bluuebunny/crossref_metadata_2025_split huggingface dataset:
Systematic Enumeration and Identification of Unique Spatial Topologies of 3D Systems Using Spatial Graph Representations
Authors: Peddada Satya R. T. , Dunfield Nathan M. , Zeidner Lawrence E. , James Kai A. , Allison James T. 
Published: November 2021

Abstract
Systematic enumeration and identification of unique 3D spatial topologies of complex engineering systems such as automotive cooling layouts, hybrid-electric power trains, and aero-engines are essential to search their exhaustive design spaces to identify spatial topologies that can satisfy challenging system requirements. However, efficient navigation through discrete 3D spatial topology options is a very challenging problem due to its combinatorial nature and can quickly exceed human cognitive abilities at even moderate complexity levels. Here we present a new, efficient, and generic design framework that utilizes mathematical spatial graph theory to represent, enumerate, and identify distinctive 3D topological classes for an abstract engineering system, given its system architecture (SA) — its components and interconnections. Spatial graph diagrams (SGDs) are generated for a given SA from zero to a specified maximum crossing number. Corresponding Yamada polynomials for all the planar SGDs are then generated. SGDs are categorized into topological classes, each of which shares a unique Yamada polynomial. Finally, for each topological class, one 3D geometric model is generated for an SGD with the fewest interconnect crossings. Several case studies are shown to illustrate the different features of our proposed framework. Design guidelines are also provided for practicing engineers to aid the utilization of this framework for application to different types of real-world problems.
A Multi-Scale Topology Optimization Approach for Optimal Macro-Layout and Local Grading of TPMS-Based Lattices
Authors: Strömberg Niclas 
Published: November 2021

Abstract
The use of lattice structures in design for additive manufacturing has quickly emerged as a popular and efficient design alternative for creating innovative multifunctional lightweight solutions. In particular, the family of triply periodic minimal surfaces (TPMS) studied in detail by Schoen for generating frame-or shell-based lattice structures seems extra promising. In this paper a multi-scale topology optimization approach for optimal macro-layout and local grading of TPMS-based lattice structures is presented. The approach is formulated using two different density fields, one for identifying the macro-layout and another one for setting the local grading of the TPMS-based lattice. The macro density variable is governed by the standard SIMP formulation, but the local one defines the orthotropic elasticity of the element following material interpolation laws derived by numerical homogenization. Such laws are derived for frame- and shell-based Gyroid, G-prime and Schwarz-D lattices using transversely isotropic elasticity for the bulk material. A nice feature of the approach is that the lower and upper additive manufacturing limits on the local density of the TMPS-based lattices are included properly. The performance of the approach is excellent, and this is demonstrated by solving several three-dimensional benchmark problems, e.g., the optimal macro-layout and local grading of Schwarz-D lattice for the established GE-bracket is identified using the presented approach.
A Framework to Study Human-AI Collaborative Design Space Exploration
Authors: Viros-i-Martin Antoni , Selva Daniel 
Published: November 2021

Abstract
This paper presents a framework to describe and explain human-machine collaborative design focusing on Design Space Exploration (DSE), which is a popular method used in the early design of complex systems with roots in the well-known design as exploration paradigm. The human designer and a cognitive design assistant are both modeled as intelligent agents, with an internal state (e.g., motivation, cognitive workload), a knowledge state (separated in domain, design process, and problem specific knowledge), an estimated state of the world (i.e., status of the design task) and of the other agent, a hierarchy of goals (short-term and long-term, design and learning goals) and a set of long-term attributes (e.g., Kirton’s Adaption-Innovation inventory style, risk aversion). The framework emphasizes the relation between design goals and learning goals in DSE, as previously highlighted in the literature (e.g., Concept-Knowledge theory, LinD model) and builds upon the theory of common ground from human-computer interaction (e.g., shared goals, plans, attention) as a building block to develop successful assistants and interactions. Recent studies in human-AI collaborative DSE are reviewed from the lens of the proposed framework, and some new research questions are identified. This framework can help advance the theory of human-AI collaborative design by helping design researchers build promising hypotheses, and design studies to test these hypotheses that consider most relevant factors.
Operational Modal Analysis of a Rotating Structure Subject to Random Excitation Using a Tracking Continuously Scanning Laser Doppler Vibrometer via an Improved Demodulation Method
Authors: Lyu Linfeng , Zhu Weidong 
Published: November 2021

