bluuebunny/crossref_metadata_2025 huggingface dataset:
Linking Performance Data and Geospatial Information of Manufacturing Assets Through Standard Representations
Authors: Hanke Aaron , Vernica Teodor , Bernstein William Z.
Published: November 2020

Interoperability across emerging visualization modalities, including augmented reality (AR) and virtual reality (VR), remains a challenge with respect to industrial applications. One critical issue relates to the lack of standard approaches for coordinating geospatial representations that are required to facilitate AR/VR scenes with domain-specific information in the form of time-series data, solid models, among other data types. In this paper, we focus on the linking of manufacturing asset data via the MTConnect standard with geospatial data via the IndoorGML standard. To this end, we demonstrate the utility of this integration through two visualization-based prototype implementations, including one focused on (i) monitoring production facilities to improve situational awareness and (ii) evaluating and delivering suggested navigation paths in production facilities. We then comment on implications of such standards-driven approaches for related domains, including AR prototype development and automatic guided vehicles.
Identification of Non-Transversal Bifurcations of Linkages
Authors: Müller Andreas , López Custodio P. C. , Dai J. S.
Published: November 2020

The local analysis is an established approach to the study of singularities and mobility of linkages. Key result of such analyses is a local picture of the finite motion through a configuration. This reveals the finite mobility at that point and the tangents to smooth motion curves. It does, however, not immediately allow to distinguish between motion branches that do not intersect transversally (which is a rather uncommon situation that has only recently been discussed in the literature). The mathematical framework for such a local analysis is the kinematic tangent cone. It is shown in this paper that the constructive definition of the kinematic tangent cone already involves all information necessary to separate different motion branches. A computational method is derived by amending the algorithmic framework reported in previous publications.
A Low Cost Motion Analysis System Based on RGB Cameras to Support Ergonomic Risk Assessment in Real Workplaces
Authors: Altieri Alex , Ceccacci Silvia , Talipu Abudukaiyoumu , Mengoni Maura
Published: November 2020

This paper introduces a motion analysis system based on a network of common RGB cameras, which provides the measurement of various angles considered for postural assessment, in order to facilitate the evaluation of the ergonomic indices commonly used for the determination of risk of musculoskeletal disorders of operators in manufacturing workplaces. To enable the tracking of operator postures during the performed tasks, the system exploits the multi person keypoints detection library “OpenPose”. The proposed system has been validated with a real industrial case study regarding a washing machine assembly line. Results suggest how the proposed system supports ergonomists in risk assessment of musculoskeletal disorders through the OCRA index.
Additively Manufactured Tags for Cast Part Traceability Using Two-Dimensional Digital Code Direct-Part-Marking
Authors: Uyan Tekin , Jalava Kalle , Orkas Juhani , Otto Kevin
Published: November 2020

Statistical quality control is applied in factories and foundries to identify special cause defects and to identify root causes through statistical correlation of process input variations to defects. A difficulty arises in associating process data collected with individual cast parts as they are worked through the foundry and out into the supply chain. Typically, alphanumeric labels for marking castings and manual identification of the castings with route-paper based tracing approaches have been used. Such manual based systems make root cause analysis of quality defect issues tedious. We here develop a semi-automated approach using 3D printed sand mold inserts shaped as 2D matrix codes which thereby permit directly cast identification code into the parts. This enables automated part tracking at the very beginning of the casting process including mold making. Automated scan-based tracking of parts through a foundry and subsequent supply chain allows for statistical process data collected to also be associated with each part processed with unique identification.
Experimental Modal Analysis of Business Jet Fuselage Tail Section Sub-Assemblies
Authors: Donaldson Ian A. , Mechefske Chris K.
Published: November 2020

