bluuebunny/crossref_metadata_2025_split huggingface dataset:
Can Process Metrics Predict Product Success?: A Pilot Study of Student Design Teams
Authors: Bracken Jennifer , Glavin Francis Xavier , Henderson Daniel , Jablokow Kathryn , Sonalkar Neeraj , Erdman Andrew Michael 
Published: November 2019

Abstract
Engineering projects typically revolve around producing a deliverable. That deliverable goes to a customer, who either deems it acceptable or in need of further work. The engineering analysis and components of whatever system is to be produced are the subject of much scrutiny. However, the human process composed of team interactions that lead up to creating that final product is frequently treated as a “black box” that simply produces an output. In order to identify what factors in that process are key to a successful product, this work seeks to identify what successful engineering design teams do differently than less successful teams. As part of our larger research project, metrics for measuring team performance during the process of design have also been created. In this paper, we use three of those metrics in a case study of 5 senior-level student design teams. These data are employed in conjunction with feedback from the instructor, acting as their customer, to identify which behaviors had strong links with more successful team results. We also investigate whether any of the behaviors exhibited by the teams correspond to worse results, in order to identify behaviors with the potential to be used to predict poorer performance in advance. This analysis is completed using data collected via a mid-term survey and an end-of-project survey (both completed by the team members), in addition to video and audio meeting data, and data collected from both midterm and final presentations. We present these results as an avenue to move us towards enabling engineers to choose to engage knowingly in behaviors that correlate with better project results.
Design and Optimization of an Origami-Inspired Jumping Mechanism With Nonlinear Stiffness Properties
Authors: Sadeghi Sahand , Betsill Blake D. , Li Suyi 
Published: November 2019

Abstract
This research investigates the feasibility of utilizing origami folding techniques to create an optimized jumping mechanism. As a theoretical example, we study the dynamic characteristics of a jumping mechanism consisting of two masses connected by a Tachi-Miura Polyhedron (TMP) origami structure with nonlinear stiffness characteristics. We show how the desired “strain-softening” effects of the TMP structure can lead to design of jumping mechanisms with optimized performance. The kinematics of TMP origami structure is reviewed and a modified model of its reaction-force displacement curve is presented. We derive the equations of motion of the jumping process and use their numerical solutions extensively for design optimization. Through this process we are able to obtain optimum geometrical configurations for two different objectives: The maximum time spent in the air and the maximum clearance off the ground. Results of this study can lead to emergence of a new generation of more efficient jumping mechanisms with optimized performance in the future.
MPC-Based Control of Autonomous Vehicles With Localized Path Planning for Obstacle Avoidance Under Uncertainties
Authors: Devaragudi Sai Rajeev , Chen Bo 
Published: November 2019

Abstract
This paper presents a Model Predictive Control (MPC) approach for longitudinal and lateral control of autonomous vehicles with a real-time local path planning algorithm. A heuristic graph search method (A* algorithm) combined with piecewise Bezier curve generation is implemented for obstacle avoidance in autonomous driving applications. Constant time headway control is implemented for a longitudinal motion to track lead vehicles and maintain a constant time gap. MPC is used to control the steering angle and the tractive force of the autonomous vehicle. Furthermore, a new method of developing Advanced Driver Assistance Systems (ADAS) algorithms and vehicle controllers using Model-In-the-Loop (MIL) testing is explored with the use of PreScan®. With PreScan®, various traffic scenarios are modeled and the sensor data are simulated by using physics-based sensor models, which are fed to the controller for data processing and motion planning. Obstacle detection and collision avoidance are demonstrated using the presented MPC controller.
Determination of the Number of Input Data Through Bi-Objective Confidence-Based Design Optimization
Authors: Jung Yongsu , Cho Hyunkyoo , Duan Zunyi , Lee Ikjin 
Published: November 2019

Abstract
The confidence of reliability indicates that reliability has randomness induced by any epistemic uncertainties, and these uncertainties can be reduced and manipulated by additional knowledge. In this paper, the uncertainty of input statistical models is mainly treated in the context of confidence-based design optimization (CBDO). Thus, the objective of this paper is to determine the optimal number of data for reliability-based design optimization (RBDO) under input model uncertainty. The uncertainty of input statistical models due to insufficient data is frequent in practical applications since collecting and testing samples of random variables requires engineering efforts. There are two ways to increase the confidence of reliability to be satisfied, which are shifting design vector and supplementing input data. The purpose of this research is to find balanced optimum accounting for a trade-off between two operations since both operations lead to the growth of overall cost. Therefore, it is necessary to optimally distribute the resources to two costs which are denoted as the operating cost of design vector and the development cost of acquiring new data. In this study, two types of costs are integrated as a bi-objective function, satisfying the probabilistic constraint for the confidence of reliability. The number of data is regarded as design variable to be optimized, and stochastic sensitivity analysis of reliability with respect to the number of data is developed. The proposed bi-objective CBDO can determine the optimal number of input data based on the current dataset. Then, the designers decide the additional number of tests for collecting input data according to the optimum of bi-objective CBDO to minimize the overall cost.
On the Analysis of a Compromised Additive Manufacturing System Using Spatio-Temporal Decomposition
Authors: Prakash Sakthi Kumar Arul , Mahan Tobias , Williams Glen , McComb Christopher , Menold Jessica , Tucker Conrad S. 
Published: November 2019