Abstract
A new operational modal analysis (OMA) method is developed for estimation of modal parameters (MPs) of a rotating structure (RS) subject to random excitation using a nonuniform rotating beam model, an image processing method, and an improved demodulation method. The solution to the governing equation of a nonuniform rotating beam is derived, which can be considered as the response of the beam measured by a continuously scanning laser Doppler vibrometer (CSLDV) system. A recently developed tracking CSLDV system can track and scan the RS. The image processing method determines the angular position of the RS so that the tracking CSLDV system can sweep its laser spot along a time-varying path on it. The improved demodulation method obtains undamped mode shapes (UMSs) of the RS by multiplying its measured response by sinusoids whose frequencies are its damped natural frequencies (DNFs) that are obtained from the fast Fourier transform of the measured response. Experimental investigation of the OMA method using the tracking CSLDV system is conducted, and MPs of a rotating fan blade (RFB), including DNFs and UMSs, with different constant speeds and its instantaneous MPs with a non-constant speed are estimated. Estimated first DNFs and UMSs of the stationary fan blade and RFB are compared with those from the lifting method that was previously developed by the authors.
Cost of Controls for Multi-Rotor Drones
Authors: Hoang Samantha , Shen I. Y. 
Published: November 2021

Abstract
This paper focuses on the energy costs of using a control system to follow a specific trajectory as opposed to directly choosing the inputs in an open-loop sense to achieve a specific trajectory. Results show that the PID controller will use 7–13% more energy than the open-loop system for the small-amplitude trajectories and 62–74% more energy for the high-amplitude trajectories while maintaining trajectory errors of approximately 5% and 19%, respectively. The PD controller will use 0.03–0.04% less energy than the open loop system for the small-amplitude trajectories and 0.35% less energy for the high-amplitude trajectories while maintaining similar trajectory errors to the PID controller. The two trajectories have different trajectory errors (5% vs. 19%). Therefore, the controller does not have to move the rotor speeds to the same level, thus causing more energy saved for the large-amplitude trajectory. Based on these simulations, it can be concluded that the PD controller will be significantly more energy efficient than the PID controller while maintaining similar trajectory errors throughout the entire flight. This is because the integral control term in the PID controller accumulates the error so any initial large errors in the position will result in large controller inputs later in the flight. The accumulation of error in the integral term then results in large variations in rotor speed which often leads to a higher energy usage. Thus, a PD controller may be a more energy efficient choice while maintaining effectiveness in trajectory-tracking. These results highlight the importance of considering and comparing the energy costs of various control systems when designing controllers for multi-rotor drones.
Intelligent Design Prediction Aided by Non-Uniform Parametric Study and Machine Learning in Feature Based Product Development
Authors: Ramnath Satchit , Ma Jiachen , Shah Jami J. , Detwiler Duane 
Published: November 2021

Abstract
Automotive body structure design is critical to achieve lightweight and crash worthiness based on engineers’ experience. In the current design process, it frequently occurs that designers use a previous generation design to evolve the latest designs to meet certain targets. However, in this process the possibility of adapting design ideas from other models is unlikely. The uniqueness of each design and presence of non-uniform parameters further makes it difficult to compare two or more designs and extract useful feature information. There is a need for a method that will fill the missing gap in assisting designers with better design options. This paper aims to fill this gap by introducing an innovative approach to use a non-uniform parametric study with machine learning in order to make valuable suggestions to the designer. The proposed method uses data sets produced from experiment design to reduce the number of parameters, perform parameter correlation studies and run finite element analysis (FEA), for a given set of loads. The response data generated from this FEA is then used in a machine learning algorithm to make predictions on the ideal features to be used in the design. The method can be applied to any component that has a feature-based parametric design.
Service Recommendation Based on Dynamic User Portrait: An Integrated Approach
Authors: Tang Yuqi , Li Shanshan , Song Wenyan , Zhou Caibo , Niu Zixuan 
Published: November 2021

Abstract
The rapid development of computer science and internet technology has enabled the prevailing digital transformation. Nowadays, more and more service companies have built up their smart service platforms. Due to the rapid growth in services, consumers may not get their personalized needs accurately. So companies have concentrated more on smart services than before, such as proactive recommending services. However, few studies focus on smart service recommendation, and few recommendation methods are suitable for service recommending. This paper proposes a smart service recommendation method based on user dynamic portrait and collaborative filtering. Theoretically, this method improves the accuracy of recommendation and reflects the change of users’ preference. Finally, we use the users’ data from a science and technology service platform to verify the effectiveness of the method.
CreativeGAN: Editing Generative Adversarial Networks for Creative Design Synthesis
Authors: Heyrani Nobari Amin , Rashad Muhammad Fathy , Ahmed Faez 
Published: November 2021