Experimental modal testing is a technique through which the dynamic response of a system can be found. Parameters such as the natural frequencies and mode shapes of a system can be extracted through experimentation, and these results can be used to confirm computational models and guide structural improvements. This paper provides an overview of experimental modal analysis performed on two aircraft fuselage half scale subassemblies, with the use of shaker excitation. The experimental methodology including the construction of each structure, data acquisition parameters, and validity checks, is presented in detail. Linearity and repeatability checks were used to validate the testing methodology and increase the level of confidence in the experimental results. The experimental natural frequencies were correlated with the computational results, and recommendations were made. The experimental results presented in this work provide a basis for computational model updating work to be considered in future work.
Design of a Miniaturized Actuation System for Robotic Lumbar Discectomy Tools
Authors: Craker Ryan , Johnson Benjamin V. , Sakthivel Haripriya , Cappelleri David J.
Published: November 2020

In this paper, we present the design of a miniaturized actuation system for robotic lumbar discectomy tools. Lumbar dis-cectomy is one of the most common types of back surgery in the United States. Our previous work proposed a new robotic lumbar discectomy (RLD) system consisting of teleoperated articulated instruments inside a robotic cannula for performing this operation. The robotic cannula is used for the independent translation and rotation of the instruments residing in it. Due to the large servo-motor based actuation systems of the initially developed instruments, it was not possible to achieve a fully integrated RLD system. Here, we present the design of a shape memory alloy actuation system for teleoperating RLD tools that allows full integration with the robotic cannula. More than a 10X size reduction in footprint has been achieved. Experimental results show that SMA-driven actuation system meets the instrument range-of-motion, manipulation, and grasping force requirements. Integrated system tests demonstrate the successful operation of the miniaturized RDL tools with the robotic cannula.
Investigating Cyber-Physical Threats of Numerically Controlled Manufacturing Processes
Authors: Piacenza Joseph , Faller Kenneth J. , Regez Bradley , Gomez Luisfernando
Published: November 2020

Motivated by cyber-physical vulnerabilities in precision manufacturing processes, there is a need to externally examine the operational performance of Computer Numerically Controlled (CNC) manufacturing systems. The overarching objective of this work is to design and fabricate a proof-of-concept CNC machine evaluation device, ultimately re-configurable to the mill and lathe machine classes. This device will assist in identifying potential cyber-physical security threats in manufacturing systems by identifying perturbations, outside the expected variations of machining processes, and comparing the desired command inputted into the numerical controller and the actual machine performance (e.g., tool displacement, frequency). In this directed research, a device design is presented based on specific performance requirements provided by the project sponsor. The first design iteration is tested on a Kuka KR 6 R700 series robotic arm, and machine movement comparisons are performed ex-situ using Keyence laser measurement sensors. Data acquisition is performed with a Raspberry Pi 4 microcomputer, controlled by custom, cross-platform Python code, and includes a touch screen human-computer interface. A device design adapted for a CNC mill is also presented, and the Haas TM-2 is used as a case study, which can be operated by technicians to check CNC machine accuracy, as needed, before a critical manufacturing process.
Parametric Stiffness in Large-Scale Wind-Turbine Blades and the Effects on Resonance and Speed Locking
Authors: Sapmaz Ayse , Feeny Brian F.
Published: November 2020

This paper is on parametric effect in large scale horizontal-axis wind-turbine blades and speed locking phenomenon for a simplified model of the in-plane blade-hub dynamics. The relative strength of the parametric stiffness is evaluated for actual and scaled-length blades. Fixed-position natural frequencies are found at different rotation angles to show the significance of the gravity’s parametric effect. The ratio of the parametric and elastic modal stiffness is then estimated for the scaled versions of the NREL’s blades for four models to present the relation between the blade size and the parametric effects. The parametric effect on blade tip placements are investigated for superharmonic resonances at orders two and three for blades of various lengths. An analysis of speed-locking is presented, and interpreted for the various blades.
A Novel Design of a Robotic Glove System for Patients With Brachial Plexus Injuries
Authors: Xu Wenda , Pradhan Sarthak , Guo Yunfei , Bravo Cesar , Ben-Tzvi Pinhas
Published: November 2020

