bluuebunny/crossref_metadata_2025_split huggingface dataset:
Energy Savings From Electrification of Cooling System
Authors: Ethelbert N. Ezemobi , Ruzimov Sanjarbek , Angelo Bonfitto , Tonoli Andrea , Nicola Amati 
Published: November 2020

Abstract
The cooling system is essential for creating suitable ambient for optimal performance of the engine and a healthy operating temperature to achieve high efficiency. Reaching the desired engine operating condition earlier will not only improve the life span of the engine but also improve the fuel efficiency and reduce environmental pollution. In conventional cooling systems, where the pump is always mechanically connected to the crankshaft, the optimum cooling efficiency of the engine is limited. In fact, the coolant circulates in the engine even when cooling is not desired, and this leads to extended engine warm-up time. Decoupling the pump from the crankshaft and driving the pump with electric motor, the cooling process can be controlled efficiently. The pump is then used only when required. A simple Pulse Width Modulation (PWM) controller with variable duty cycle has been adopted to impose the forced convention flow using a pump. The result of the simulation of the electrically driven pump reported here has been obtained for New European Drive Cycle (NEDC) and shows the possibility of saving about 3% of fuel during the homologation cycle compared to mechanically coupled pump. In addition, the engine operating temperature can be reached seven minutes (7mins) earlier in idling condition, corresponding to about 46.5% fuel saving with respect to the conventional cooling system.
Bi-Level Optimization for Electricity Transaction in Smart Community With Modular Pump Hydro Storage
Authors: Chen Yang , Kou Xiao , Olama Mohammed , Zandi Helia , Liu Chenang , Kassaee Saiid , Smith Brennan T. , Abu-Heiba Ahmad , Momen Ayyoub M. 
Published: November 2020

Abstract
Grid integration of the increasing distributed energy resources could be challenging in terms of new infrastructure investment, power grid stability, etc. To resolve more renewables locally and reduce the need for extensive electricity transmission, a community energy transaction market is assumed with market operator as the leader whose responsibility is to generate local energy prices and clear the energy transaction payment among the prosumers (followers). The leader and multi-followers have competitive objectives of revenue maximization and operational cost minimization. This non-cooperative leader-follower (Stackelberg) game is formulated using a bi-level optimization framework, where a novel modular pump hydro storage technology (GLIDES system) is set as an upper level market operator, and the lower level prosumers are nearby commercial buildings. The best responses of the lower level model could be derived by necessary optimality conditions, and thus the bi-level model could be transformed into single level optimization model via replacing the lower level model by its Karush-Kuhn-Tucker (KKT) necessary conditions. Several experiments have been designed to compare the local energy transaction behavior and profit distribution with the different demand response levels and different local price structures. The experimental results indicate that the lower level prosumers could benefit the most when local buying and selling prices are equal, while maximum revenue potential for the upper level agent could be reached with non-equal trading prices.
Importance-Performance Analysis of Product Attributes Using Explainable Deep Neural Network From Online Reviews
Authors: Joung Junegak , Kim Harrison M. 
Published: November 2020

Abstract
Importance-performance analysis (IPA) is a technique used to understand customer satisfaction and improve the quality of product attributes. This study proposes an explainable deep-neural-network-based method to carry out IPA of product attributes from online reviews for product design. Previous works used shallow neural network (SNN)-based methods to estimate importance values, but it was unclear whether the SNN is an optimal neural network architecture. The estimated importance has high variability by a single neural network from a training set that is randomly selected. However, the proposed method provides importance values with a lower variance by improving the importance estimation of each product attribute in the IPA. The proposed method first identifies the product attributes and estimates their performance. Then, it infers the importance values by combining explanations of the input features from multiple optimal neural networks. A case study on smartphones is used herein to demonstrate the proposed method.
Bifurcation Analysis of a Lane Keeping Controller With Feedback Delay
Authors: Vörös Illés , Takács Dénes 
Published: November 2020

Abstract
The aim of this study is to highlight nonlinear behaviors and periodic orbits of the single-track vehicle model with a delayed feedback controller. Two widely used tire models, namely a linear tire characteristic and Pacejka’s Magic Formula are considered. Linearly stable domains of parameters such as the vehicle speed and the control gains are determined. Periodic solutions originating from Hopf bifurcation points are followed using numerical continuation and the results obtained with the two different tire models are compared. It is shown that neglecting the saturation of the tire lateral forces at total sliding might change the sense of certain Hopf bifurcations from subcritical to supercritical. The results are verified by numerical simulations. The resulting bifurcation diagrams aim to quantify the degree of robustness of these controllers with regards to the initial conditions at various parameter ranges in order to assure stable and safe operation.
An Unsupervised Deep Learning Model to Discover Visual Similarity Between Sketches for Visual Analogy Support
Authors: Zhang Zijian , Jin Yan 
Published: November 2020