Abstract
Modern machine learning techniques, such as deep neural networks, are transforming many disciplines ranging from image recognition to language understanding, by uncovering patterns in big data and making accurate predictions. They have also shown promising results for synthesizing new designs, which is crucial for creating products and enabling innovation. Generative models, including generative adversarial networks (GANs), have proven to be effective for design synthesis with applications ranging from product design to metamaterial design. These automated computational design methods can support human designers, who typically create designs by a time-consuming process of iteratively exploring ideas using experience and heuristics. However, there are still challenges remaining in automatically synthesizing ‘creative’ designs. GAN models, however, are not capable of generating unique designs, a key to innovation and a major gap in AI-based design automation applications. This paper proposes an automated method, named CreativeGAN, for generating novel designs. It does so by identifying components that make a design unique and modifying a GAN model such that it becomes more likely to generate designs with identified unique components. The method combines state-of-art novelty detection, segmentation, novelty localization, rewriting, and generative models for creative design synthesis. Using a dataset of bicycle designs, we demonstrate that the method can create new bicycle designs with unique frames and handles, and generalize rare novelties to a broad set of designs. Our automated method requires no human intervention and demonstrates a way to rethink creative design synthesis and exploration. For details and code used in this paper please refer to http://decode.mit.edu/projects/creativegan/.
Reliability Constrained Optimal Design of Distributed Generators in Power System Under Load and Wind Turbine Generation Uncertainty
Authors: Chen Zhetao , Xi Zhimin 
Published: November 2021

Abstract
Power systems are designed to meet power demands of the communities with high reliability. Distributed generators (DGs) could play an essential role in improving the power system reliability and resilience. To date, influence of the uncertainty of the DGs to power system reliability has not been well addressed. Consequently, placement of the DGs considering reliability constraints may not be optimally conducted. This paper proposes reliability analysis and design of power systems under time-dependent load uncertainty and wind power generation uncertainty using an efficient uncertainty quantification (UQ) method, i.e., the eigenvector dimension reduction (EDR) method. Furthermore, binary particle swarm optimization (B-PSO) is proposed to address the optimal placement of DGs considering the reliability constraint. Two case studies, including an IEEE 14-bus power system and an IEEE 57-bus power system, are used to demonstrate the effectiveness of the proposed methodology.
A Mixed Sideslip Yaw Rate Stability Controller for Over-Actuated Vehicles
Authors: Gimondi Alex , Corno Matteo , Savaresi Sergio M. 
Published: November 2021

Abstract
Electronic stability control (ESC) has become a fundamental safety feature for passenger cars. Commonly employed ESCs are based on differential braking. Nevertheless, electric vehicles’ growth, particularly those featuring an over-actuated configuration with individual wheel motors, allows for maintaining driveability without slowing down the vehicle. Standard control strategies are based on yaw rate tracking. The reference signal is model-based and needs precise knowledge of the friction coefficient. To increase the system robustness, more sophisticated approaches that include vehicle sideslip are introduced. Still, it is unclear how the two signals have to be weighted, and rarely proposed controllers have been experimentally validated. In this paper, we present a mixed sideslip and yaw rate stability controller. The mixed approach allows to address the control design as a single-input single-output problem simplifying the tuning process. Furthermore, we explain the rationale behind the choice of the weighting parameter. Eventually, the proposed ESC is validated following EU regulation in simulation and with an experimental vehicle on dry asphalt and snow. The results obtained in all the performed tests demonstrate that the proposed control strategy is robust and effective. The mixed approach is able to halve the sideslip in critical conditions with respect to a pure yaw rate approach.
Variable Stiffness Design and Analysis of Flexure Hinge Based on ID-LEJ
Authors: Li Yanlin , Qiu Lifang , Zhou Kang , Li Chongxiang 
Published: November 2021

Abstract
In recent years, compliant mechanisms have attracted more and more attention of scholars at home and abroad, and achieved rapid development. The introduction of flexible variable stiffness design in flexible mechanism can not only improve the safety of human-computer interaction, but also improve the adaptability of the machine. Because the ID-LEJ (Inside-Deployed Lamina Emergent Joint) is a kind of LEMs and has very good bending performance. In this paper, the rotary ID-LEJ flexure hinge is proposed based on ID-LEJ hinge, to maximize the bending capacity of the hinge. In order to realize variable stiffness of rotary ID-LEJ, four sliders are arranged in the rotary ID-LEJ to change the stiffness of the hinge. The variable stiffness of the hinge is analyzed by Equivalent system and Finite element analysis. When the slider is symmetrically divided (yl1=yl2=yr1=yr2) the bending equivalent constant of the variable stiffness hinge varies continuously from 30.8 n · mm / rad ∼ 38.2n · mm / rad. And when the slider is asymmetrically distributed (yl1=yl2≠yr1=yr2) The bending equivalent constant of the variable stiffness hinge varies continuously from 30.8n · mm / rad to 34.2 · mm / rad. The results show that the variable stiffness performance is very flexible and stable.
Challenges and Strategies in Remote Design Collaboration During Pandemic: A Case Study in Engineering Education
Authors: Belanger Elise , Bartels Caroline , She Jinjuan 
Published: November 2021