This paper presents the design of an exoskeleton glove system for people who suffer from the brachial plexus injuries in an effort to restore their lost grasping functionality. The robotic system consists of an embedded controller and a portable glove system. The glove system consists of Linear Series Elastic Actuators (SEA), Rotary SEA and optimized finger linkages to provide motion to each finger and a coupled motion of the hand and the wrist. The design is based on various functionality requirements such as being lightweight and portable for activities of daily living, especially for grasping. The contact force at each fingertip and bending angle of each finger are measured for future implementation of intelligent control algorithms for autonomous grasping. To provide better flexibility and comfort for the users, abduction and adduction of each finger as well as flexion of the thumb were taken into consideration in the design. The glove system is adjustable for different hand sizes. The micro-controllers and batteries are integrated on the forearm in order to provide a completely portable design solution.
Simulation Assisted Design of LCO Cathode Materials With High Performance Stability
Authors: Zheng Zhuoyuan , Bansal Parth , Wang Pingfeng , Li Yumeng
Published: November 2020

Lithium cobalt oxide (LCO) cathode is one of the most commonly used positive active materials for lithium ion batteries (LIBs). However, one reliability issue that limits its applications is the presence of moisture adsorbing on the LCO cathode surface, which may react with the electrolyte and lead to detrimental effects. In this study, a novel LCO thin film cathode is proposed, by adding a layer of high diffusion resistant (003) phase of LCO as the protective coating onto the diffusion favorable (110) phase, in order to simultaneously achieve good electrochemical performance and chemical stability of the LIB. A multi-physics-based finite element model is built to investigate the performance of the cathode and the influences of the design and operation variables, including the layout the two crystal phases, the fraction of each phase and the lithiation C rate. In addition, a Gaussian Process based surrogate model is developed, using the simulated results from the FE model as training data, to efficiently explore the design space of the cathode. It is found that, the 110//003 layout cathode could provide high capacity and good rate performance; meanwhile, the 003//110 design may lead to a largely reduced capacity, especially at high lithiation C rates.
Integrated and Configurable Voice Activation and Speaker Verification System for a Robotic Exoskeleton Glove
Authors: Guo Yunfei , Xu Wenda , Pradhan Sarthak , Bravo Cesar , Ben-Tzvi Pinhas
Published: November 2020

Efficient human-machine interface (HMI) for exoskeletons remains an active research topic, where sample methods have been proposed including using computer vision, EEG (electroencephalogram), and voice recognition. However, some of these methods lack sufficient accuracy, security, and portability. This paper proposes a HMI referred as integrated trigger-word configurable voice activation and speaker verification system (CVASV). The CVASV system is designed for embedded systems with limited computing power that can be applied to any exoskeleton platform. The CVASV system consists of two main sections, including an API based voice activation section and a deep learning based text-independent voice verification section. These two sections are combined into a system that allows the user to configure the activation trigger-word and verify the user’s command in real-time.
Energy Savings From Electrification of Cooling System
Authors: Ethelbert N. Ezemobi , Ruzimov Sanjarbek , Angelo Bonfitto , Tonoli Andrea , Nicola Amati
Published: November 2020

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.
Stochastic Kriging for Crashworthiness Optimization Accounting for Simulation Noise
Authors: Ahmadisoleymani Seyed Saeed , Missoum Samy
Published: November 2020

Vehicle crash simulations are notoriously costly and noisy. When performing crashworthiness optimization, it is therefore important to include available information to quantify the noise in the optimization. For this purpose, a stochastic kriging can be used to account for the uncertainty due to the simulation noise. It is done through the addition of a non-stationary stochastic process to the deterministic kriging formulation. This stochastic kriging, which can also be used to include the effect of random non-controllable parameters, can then be used for surrogate-based optimization. In this work, a stochastic kriging-based optimization algorithm is proposed with an infill criterion referred to as the Augmented Expected Improvement, which, unlike its deterministic counterpart the Expect Improvement, accounts for the presence of irreducible aleatory variance due to noise. One of the key novelty of the proposed algorithm stems from the approximation of the aleatory variance and its update during the optimization. The proposed approach is applied to the optimization of two problems including an analytical function and a crashwor-thiness problem where the components of an occupant restraint system of a vehicle are optimized.
Additive Manufacturing Adaptiveness Analysis Using Fuzzy Bayesian Network
Authors: Jing Liting , Ma Junfeng
Published: November 2020

