnut leaves from different nematode infestation levels was measured. The hypothesis was that waveforms generated from walnut leaves can estimate the damage caused by nematodes. DNNs with Tensor-Flow were used to train and test the proposed method. Results showed that the Walabot predicted nematode infestation levels with an accuracy of 82%, which showed great potentials for early detection of nematodes.
Design, Modeling and Ride Analysis of Energy-Harvesting Hydraulically Interconnected Suspension
Authors: Qin Bonan , Chen Yuzhe , Zuo Lei 
Published: November 2021

Abstract
This paper introduces a novel energy-harvesting hydraulically interconnected suspension (EH-HIS) to improve the riding comfort and road handling performance for off-road vehicles while harvesting the vibration energy traditionally dissipated into heat by the oil shock absorbers. To understand the system, we built a model of the off-road vehicle equipped with the EH-HIS and conducted the performance analysis. The system model is established based on the pressure drop principle and validated by commercial simulation software AMESim. The damping characteristic and energy harvesting performance have been investigated based on the mathematical suspension model. Further, a thorough analysis is implemented to compare the dynamic responses of the vehicle equipped with the traditional suspension and EH-HIS under different driving speeds and road classes. Results show that the EH-HIS system can provide tunable asymmetric damping from 3134 Ns/ to 7558 Ns/m, which covers most of the damping range of the off-road vehicles. The average regenerative power of the half EH-HIS system reaches 438 watts, and the corresponding hydraulic efficiency reaches 19%, at a vibration input of 2 Hz frequency and 30 mm amplitude. The ride analysis shows that the vehicle equipped with the EH-HIS system on the D class road has good handling stability and better ride comfort over the traditional suspension.
Quantifying the Predictive Abilities of Speculative Fiction: A Feasibility Study
Authors: Xu Wanyu , Kotecha Maulik C. , Padilla Diego , Jimenez Juliette , McAdams Daniel A. 
Published: November 2021

Abstract
In this article, we explore the capability of speculative fiction to predict future realized technologies. We review a large set of speculative technologies introduced in speculative fiction to determine if the technologies were subsequently realized. Additionally, we explore the time between the speculated introduction and actual realization. Our dataset for analysis is built from the ‘Technovelgy’ database of speculative technologies. A realization assessment methodology is created that includes detailed rubrics to rate and quantify the predictability or realizability of speculative technologies. Three independent raters perform realization assessments for each entry. An inter-rater agreement analysis is carried out to validate the rating method. Based on the dataset of 3095 speculated technologies, 45% are labeled as ‘realized’ by at least one rater. A moderate overall agreement with a Fleiss’ Kappa of 0.57 is reached by all raters. The average time to realization of realized technologies is approximately 45 years with a standard deviation of approximately 34 years. We observe patterns in the realization of speculative technologies and analyze the underlying reasons preventing the technologies from realization. We conclude that speculative fiction predicts future technologies to such a degree that the introduction of speculative technology can be used as an input to designer decision-making.
Mathematical Elucidation of the Traditional Japanese Fan Focusing on Its Structure
Authors: Yamazaki Keiko , Abe Fujiko , Hagiwara Ichiro 
Published: November 2021

Abstract
The Japanese traditional fan, which is a form of origami originating in Japan with a folding culture, has a variety of three-dimensional expression that differs from two-dimensional expression. The image painted on the fan deforms when the fan is folded. In this study, we create a digital fan model for clarifying the deformation on the fan face according to parameters such as length of the bamboo bones. We then validate the digital model with an actual fan. Furthermore, we obtain the original plan view from images of the folded fan as a reverse problem. Because folding fans are made of paper and bamboo and held in the hand, old traditional folding fans are more or less damaged; for example, many culturally valuable folding fans have lost their bones and have damaged edges, have been stretched flat, and have been framed like paintings. Reproducing the original fan without information of the original form is difficult. In the present study, we provide a digital fan model for examining the original fan shape. Old valuable folding fans are treasured by museums and collectors around the world. In future research, we would like to capture such precious folding fans in three-dimensional space applying our digital fan model and to exhibit these fans in a digital museum, providing opportunities not only to enjoy the value of the fans but also to encourage the research of Japanese traditional culture.
Lessons Learned From Three Iterative Studies on Creativity Interventions
Authors: Sahar A. , Shu L. H. 
Published: November 2021

Abstract
Previous work by the authors suggested that performing conflict-processing tasks improved subsequent creative output on the Alternative Uses Test (AUT). Although a positive relationship was established, the number of conflict levels was limited, i.e., previous work included only conflict and no-conflict conditions. Two online follow-up studies included an additional high-conflict level to better understand the relationship between conflict processing and creative performance. These two follow-up studies did not replicate the previous study’s results, but revealed similar, yet non-significant trends.
The current paper compares the three studies, emphasizing differences between them, including study environments, instructions, types of tasks used as interventions, and participant backgrounds, etc. Key conclusions relevant to future, particularly online, studies in design creativity and beyond are as follows.
Effective in-person studies may not translate well to online studies, where participant distraction and lack of motivation are more difficult to detect, monitor and control. Imposing a minimum number of correct responses to complete study tasks may reduce the effects of distraction and lack of motivation. Without in-person presence of both the researcher and the study participant, enhanced feedback for online responses may promote comprehension of instructions. However, enabling online participants to ask questions directly can further reduce confusion and improve task completion.
Prediction of Melt Pool Geometry Using Deep Neural Networks
Authors: Milaat Fahad Ali , Yang Zhuo , Ko Hyunwoong , Jones Albert T. 
Published: November 2021

Abstract
Selective laser melting (SLM) is modernizing the production of highly complex metal parts across the manufacturing industry. However, achieving material homogeneity and controlling thermal deformation remain major challenges for metal-based, additive manufacturing. Therefore, adequate control systems are needed to monitor build processes, and ensure part quality throughout production. Traditionally, control designs relied on physics-based knowledge in analyzing, characterizing, and modeling complex, nonstationary patterns. Recent advancements in machine learning techniques harness the abundance of data to discover effective control designs. In this paper, we investigate the efficacy of a data-driven approach towards in-situ modeling of melt-pool geometry. Specifically, we propose a new methodology that uses a deep neural network architecture to predict melt pool geometries with linear regression models, which manifest during in-situ processes. Experimental results show that our deep neural network model with multiple input features produced 84% goodness of fit score, outperforming the model with a single feature that scored 37% for the given dataset, and the monitored regression models. These outcomes promote further investigation into new and efficient ways for acquiring real-time data from in-situ processes. Our contribution complements the way we understand properties of in-situ data, and predict patterns of melt pools, based on artificial cognition.