bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Reliability Model Validation Method for Mitigating the Effects of Measurement Uncertainty
Authors: Sadoughi Mohammadkazem , Li Meng , Beck Joseph , Hu Chao 
Published: November 2019

Abstract
With the increasing role of numerical modeling in engineering design and development processes, improved techniques are needed for validating computational results against experimental measurements. Most existing validation methods suffer from two main limitations: (i) they are often highly sensitive to the experimental measurement uncertainty, and (ii) extending these methods for reliability model validation requires large quantities of failure data that may be very time-consuming or costly to obtain. In order to overcome the aforementioned limitations, this study proposes an indirect reliability model validation method. First, a new procedure for computing a validation metric is developed based on Richardson extrapolation (RE) to reduce the sensitivity of the metric to the experimental measurement uncertainty. Second, a new validation metric is defined based on the limit state function (LSF) approximation to extend numerical model validation to reliability model validation. The proposed method is illustrated by validating a reliability estimation model for a cantilever beam under a vertical load.
Self-Adapting Intelligent Battery Thermal Management System via Artificial Neural Network Based Model Predictive Control
Authors: Liu Yuanzhi , Zhang Jie 
Published: November 2019

Abstract
This paper develops a self-adaptive control strategy for a newly-proposed J-type air-based battery thermal management system (BTMS) for electric vehicles (EVs). The structure of the J-type BTMS is first optimized through surrogate-based optimization in conjunction with computational fluid dynamics (CFD) simulations, with the aim of minimizing temperature rise and maximizing temperature uniformity. Based on the optimized J-type BTMS, an artificial neural network (ANN)-based model predictive control (MPC) strategy is set up to perform real-time control of mass flow rate and BTMS mode switch among J-, Z-, and U-mode. The ANN-based MCP strategy is tested with the Urban Dynamometer Driving Schedule (UDDS) driving cycle. With a genetic algorithm optimizer, the control system is able to optimize the mass flow rate by considering several steps ahead. The results show that the ANN-based MPC strategy is able to constrain the battery temperature difference within a narrow range, and to satisfy light-duty daily operations like the UDDS driving cycle for EVs.
Data-Centric Mixed-Variable Bayesian Optimization for Materials Design
Authors: Iyer Akshay , Zhang Yichi , Prasad Aditya , Tao Siyu , Wang Yixing , Schadler Linda , Brinson L. Catherine , Chen Wei 
Published: November 2019

Abstract
Materials design can be cast as an optimization problem with the goal of achieving desired properties, by varying material composition, microstructure morphology, and processing conditions. Existence of both qualitative and quantitative material design variables leads to disjointed regions in property space, making the search for optimal design challenging. Limited availability of experimental data and the high cost of simulations magnify the challenge. This situation calls for design methodologies that can extract useful information from existing data and guide the search for optimal designs efficiently. To this end, we present a data-centric, mixed-variable Bayesian Optimization framework that integrates data from literature, experiments, and simulations for knowledge discovery and computational materials design. Our framework pivots around the Latent Variable Gaussian Process (LVGP), a novel Gaussian Process technique which projects qualitative variables on a continuous latent space for covariance formulation, as the surrogate model to quantify “lack of data” uncertainty. Expected improvement, an acquisition criterion that balances exploration and exploitation, helps navigate a complex, nonlinear design space to locate the optimum design. The proposed framework is tested through a case study which seeks to concurrently identify the optimal composition and morphology for insulating polymer nanocomposites. We also present an extension of mixed-variable Bayesian Optimization for multiple objectives to identify the Pareto Frontier within tens of iterations. These findings project Bayesian Optimization as a powerful tool for design of engineered material systems.
Does Visual Fixation Affect Idea Fixation?
Authors: Kwon E. , Ryan J. D. , Bazylak A. , Shu L. H. 
Published: November 2019

Abstract
Divergent thinking, an aspect of creativity, is often studied by measuring performance on the Alternative Uses Test (AUT). There is however a gap in creativity research concerning how visual stimuli on the AUT are most effectively perceived. Research in memory and attention have used eye-tracking studies to reveal insights into how people think and perceive visual stimuli. Thus, the current work uses eye tracking to study how eye movements are related to creativity.
Participants orally listed alternative uses for twelve objects, each visually represented for two minutes in four different views. Using eye tracking, we specifically explored where and for how long people fixate their eyes at objects during the AUT. Eye movements before and while naming alternative uses are studied.
Results revealed that naming new instances and categories of alternative uses correlates more strongly with visual fixation towards multiple views than towards a single view of the object. Alternative uses in new, previously unnamed categories are also more likely named following increased visual fixation towards blank space. These and other findings reveal the cognitive-thinking styles and eye-movement behaviors associated with finding new ideas. Such findings may be applied to reduce fixation to existing ideas during design.
The Study of Magnetorheological Elastomer Based on Natural Rubber (NR)/Polybutadiene Rubber (BR) Hybrid Matrix: Experimental and Numerical Simulation
Authors: Song Xincheng , Wang Wenju , Yang Fufeng , Wang Guoping , Rui Xiaoting 
Published: November 2019