Abstract
3D printing systems have expanded the access to low cost, rapid methods for attaining physical prototypes or products. However, a cyber attack, system error, or operator error on a 3D printing system may result in catastrophic situations, ranging from complete product failure, to small types of defects which weaken the structural integrity of the product, making it unreliable for its intended use. Such defects can be introduced early-on via solid models or through G-codes for printer movements at a later stage. Previous works have studied the use of image classifiers to predict defects in real-time as a print is in progress and also by studying the printed entity once the print is complete. However, a major restriction in the functionality of these methods is the availability of a dataset capturing diverse attacks on printed entities or the printing process. This paper introduces a visual inspection technique that analyzes the amplitude and phase variations of the print head platform arising through induced system manipulations. The method uses an image sequence of a 3D printing process captured via an off the shelf camera to perform an offline multi-scale, multi-orientation decomposition to amplify imperceptible system movements attributable to a change in system parameters. The authors hypothesize that a change in the amplitude envelope and instantaneous phase response as a result of a change in the end effector translational instructions, to be correlated with an AM system compromise. A case study is presented that tests the hypothesis and provides statistical validity in support of the method. The method has the potential to enhance the robustness of cyber-physical systems such as 3D printers that rely on secure, high quality hardware and software to perform optimally.
Ride Comfort Enhancement of MR-Damped Vehicle Suspension System Based on Fractional Order Fuzzy PI+D Controller
Authors: Bashir Ahmed , Rui Xiaoting , Shehzad Adeel 
Published: November 2019

Abstract
In this paper, a fractional order fuzzy proportional-integral plus differential (FOFPI+D) controller is presented for nonlinear vehicle semi-active suspension system (SAS). The control goal is to meliorate the ride quality level by minimizing the root mean square of vehicle body vertical acceleration (RMSVBVA) and maintaining suspension travel. The FOFPI+D controller is realized using non-integer differentiator operator in fuzzy proportional integral (FPI) controller plus the derivative (D) action with additional fractional differentiator. A dynamical model of four degrees–of–freedom vehicle suspension system incorporating magnetorheological dampers (MRD’s) is derived and simulated using Matlab/Simulink software. The performance of the semi-active suspension system using FOFPI+D controller is compared to MR-passive suspension system. The simulation results prove that semi-active suspension system controlled using FOFPI+D outperform and offer better comfort ride under road profiles such as random and bump.
Parallel Co-Simulation Approach With Macro-Step Size and Order Control Algorithm
Authors: Kraft Jan , Meyer Tobias , Schweizer Bernhard 
Published: November 2019

Abstract
This contribution deals with the parallelization of multibody systems by making use of co-simulation techniques. The overall model is split into a user-defined number of subsystems, which are coupled and computed by means of a co-simulation approach. The co-simulation methods considered here are weak coupling approaches, which implies that each subsystem is solved independently from the other subsystems within a macro-time step. Information (i.e. coupling variables) is only exchanged between the subsystems at certain communication-time points (macro-time points). Within each macro-time step, the unknown coupling variables are approximated by extrapolation polynomials. The separate integration of the subsystems is the crucial point for a parallelized computation. A main drawback of many co-simulation implementations is that they are based on a constant macro-step size. Using an equidistant communication-time grid may in many practical applications be not very efficient with respect to computation time, especially in connection with highly nonlinear models or in context with models with strongly varying quantities. Here, a co-simulation approach is presented which incorporates a macro-step size and order control algorithm. Numerical examples show the benefit of this implementation and the significant reduction in computation time compared to an implementation with an equidistant communication-time grid.
Orbit Jumps of Monostable Energy Harvesters by a Bidirectional Energy Conversion Circuit
Authors: Wang Jiahua , Zhao Bao , Liang Junrui , Liao Wei-Hsin 
Published: November 2019