Additive manufacturing (AM) is a revolutionary manufacturing technology that can produce products in a layer by layer manner. Because of its significant merits in complex geometry and fast fabrication, AM has received worldwide attentions from both industries and academia. Although extensive studies have been conducted on the aspects of process design, prototyping, quality control and reliability, the study of adopting AM in the application is still not fully investigated, which motives this study. In order to close this gap, this study proposes a fuzzy Bayesian Network based approach to discover the applicability of AM. Twelve features of AM applicability obtained from existing literature have been considered in the analysis; fuzzy linguistic description was used to capture the users’ perception; fuzzy Bayesian Network based causation model was developed to study the AM’s adaptiveness. The jet engine blade case study was applied to demonstrate the applicability of the proposed approach. The results showed that fuzzy Bayesian Network based causation approach is able to provide the robust and reliable results of applicability analysis and could also be extended to other risk assessment related design decision making process.
A Neighborhood-Based Neural Network for Melt Pool Prediction and Control
Authors: Zhang Yaqi , Shapiro Vadim , Witherell Paul
Published: November 2020

One of the most prevalent additive manufacturing processes, the powder bed fusion process, is driven by a moving heat source that melts metals to build a part. This moving heat source, and the subsequent formation and moving of a melt pool, plays an important role in determining both the geometric and mechanical properties of the printed components. The ability to control the melt pool during the build process is a sought after mechanism for improving quality control and optimizing manufacturing parameters. For this reason, efficient models that can predict melt pool size based on the process input (i.e., laser power, scan speed, spot size and scan path) offer a path to improved process control.

Towards improved process control, a data-driven melt pool prediction model is built with a neighborhood-based neural network and trained using experimental data from the National Institute of Standards and Technology (NIST). The model considers the influence of both manufacturing parameters and laser scan paths. The scan path information is encoded using two novel neighborhood features of the neural network through locality. The neural network is used to generate a surrogate model, and we demonstrate how the performance of the resulting surrogate model can be further improved by using several ensemble methods. We then demonstrate how the trained surrogate model can be used as a forward solver for developing novel laser power design algorithms. The resulting laser power plan is designed to keep melt pool size as constant as possible for any given scan pattern. The algorithm is implemented and validated with numerical experiments.
A Jupyter Notebook Environment for Multibody Dynamics
Authors: Gaut Aaron , Cameron Jonathan , Jain Abhinandan
Published: November 2020

DARTS is a rigid/flexible multibody dynamics toolkit for the modeling and simulation of aerospace and robotic vehicles for engineering applications. In this paper we describe an on-line, browser-based environment using Jupyter notebooks to support training needs for the DARTS software. The suite of curated tutorial notebooks is organized into different topic areas, and into multiple themes within each topic area. The notebooks within a theme use a progression of examples for users to expand their understanding of the software. The topic areas include one on the DARTS multibody dynamics software and another one on the theory underlying the multibody dynamics formulation. We also describe a number of Jupyter extensions that were used — and some developed in house — to enhance the notebook interface for use with the dynamics simulation software. One significant extension we implemented allows the embedding of live 3D visualizations within simulation notebooks.
Social Life Cycle Assessment of Computer-Aided Design Tools
Authors: Walters Justin , Mirkouei Amin
Published: November 2020

Social life cycle assessment (SLCA) is a newly developed concept that is used to assess the potential positive and negative social impacts of products and services. However, the existing approaches have not focused on improving social aspects in the execution of computer-aided design (CAD) software. The Idaho National Laboratory’s Materials and Fuels Complex is currently using Creo Parametric CAD software to design all experimental equipment. The purpose of this study is to conduct a socio-environmental life cycle assessment on the existing design procedures and present the findings and possible solutions to upper management. A comparison was performed to highlight the differences between the procedures. To determine the social effects, the Social Hotspots Database in OpenLCA was used in connection with a low, medium, high, and very high scale, which was used to quantify specific social categories. The social categories developed for this study include communication, rework time, time spent investigating non-normal methods of task completion, excessive working time, and social impacts of electricity usage. The environmental aspects were calculated by gathering data on carbon dioxide emissions per computer, utilizing the Creo software. The results produced through the calculations show that in all three areas of interest, the proposed approach decreased time and carbon dioxide emissions as well as an increase in employee satisfaction. Due to the virtually nonexistent SLCA studies in relation to the use of CAD software, it is anticipated that this study will provide a starting point for a more in-depth analysis of engineering departments.
Fostering Function-Sharing Using Bioinspired Product Architecture
Authors: Bhasin Devesh , McAdams Daniel A.
Published: November 2020