Abstract
Magnetorheological elastomer (MRE) is a new kind of smart materials whose mechanical properties can be controlled under external magnetic field and it is mainly consist of matrix materials and magnetic particles. In this work, the natural rubber (NR)/polybutadiene rubber (BR) hybrid matrix based MRE were prepared and the compatibility of NR and BR were studied. The hybrid matrix was prepared by physical mixing method. The characterization results showed that the BR had excellent compatibility with NR. The measurement result using rheological showed that the MR effect can be increased to 44.19% by adding of BR. The dynamic thermomechanical analysis showed that the hybrid matrix formed a homogeneous system when the ratio of BR and NR is 1/9 and 3/7. The particles was mixed with matrix using physical technology. The process of mixing was analyzed by numerical simulation. The simulation result showed that the increase of diameter of particles would increase the temperature and velocity of matrix in mixing. The particles was distributed evenly at enough mixing time and the mixing time was decreased with the diameter of particles.
Surrogate Models for the Efficient Estimation of Residual Fields Associated With Additively Manufactured Parts
Authors: Iliopoulos Athanasios P. , Steuben John C. , Apetre Nicole A. , Michopoulos John G. 
Published: November 2019

Abstract
Computing residual field distributions resulting from the thermomechanical history and interactions experienced by materials build by additive manufacturing (AM) methods, can be a very inefficient and computationally expensive process. To address this issue, the present work proposes and demonstrates a data-driven surrogate modeling approach that does not require solving the thermal-structural simulation of the AM process explicitly. Instead, it introduces the employment of various types of physics-agnostic surrogate models that are trained to data produced by full order physics-informed models. This enables to efficiently predict the resulting residual fields (e.g. distortions and residual stress) throughout the entire component. More specifically, two types of surrogate models for two different requirements scenarios are selected for the proposed work: Non-Uniform Rational B-Splines (NURBs) for a regularly sampled parametric space and k-simplex interpolants approach based on a two-step 3 + 1 dimensional interpolation that can operate on irregularly sampled spaces and grids. It is demonstrated that both methodologies can operate with low error and high performance (solution can be obtained within a few seconds on a desktop computer) on additively manufactured components of complex geometries.
Dynamic Modelling of a Planar Parallel Robot Manipulator Using the Discrete Time Transfer Matrix Method
Authors: Chu Mengqiu , Si Guoning , Zhang Xuping , Li Haijie 
Published: November 2019

Abstract
This paper aims to develop a new computationally efficient method for the dynamic modelling of a Planar Parallel Manipulator (PPM) based on the Discrete Time Transfer Matrix Method (DT-TMM). In this preliminary work, we use a 3-PRR PPM as a study case to demonstrate the major procedures and principles of employing the DT-TMM for the dynamic modelling of a PPM. The major focus of this work is to present the basic principles of the DT-TMM for the dynamic modelling of a PPM: decomposing the whole parallel manipulator to the individual components, establishing the dynamics of each component/link, linearizing the component/element dynamics to obtain the transfer matrix of each component/link, and assembling the component dynamics into the system dynamics of the PPM using the transfer matrices of all components/elements. To make the work more readable, the brief introduction of the inverse kinematics and the inverse dynamics is also included. The numerical simulations are conducted based on the 3-PRR PPM with rigid links in this preliminary research effort. The simulation results are compared with those from the model using the principle virtual work method and ADAMS software. The numerical simulation results and comparison demonstrate the effectiveness of the dynamic modelling method using DT-TMM for the PPM.
Geometric Complexity Estimation of Continuous Surfaces for Fitting Processes
Authors: Bahabadi Hossein Gohari , Barari Ahmad 
Published: November 2019

Abstract
The advances in manufacturing methods such as Additive manufacturing provide more flexibility in fabrication of complex geometries. Meanwhile, design tools such as aesthetic design and topology optimization algorithms have been implemented in industrial applications mostly due to the provided flexibility to manufacture freeform surfaces. Computational time and efficiency of the developed algorithms for design, manufacturing and inspection are heavily dependent on the geometric complexity of surfaces. In this paper a measure to estimate the geometric complexity is introduced based on the inherent property of a surface which is curvature. A quantitative value for the geometric complexity is defined through normalization and integration of the mean curvatures. Case studies of the implementation of the proposed measure of complexity verifies the ability of the method to predict the convergence of a surface fitting algorithm based on the geometric complexity of the input model.
Enabling Performance Qualification of Additively Manufactured Parts Under Multiaxial Loading Emulating In-Service Conditions
Authors: Michopoulos John G. , Iliopoulos Athanasios P. , Steuben John C. , Nguyen Trung , Phan Nam 
Published: November 2019