Abstract
Nonlinear energy harvesters have been widely studied in the last decade. Their broad bandwidth and relatively high power output contribute to energy harvesting applications. However, the coexisting multiple orbits brought by the nonlinearity weaken the performance of nonlinear energy harvesters. This paper proposes to achieve orbit jumps of monostable energy harvesters by a bidirectional energy conversion circuit. Changing the switch control sequence in the bidirectional energy conversion circuit facilitates it with both the energy harvesting and vibration exciting functions. Thus, a nonlinear energy harvester in connection with the circuit can harness ambient energy as well as excite itself, through energy harvesting and vibration exciting modes separately. Based on the concept of vibration exciting, the energy saved in the storage is used to stimulate the piezoelectric transducer for a larger vibration amplitude, which enables orbit jumps. The working mechanism of the circuit is introduced. Experimental setup of a monostable energy harvester has been developed to validate the proposed method. The monostable system can be stimulated to high-energy orbit from a small vibration amplitude by the vibration exciting mode of the circuit. It is also revealed that the method can achieve orbit jumps in a wide frequency range within the hysteresis area. Evaluations on energy consumption and energy gain show that the sacrificed energy can be quickly recovered. A novel approach for orbit jumps of monostable energy harvesters is performed so as to open new opportunities for monostable energy harvesters.
Large-Stroke Constant-Force Mechanisms Utilizing Second Bending Mode of Flexible Beams: Evaluation Metrics and Design Approach
Authors: Ma Fulei , Chen Guimin , Wang Haitian 
Published: November 2019

Abstract
Compliant constant-force mechanisms (CCFMs), which provide a near constant force output over a range of displacement, can benefit many applications. This work proposes a novel large-stroke CCFM (abbreviated as B2CCFM) that utilizes the second bending mode of flexible beams. Two general nondimensionalized metrics, one describing the variation of output force and the other describing the operational displacement, are proposed to effectively characterize the performances of various CCFMs. Based on the general metrics, design formulas that can help designers quickly find suitable B2CCFM design for a specific application are obtained. A kinetostatic model for B2CCFM is also provided based on the chained beam constrain model (CBCM) to verify B2CCFM designs. An example accompanied with a prototype is presented to verify this novel CCFM and the effectiveness of the design formulas. The experimental results show that the B2CCFM example outputs a constant-force in a range as large as 45% of the beam length with variation less than 4.7%. The nondimensionalized metrics were demonstrated in comparison of several CCFMs, and the comparison results show the superior performances of B2CCFMs.
Computational Fluid Dynamics Simulation of Flow of Exhaled Particles From Powered-Air Purifying Respirators
Authors: Xu Susan S. , Lei Zhipeng , Zhuang Ziqing , Bergman Michael 
Published: November 2019

Abstract
In surgical settings, infectious particulate wound contamination is a recognized cause of post-operative infections. Powered air-purifying respirators (PAPRs) are widely used by healthcare workers personal protection against infectious aerosols. Healthcare infection preventionists have expressed concern about the possibility that infectious particles expelled from PAPR exhalation channels could lead to healthcare associated infections, especially in operative settings where sterile procedural technique is emphasized.
This study used computational fluid dynamics (CFD) modeling to simulate and visualize the distribution of particles exhaled by the PAPR wearer. In CFD simulations, the outward release of the exhaled particles, i.e., ratio of exhaled particle concentration outside the PAPR to that of inside the PAPR, was determined. This study also evaluated the effect of particle sizes, supplied air flow rates, and breathing work rates on outward leakage.
This simulation study for the headform and loose-fitting PAPR system included the following four main steps: (1) preprocessing (establishing a geometrical model of a headform wearing a loose-fitting PAPR by capturing a 3D image), (2) defining a mathematical model for the headform and PAPR system, and (3) running a total 24 simulations with four particle sizes, three breathing workloads and two supplied-air flow rates (4 × 3 × 2 = 24) applied on the digital model of the headform and PAPR system, and (4) post-processing the simulation results to visually display the distribution of exhaled particles inside the PAPR and determine the particle concentration of outside the PAPR compared with the concentration inside. We assume that there was no ambient particle, and only exhaled particles existed. The results showed that the ratio of the exhaled particle concentration outside to inside the PAPR were influenced by exhaled particle sizes, breathing workloads, and supplied-air flow rates. We found that outward concentration leakage from PAPR wearers was approximately 9% with a particle size of 0.1 and 1 μm at the light breathing and 205 L/min supplied-air flow rates, which is similar to the respiratory physiology of a health care worker in operative settings, The range of the ratio of exhaled particle concentration leaking outside the PAPR to the exhaled particle concentration inside the PAPR is from 7.6% to 49. We found that supplied air flow rates and work rates have significant impact on outward leakage, the outward concentration leakage increased as particle size decreased, breathing workload increased, and supplied-air flow rate decreased. The results of our simulation study should help provide a foundation for future clinical studies.
On External Disturbance Rejection of Linear ADRC
Authors: Jin Huiyu , Chen Yang , Lan Weiyao 
Published: November 2019