Abstract
Visual analogy has been recognized as an important cognitive process in engineering design. Human free-hand sketches provide a useful data source for facilitating visual analogy. Although there has been research on the roles of sketching and the impact of visual analogy in design, little work has been done aiming to develop computational tools and methods to support visual analogy from sketches. In this paper, we propose a computational method to discover visual similarity between sketches, considering the following practical application: Given a sketch drawn by a designer that reflects the designer’s rough idea in mind, our goal is to identify the shape similar sketches that can stimulate the designer to make more and better visual analogies. The first challenge in doing so is how to discover the similar shape features embedded in sketches from various categories. To address this challenge, we propose a deep clustering model to learn a latent space which can reveal underlying shape features for multiple categories of sketches and cluster sketches simultaneously. An extensive evaluation of the clustering performance of our proposed method has been carried out in different configurations. The results have shown that the proposed method can discover sketches that have similar appearance, provide useful explanations of the visual relationship between different sketch categories, and has the potential to generate visual stimuli to enhance designers’ visual imageries.
Curve Cognate Constructions Made Easy
Authors: Sherman Samantha N. , Hauenstein Jonathan D. , Wampler Charles W. 
Published: November 2020

Abstract
Cognate linkages are mechanisms that share the same motion, a property that can be useful in mechanical design. This paper treats planar curve cognates, that is, planar mechanisms whose tracing point draws the same curve. While Roberts cognates for planar four-bars are relatively simple to understand from a geometric drawing, the same cannot be said for planar six-bar cognates, especially the intricate diagrams Dijksman presented in cataloging all the known six-bar curve cognates. The purpose of this article is to show how the six-bar cognates can be easily understood using kinematic equations written using complex vectors, giving a simple method for generating these cognates as alternatives in a mechanical design. The simplicity of the approach enables the derivation of cognates for eight-bars and possibly beyond.
Experimental Investigation of Microfluidic Feature Manufacturing by Digital Light Processing Stereolithography
Authors: Rebaioli Lara , Fassi Irene 
Published: November 2020

Abstract
Lab on Chips (LOCs) are devices, mostly based on microfluidics, that allow to perform one or several chemical, biochemical or biological analysis in a miniaturized format on a single chip. The Additive Manufacturing processes, and in particular the Digital Light Processing stereolithography (DLP-SLA), could quickly produce a complete LOC with high resolution 3D features in a single step, i.e. without the need for assembly processes, and using low cost and user-friendly desktop machines. However, the potential of DLP-SLA to produce non-planar channels or channels with complex sections has not been fully investigated yet. This study proposes a benchmark artifact (including also some channels with their axis lying in a plane parallel to the machine building platform) aiming at assessing the capability and performance of DLP-SLA for manufacturing microfeatures for microfluidic devices. A proper experimental campaign was performed to evaluate the effect of the main process parameters (namely, layer thickness and exposure time) on the process performance. The results pointed out that both the process parameters influence the quality and dimensional accuracy of the analyzed features.
Switching From Petroleum- to Bio-Based Plastics: Visualization Tools to Screen Sustainable Material Alternatives During the Design Process
Authors: Saidani Michael , Pan Erik , Kim Harrison 
Published: November 2020

Abstract
The recent development in technology has made bio-based plastics an increasingly attractive alternative to petroleum-based plastics to tackle plastic pollution. However, currently, bio-based plastics have not been widely adopted in the design and manufacturing of new products. To advocate the use of bio-based plastics, this paper proposes two visualization-based tools to educate designers and engineers about the availabilities and the properties of different bio-based plastics. After analyzing the literature on visual tools for sustainable design and material selection, two new prototype tools for screening bio-plastic alternatives are designed with the advice and support of the engineers of a major U.S. manufacturer of agricultural equipment. Surveys and focus groups with the manufacturer’s engineers are conducted to improve the tools, and a first case study is completed to examine their usefulness.
Stiffness Modulation for a Planar Mobile Cable-Driven Parallel Manipulators via Structural Reconfiguration
Authors: Raman Adhiti , Schmid Matthias , Krovi Venkat 
Published: November 2020