Abstract
The material imperfections generated by Additive Manufacturing (AM) processes across multiple scales can differ significantly from those arising by conventional manufacturing (CM) methods. To qualify these parts in a manner that accounts for all these imperfections without accounting explicitly for each of them, an outline of a rapid functional qualification methodology based on the concept of Performance Signature (PerSig) is presented first. The PerSigs are defined for both the prequalified CM parts and the AM-produced ones. Comparison measures are defined and enable the construction of differential PerSigs (dPerSig) in a manner that captures the differential performance of the AM part vs. the prequalified CM one. This approach is extended in this paper for the case of multiaxial loading conditions reflecting actual in-service loading. Application of the methodology is presented for a fitting bracket in the P-3C Orion aircraft platform and is based on synthetic data. The application of multiaxial loading emulating in-service loading conditions is proposed by the utilization of a custom-designed 6-DoF robotic testing system that will generate physical data.
Flexion and Extension Capable Motor Tendon Actuated Exosuit Glove With Open Palm
Authors: Patterson Georgia S. , Zell Tyler D. , Parker David E. , Grieve John , Watson Samuel C. , Vikas Vishesh 
Published: November 2019

Abstract
Patients suffering from medical conditions resulting in hand impairment experience difficulty in performing simple daily tasks, like getting dressed or using a pencil, resulting in a poorer quality of life. Rehabilitation attempts to help such individuals regain a sense of control and normalcy. In this context, recent advances in robotics have manifested in multiple designs of hand exoskeletons and exosuit gloves for assistance and rehabilitation. These designs are typically actuated using pneumatic, shape memory alloys and motor-tendon actuators. The proposed Motor Tendon Actuated Exosuit Glove (MTAEG) with an open palm is a soft material glove capable of both flexion and extension of all four fingers of the human hand. Its minimally invasive design maintains an open palm to facilitate haptic and tactile interaction with the environment. The MTAEG achieves flexion-extension motion with joint angles of 45° at the metacarpal joint which is 57% of the desired motion; 90° at the proximal interphalangeal joint which is 100% of the desired motion; and 50° at the distal interphalangeal joint which is 96% of the desired motion. The paper discusses the challenges in achieving the desired motion without the ability to directly model human tendons, and the inability to actuate joints individually.
Prediction of Nitrogen Concentration in Fuel Cells Using Data-Driven Modeling
Authors: Lin Tong , Hu Leiming , Litster Shawn , Kara Levent Burak 
Published: November 2019

Abstract
This paper presents a set of data-driven methods for predicting nitrogen concentration in proton exchange membrane fuel cells (PEMFCs). The nitrogen that accumulates in the anode channel is a critical factor giving rise to significant inefficiency in fuel cells. While periodically purging the gases in the anode channel is a common strategy to combat nitrogen accumulation, such open-loop strategies also create sub-optimal purging decisions. Instead, an accurate prediction of nitrogen concentration can help devise optimal purging strategies. However, model based approaches such as CFD simulations for nitrogen prediction are often unavailable for long-stack fuel cells due to the complexity of the chemical environment, or are inherently slow preventing them from being used for real-time nitrogen prediction on deployed fuel cells. As one step toward addressing this challenge, we explore a set of data-driven techniques for learning a regression model from the input parameters to the nitrogen build-up using a model-based fuel cell simulator as an offline data generator. This allows the trained machine learning system to make fast decisions about nitrogen concentration during deployment based on other parameters that can be obtained through sensors. We describe the various methods we explore, compare the outcomes, and provide future directions in utilizing machine learning for fuel cell physics modeling in general.
A Load Distribution Model for Double-Planet Planetary Gear Sets
Authors: Hu Yong , Talbot David , Kahraman Ahmet 
Published: November 2019

Abstract
In this paper, a load distribution model for a double-planet planetary gear set is developed by modifying an existing single-planet planetary gear set model [1] to account for an additional planet to planet gear mesh and their impact on phasing relationship among different sun-planet, planet-planet and planet-ring gear meshes. Similar to the single-planet planetary gear set model, the double-planet planetary gear set model accounts for effects of various component and system level variations such as supporting conditions, gear tooth modifications, manufacturing errors and kinematic configurations. The double-planet planetary gear load distribution model is derived for both rigid and flexible ring gear rim, while only parametric studies for a rigid ring gear rim is presented in this paper to demonstrate load distribution characteristics of double-planet planetary gear sets with different planet bearing stiffness and combination of various types of manufacturing errors, including pin hole position error and runout errors.
ContaBat: Designing and Prototyping an Attachable Sports Analytics Device That Provides Ball-Bat Impact Location for Performance Enhancement
Authors: Sarwar Umair , Cheema Kamran , Reid Tahira 
Published: November 2019