In this work, we deduce principles of bioinspired product architectures to leverage biological function-sharing in engineering design. Function-sharing allows multiple functions to be performed by a single structure and can lead to improvements in cost, weight and other performance characteristics. Billions of years of evolution has led to the emergence of function-sharing adaptations in biological systems. However, the current practice of bioinspired function-sharing is largely limited to the solution-driven mimicry of biological structures. In order to effectively leverage biological function-sharing in engineering design, we model and analyze the product architectures of five generalized case studies from the animal kingdom. Further, we create a categorization framework to explore patterns in the function-sharing scenarios associated with biological product architectures. Our results indicate the existence of four types of modules in the biological systems from the animal kingdom. We use the classification framework to deduce four guidelines for the bioinspiration of product architectures. The deduced guidelines can allow engineers to identify and implement novel function-sharing scenarios in early stages of product design. The application of the guidelines has been demonstrated by using a case study.
2:1 MUX and OR Logic Functions Using Triple Partial Electrodes: Toward Cascadable MEMS Logic Devices
Authors: Tella Sherif A. , Younis Mohammad I.
Published: November 2020

In the era of IoT and smarter sensors and actuators, MEMS resonators are actively being explored for ultra-low-power computing devices due to their simplicity and potential toward energy-efficient computing machines. However, the realization of complex logic functions through the cascadability of MEMS resonator logic devices has introduced new challenges that require both the logic input and logic output signals to be based on AC signals at the same frequency. Toward these challenges, this study demonstrates 2:1 MUX function and OR gate with improved energy efficiency based on activation and deactivation of the third vibrational mode of an arch microbeam resonator with a pair of three partial electrodes.
Best Fits and Dark Horses: Can Design Teams Tell the Difference?
Authors: Henderson Daniel , Booth Thomas , Jablokow Kathryn , Sonalkar Neeraj
Published: November 2020

Design teams are often asked to produce solutions of a certain type in response to design challenges. Depending on the circumstances, they may be tasked with generating a solution that clearly follows the given specifications and constraints of a problem (i.e., a Best Fit solution), or they may be encouraged to provide a higher risk solution that challenges those constraints, but offers other potential rewards (i.e., a Dark Horse solution). In the current research, we investigate: what happens when design teams are asked to generate solutions of both types at the same time? How does this request for dual and conflicting modes of thinking impact a team’s design solutions? In addition, as concept generation proceeds, are design teams able to discern which solution fits best in each category? Rarely, in design research, do we prompt design teams for “normal” designs or ask them to think about both types of solutions (boundary preserving and boundary challenging) at the same time. This leaves us with the additional question: can design teams tell the difference between Best Fit solutions and Dark Horse solutions?

In this paper, we present the results of an exploratory study with 17 design teams from five different organizations. Each team was asked to generate both a Best Fit solution and a Dark Horse solution in response to the same design prompt. We analyzed these solutions using rubrics based on familiar design metrics (feasibility, usefulness, and novelty) to investigate their characteristics. Our assumption was that teams’ Dark Horse solutions would be more novel, less feasible, but equally useful when compared with their Best Fit solutions. Our analysis revealed statistically significant results showing that teams generally produced Best Fit solutions that were more useful (met client needs) than Dark Horse solutions, and Dark Horse solutions that were more novel than Best Fit solutions. When looking at each team individually, however, we found that Dark Horse concepts were not always more novel than Best Fit concepts for every team, despite the general trend in that direction. Some teams created equally novel Best Fit and Dark Horse solutions, and a few teams generated Best Fit solutions that were more novel than their Dark Horse solutions. In terms of feasibility, Best Fit and Dark Horse solutions did not show significant differences. These findings have implications for both design educators and design practitioners as they frame design prompts and tasks for their teams of interest.