Abstract
Mobile Cable-Driven Parallel Manipulators (m-CDPM) are a sub-class of CDPM with greater-capabilities (antagonistic cable-tensioning and reconfigurability) by virtue of mobility of the base-winches. In past work, we had also explored creation of adjustable spring-stiffness modules, in-line with cables, which decouple cable-stiffness and cable-tensions. All these internal-freedoms allow an m-CDPM to track desired trajectories while equilibrating end-effector wrenches and improving lateral disturbance-rejection. However, parameter and configuration selection is key to unlocking these benefits.
To this end, we consider an approach to partition task-execution into a primary (fast) winch-tension control and secondary (slow) reconfiguration and joint-stiffness modulation. This would enable a primary trajectory-tracking task together with secondary task-space stiffness tailoring, using system-reconfiguration and joint-stiffness modulation. In this paper, we limit our scope to feasibility-evaluation to achieve the stiffness modulation as a secondary goal within an offline design-optimization setting (but with an eye towards real-time implementation).
These aspects are illustrated in the context of a 3-PRP m-CDPM for tracking a desired trajectory within its wrench-feasible workspace. The secondary-task is the directional-alignment and shaping of the stiffness ellipsoid to shape the disturbance-rejection characteristics along the trajectory. The optimization is solved through constrained minimization of a multi-objective weighted cost function subject to non-linear workspace feasibility, and inequality stiffness and tension constraints.
A Review of Part Filtering Methods for Additive Manufacturing
Authors: Bracken Jennifer E. , McComb Christopher , Simpson Timothy W. , Jablokow Kathryn W. 
Published: November 2020

Abstract
As additive manufacturing (AM) increases in popularity, many companies seek to identify which parts can be produced via AM. This has led to new areas of research known as “part filtering”, “part selection”, or “part identification” for AM. Numerous methods have been proposed to quantify the suitability of a design to be made with AM, and each has its own benefits and drawbacks. This paper reviews popular methods of part filtering and elaborates on the advantages and disadvantages of the various approaches. The approaches for part filtering, and the example methods, are categorized and sorted along a continuum of opportunistic and restrictive methods in order to clarify use cases for various part filtering techniques. The approaches are also examined through the lens of specificity of process, as some are designed to be process agnostic, while others are customized for a specific AM technology or even a specific AM system. Finally, current gaps that exist in the part filtering research literature are discussed to help identify necessary and promising directions for future investigation.
Multiscale Tomographic Wave-Matter Interaction Modeling to Enable Artifact-Free Material Defect Reconstruction
Authors: Steuben J. C. , Michopoulos J. G. , Iliopoulos A. P. , Graber B. D. , Birnbaum A. J. 
Published: November 2020

Abstract
The fabrication and subsequent in-service excitation of materials/structures invariably generates significant defects including cracks, voids and inclusions, spanning many length scales. The various technologies available for detecting and qualitatively describing such defects suffer from the introduction of various degrees inaccuracy when it comes to the quantification of defect geometry. This can have an adverse impact on modeling and understanding how the material/part/structure performance is effected by these defects. The main cause of this shortcoming is that aspects of the physical processes used to interrogate the material system, using monochromatic or polychromatic waves such as X-ray, mm-wave, or ultrasound, are not taken into account. These waves interact with the multiscale defect ensemble in a complex fashion that inevitably produces spurious “artifacts” in the resulting data, which cannot be removed via conventional data post-processing. These artifacts then introduce unacceptable levels of error when reconstructing defect geometry and computing the remaining lifespan of defect-bearing materials/structures. The present work introduces preliminary efforts towards a multiscale modeling and simulation framework for capturing the interactions of waves (such as X-rays) with materials bearing defect ensembles. It is shown that conventional approaches such as ray tracing are not adequate, and a more robust solution to the relevant wave equations utilizing the Finite Element discretization is employed. A general parameterization of defect geometries based on superquadratic functions is also introduced, and the interactions of defects modeled in this fashion with X-rays are investigated. It is also shown that this combination of parameterization and modeling techniques allows the recovery of true, artifact-free defect geometry utilizing classical inverse methods. The methodology is demonstrated using synthetic tomographic data, and the path forward to a more complete realization of this technology is outlined.
Rapid Fabrication of POM Flexure Hinges via a Combined Injection Molding and Stereolithography Approach
Authors: Valori Marcello , Surace Rossella , Basile Vito , Luzi Luca , Vertechy Rocco , Fassi Irene 
Published: November 2020