Abstract
This paper introduces ContaBat, an attachable device for a cricket bat that provides important performance metrics like ball-bat impact location, rotation due to impact, and impact force which could be used by players, coaches and fans to gain insights about their performance. Of the metrics mentioned, obtaining the impact location is the most novel feature and is the focus of this paper. Piezo-electric sensors placed at the back of the bat are used to record pressure measurements due to impact and utilize time difference of arrival techniques (TDOA) to pinpoint the impact location. For further product realization and development, it was essential to create a prototype. The prototype was instrumental in providing testing conditions that could simulate a real-world environment, identifying the limitations of the data acquisition methods used, evaluating the usability of the product, and helping future researchers envision the final form of the product and the impact it could create before commercializing the product. The results of the algorithms showed that the accuracy of the methods used resulted in the impact location being within 0.002 m of the hit spot, with the percentage error being higher towards the edges of the bat than the middle. This paper will discuss in detail the data acquisition and data analysis methods used to measure impact location on a cricket bat, the design considerations while creating the prototype, and the insights gained from prototyping.
Investigation on the Vibration of High Speed Cable Robot Manipulation due to Tension Around Drum
Authors: Jung Jinwoo , Piao Jinlong , Choi Eunpyo , Park Jong-Oh , Kim Chang-Sei 
Published: November 2019

Abstract
A cable-driven parallel robot (CDPR) consists of an end-effector, flexible lightweight cables, pulleys, winches, and a rigid base frame. As opposed to the rigid links of the traditional serial robots and parallel robots, the flexible lightweight cables allow the CDPR to easily achieve the high speed, heavy payload manipulation, and scalable workspace. Especially, the conventional high-speed pick and place operation can be realized due to the lightweight of its flexible cables. However, the flexibility of the lightweight cables can introduce a considerable vibration problem to the high speed cable robot system. One of main causes can be a cable tension difference between initial pre-tension and winding tension around a drum of the winch-motor actuator. To effectively investigate the effect of the tension around the drum on the high speed manipulation of the cable robot system, the spatial eight-cable high speed cable robot was reduced to the horizontal two cable system. The reduction of the number of the cable enables us to minimize the influences from the other factors such as the cable sagging and the geometric errors. A series of experiments was conducted using the combinations of the low and high initial pre-tensions and low and high tensions around the drum. The experimental results clearly show that the low tension around the drum can cause the vibration problem during the high speed pick and place operation. Also, it demonstrates that securing the drum tension similar to the initial pre-tension can effectively reduce the magnitude of the vibration.
A Real-Time Human Recognition and Tracking System With a Dual-Camera Setup
Authors: Wu Haoyu , Xiong Shaomin , Hirano Toshiki 
Published: November 2019

Abstract
Most surveillance camera systems are still controlled and monitored by humans. Smart surveillance camera systems are proposed to automatically understand the scene captured, identify the objects of interest, detect the abnormality, etc. However, most surveillance cameras are either wide-angle or pan-tilt-zoom (PTZ). When the cameras are in the wide-view mode, small objects can be hard to be recognized. On the other hand, when the cameras are zoomed-in to the object of interest, the global view cannot be covered and important events outside the zoomed view will be missed.
In this paper, we proposed a system composed of a wide-angle camera and a PTZ camera. The system is able to capture the wide-view and the zoomed-view at the same time, taking the advantages from both views. A real-time human detection and identification algorithm based on a neural network is developed. The system can efficiently and effectively recognize humans, distinguish different identities, and follow the person of interest using the PTZ camera. A multi-target multi-camera (MTMC) system is developed based on the original system. In the MTMC system, multiple cameras are placed at different places to look at different views. The same person shown in any camera can be recognized as the same person while different persons can be distinguished among all the cameras.
Optical Simulation for Designing a PPG Signal Patch
Authors: Pribadi Eka Fitrah , Chao Paul C.-P. 
Published: November 2019

Abstract
Designing an optical patch for photoplethysmogram (PPG) signal considers the optical simulation of the patch on the skin. The skin model for the optical simulation consists of epidermis, a dermis, a subcutaneous fat, and a blood vessel, while the model of the patch includes an organic light emitting diode (OLED) and an organic photodiode (OPD). Each model of skin layers is implemented using bulk and scattering of the optical parameter. In order to model the AC/DC of PPG signal, the blood vessel of the optical model uses the diameter of 2 mm and 2.2 mm. The simulation conducted by sweeping the distance of OPD model apart of OLED in knowing the optimal distance between OLED and OPD. The simulation of the different shape of patches is conducted to know the optimum shape of the patch. The 8-shaped patch has the optimum value of the AC/DC signal which is 0.0045.