Abstract
The COVID-19 pandemic forced college education to shift from face-to-face to online instruction. This effort is particularly challenging for freshmen and sophomore students, in engineering design projects where collaborations are needed. The study aims to qualitatively understand challenges and possible strategies revealed by students in remote design collaboration through the lens of an undergraduate-level engineering design introduction class. The authors closely observed team members’ struggles and how they handled them through bi-weekly and final reflections in a semester-long project. The challenges and strategies from 11 teams (42 students) were analyzed and implications for future engineering design education were discussed. The findings provide insights to experimentations that aim to establish a successful remote learning environment that reaches core education objectives of engineering design while also helping students adapt to a geographically distributed engineering workforce in future. The study also illustrated the usefulness of reflections as a tool to capture students’ learning dynamics.
Evolutionary Discrete Multi-Material Topology Optimization Using CNN-Based Simulation Without Labeled Training Data
Authors: Yang Xingtong , Li Ming , Zhu Liangchao , Zhong Weidong 
Published: November 2021

Abstract
Multi-material topology optimization problem under total mass constraint is a challenging problem owning to the incompressibility constraint on the summation of the usage of the total materials. A novel optimization approach is proposed here that utilizes the wide search space of the genetic algorithm, and greatly reduced computational effects achieved from the direct structure-performance mapping. The former optimization is carefully designed based on our recent theoretical insights, while the latter simulation is derived via a novel convolutional neural network based simulation which does not rely on any labeled simulation data but is instead designed based on a physics-informed loss function. As compared with results obtained using latest approach based on density interpolation, structures of better compliances are observed under acceptable computational costs, as demonstrated by our numerical examples.
Techno-Economic and Environmental Assessment of Dairy Products: A Case Study in Southeast Idaho, USA
Authors: Van Slyke Brekke , Mirkouei Amin , McKellar Michael 
Published: November 2021

Abstract
Idaho was the 3rd largest milk producer in the United States in 2019, and the dairy industry remains one of the most considerable sections of the state’s economy. The dairy industry itself has many effects on the environment, and there are many opportunities within this industry to improve its environmental impacts. This paper explores a dairy processing facility (under current operating norms and an improved set of operating conditions) to assess techno-economic aspects, determine the gate-to-gate environmental impacts, and identify critical process parameters. In this study, the environmental impact was determined using the life cycle assessment method to evaluate greenhouse gas emissions in kg CO2 equivalents per kg of packaged milk. The economic assessment was performed, using a life cycle costing analysis method for estimating the net present value, payback period, and total profit of the various scenarios, as well as determining the major cost drivers to the process. The results show that the total environmental impact of 1 kg of packaged milk was between 0.0102 to 0.0125 kg CO2 equivalents. It was also determined that the proposed adjustments to the operating conditions could reduce the heating costs by 84% and the overall annual costs by 16.3%. This study can help provide justification for further research when determining the optimum operating conditions and energy sources for dairy processing equipment and facilities. This includes investigating both real-world and theoretical models when making plans for improving dairy processes.
Automatically Discovering Mechanical Functions From Physical Behaviors via Clustering
Authors: Chiu Kevin N. , Anderson David , Fuge Mark D. 
Published: November 2021

Abstract
Computational design methods provide opportunities to discover novel and diverse designs that traditional optimization approaches cannot find or that use physical phenomena in ways that engineers have overlooked. However, existing methods require supervised objectives to search or optimize for explicit behaviors or functions — e.g., optimizing aerodynamic lift. In contrast, this paper unpacks what it means to discover interesting behaviors or functions we do not know about a priori using data from experiments or simulation in a fully unsupervised way. Doing so enables computers to invent or re-invent new or existing mechanical functions given only measurements of physical fields (e.g., pressure or electromagnetic fields) without directly specifying a set of objectives to optimize.
This paper explores this approach via two related parts. First, we study clustering algorithms that can detect novel device families from simulation data. Specifically, we contribute a modification to the Hierarchical Density-Based Spatial Clustering of Applications with Noise algorithm via the use of the silhouette score to reduce excessively granular clusters. Second, we study multiple ways by which we preprocess simulation data to increase its discriminatory power in the context of clustering device behavior. This leads to an insight regarding the important role that a design’s representation has in compactly encoding its behavior.
We test our contributions via the task of discovering designs that function as fluidic logic gates. We generate synthetic data that mimics fluidic devices and show that our proposed contributions better discover logic gates, as measured by adjusted Rand score. Specifically, combining our Resolution Selection preprocessing and principal component analysis resulted in the highest and tightest spread of adjusted Rand scores on our tested datasets. This opens up new avenues of research wherein computers can automatically explore different types of physics and then derive new device functions, behaviors, and structures without the need for human labels or guidance.