Abstract
The aim of the research activity reported in this paper is to investigate a promising approach for the rapid and customized manufacturing of miniature flexure hinges with predictable and reliable mechanical performances. With these premises, the proposed methodology combines stereolithography and micro injection moulding, with the aim of exploiting the advantages of both manufacturing techniques. As a preliminary case study, a corner filleted hinge made of polyoxymethylene (POM) has been realized in multiple identical specimens that are then characterized in terms of both dimensional accuracy and function.
Using Decision Trees Supported by Data Mining to Improve Function-Based Design
Authors: Ferrero Vincenzo J. , Alqseer Naser , Tensa Melissa , DuPont Bryony 
Published: November 2020

Abstract
Engineering designers currently use downstream information about product and component functions to facilitate ideation and concept generation of analogous products. These processes, often called Function-Based Design, can be reliant on designer definitions of product function, which are inconsistent from designer to designer. In this paper, we employ supervised learning algorithms to reduce the variety of component functions that are available to designers in a design repository, thus enabling designers to focus their function-based design efforts on more accurate, reduced sets of potential functions. To do this, we generate decisions trees and rules that define the functions of components based on the identity of neighboring components. The resultant decision trees and rulesets reduce the number of feasible functions for components within a product, which is of particular interest for use by novice designers, as reducing the feasible functional space can help focus the design activities of the designer. This reduction was evident in both case studies: one exploring a component that is known to the designer, and the other looking at defining function of an unrecognizable component. The work presented here contributes to the recent popularity of using product data in data-driven design methodologies, especially those focused on supplementing designer cognition. Importantly, we found that this methodology is reliant on repository data quality, and the results indicate a need to continue the development of design repository data schemas with improved data consistency and fidelity. This research is a necessary precursor for the development of function-based design tools, including automated functional modeling.
Analysis of Planar Multilink Cable Driven Robots Using Internal Routing Scheme
Authors: Ramadoss Vishal , Lau Darwin , Zlatanov Dimiter , Zoppi Matteo 
Published: November 2020

Abstract
The multilink cable driven robot (MCDR) is an extension of the cable robots where the moving platform is replaced by a multibody chain. It is typically an open-chain structure with multiple links and complex cable routing. This design introduces the advantages of having a serial kinematic structure and preserves the benefits associated with cable-driven parallel mechanism. To achieve a minimum number of actuating cables while possessing a large workspace region, a novel internal cable routing scheme is proposed. It is shown that by incorporating internal routing with multi-segment cables, any serial chain with n degrees of freedom can be controlled with n + 1 cables. In this work, through studying the kinematics and statics, we demonstrate how internally-routed cable actuation of multilink manipulators have an increased workspace and reduced cable forces to execute trajectories.
Modeling Considerations for Testing Full-Scale Offshore Wind Turbine Nacelles With Hardware-in-the-Loop
Authors: Heinold Kirk , Panyam Meghashyam , Bibo Amin 
Published: November 2020

Abstract
When compared to open-loop configuration, full-scale wind turbine nacelle testing with Hardware-In-the-Loop (HIL) configuration allows for coupled electro-mechanical as well as full operational certification tests with the native nacelle controllers. This configuration requires a full turbine real-time simulation running in parallel to the nacelle under test. In this study, a baseline simulation model is used to investigate the nacelle fidelity necessary to capture dynamic characteristics of interest while meeting the real-time requirements. The same model is also utilized to understand the influence of different boundary conditions seen by the nacelle when mounted on a test bench without a rotor, tower, and platform. The results show that the torsional dynamics are mainly governed by the flexibility of the main shaft and the gearbox supports. It is also demonstrated that the abstraction of the nacelle leads to a torsional frequency shift and higher frequency content in component responses necessitating compensation techniques for proper implementation of HIL testing.
Microgrid Power Management With Integrated Quality of Life Considerations
Authors: Suk Hailie , Sharma Ayushi , Balu Nellippallil Anand , Das Ashok K. , Hall John 
Published: November 2020

Abstract
Electrification can act as a catalyst in social progress. In some communities, grid connection is not possible. As such, microgrids are a viable alternative to provide access to electricity. Yet, progress can be impacted by challenges with insufficient energy supply. In such scenarios, it is important to understand the relationships between electricity supply and social development in managing available resources.
We propose a framework to relate quality of life with power management, such that progress is not hindered when available energy is insufficient. In this paper, electrical loads for pumping water, powering streetlights, and powering household devices are examined. A compromise decision support problem (cDSP) is developed to balance the produced and consumed energy. We develop a set of power management options by exploring the solution space developed from performing the cDSP, anchored in quality of life. Organizations engaged in sustainable development can select the solution most appropriate for the community.
A salient feature of the framework is the versatility. The formulation can be modified for different requirements, communities, and time periods. A test problem is used to illustrate the flexibility of the approach. This framework is constructed to support decision making for microgrid operation to continue to uplift communities.