Abstract
Active disturbance rejection control (ADRC) is a quickly developing practical control technology while its ability to reject external disturbance is necessary to investigate deeply. Focusing on the simple case that the plant is an exactly known second order plant, this paper investigates the external disturbance rejection of linear ADRC. It reveals a separation diagram, in which the external disturbance goes into the output via a bandpass filter. That is the reason why linear ADRC can reject both low-frequency and high-frequency external disturbance.
A Design Modification System for Additive Manufacturing: Towards Feasible Geometry Development
Authors: Ghiasian Seyedeh Elaheh , Jaiswal Prakhar , Rai Rahul , Lewis Kemper 
Published: November 2019

Abstract
The substantial role of additive manufacturing (AM) in fabricating unique geometries is undeniable in the domain of design and manufacturing. However, the successful implementation of AM technologies requires a consistency between the geometric specifications of a component and AM manufacturability capabilities and constraints. Otherwise, AM could result in failed prints and a wasteful use of resources. The goal of this research is to provide geometrically feasible designs for AM processes by rectifying the potentially infeasible geometries. To this end, a novel design modification system is presented that addresses the problematic areas of an AM-infeasible component using appropriate redesign solutions. This system also includes a geometric assessment algorithm which identifies the potential problematic part features using a comprehensive evaluation. Based on the obtained manufacturability feedback, the detected problematic features are then modified through a holistic design modification system. The functionality of the presented system is illustrated using a case study, and the effectiveness of the implemented modification approaches is also demonstrated through an experiment.
Experimental Study for Free Rolling of Tires on Ice
Authors: Mousavi Hoda , Shenvi Mohit Nitin , Sandu Corina 
Published: November 2019

Abstract
Considering that tires play a vital role not only in the performance but also in the safety of a vehicle, studying the tire-road interaction has always been a matter of interest, specifically when the tire is travelling on icy roads. However, most of the existing studies focused on tire performance on ice for traction or braking conditions [1,2] and not many studies have been devoted to the free rolling of a tire. Considering the tire in its free rolling condition is a necessary step in assessing the friction force at the tire-ice interface, which can be used next to predict the torque to be applied on the tire in order to create a zero slip condition in the tire-ice contact patch. The zero slip condition is always difficult to obtain, as it needs a very accurate assessment of the effective rolling radius of the tire.
This study is a part of a more comprehensive study to investigate the effects of different tire parameters, such as rubber compounds properties on tire performance on ice.
For the work presented in this paper the main objective is benchmarking the results from existing methods for obtaining the effective rolling radius of the tire and the equivalent dynamic friction coefficient of the tire on ice. The investigation approach was to experimentally study a tire under free rolling on ice. A set of experiments were thus designed and conducted for the Standard Reference Test Tire (SRTT) on a layer of ice in the Terramechanics Rig in the Terramechanics, Multibody and Vehicle Systems laboratory (TMVS) Laboratory at Virginia Tech under different applied normal load and various inflation pressure conditions. The data collected from the tests performed was used to obtain and compare the result for effective rolling radius of the tire and equivalent friction coefficient. The data collected on the Terramechanics Rig by the 6-axis wheel load measurement system P650 by Kistler was processed to eliminate the noise of the raw data using zero-phase filtering techniques in MATLAB.
Other parameters measured during the tests conducted were the tire loaded radius, the width of contact patch, and the temperature distribution in the tire-ice contact patch.
Parametric Optimization for Structural Design Problems
Authors: Ravichandran Krupakaran , Masoudi Nafiseh , Fadel Georges M. , Wiecek Margaret M. 
Published: November 2019

Abstract
Parametric Optimization is used to solve problems that have certain design variables as implicit functions of some independent input parameters. The optimal solutions and optimal objective function values are provided as functions of the input parameters for the entire parameter space of interest. Since exact solutions are available merely for parametric optimization problems that are linear or convex-quadratic, general non-convex non-linear problems require approximations.
In the present work, we apply three parametric optimization algorithms to solve a case study of a benchmark structural design problem. The algorithms first approximate the nonlinear constraint(s) and then solve the optimization problem. The accuracy of their results and their computational performance are then compared to identify a suitable algorithm for structural design applications. Using the identified method, sizing optimization of a truss structure for varying load conditions such as a varying load direction is considered and solved as a parametric optimization problem to evaluate the performance of the identified algorithm. The results are also compared with non-parametric optimization to assess the accuracy of the solution and computational performance of the two methods.