bluuebunny/crossref_metadata_2025 huggingface dataset:
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

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

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

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

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

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

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

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

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

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

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

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.
Backward Mechanical-Electric Coupling Effect of a Frequency-Up-Conversion Piezoelectric Energy Harvester
Authors: Wang Fengxia , Onsorynezhad Saeed
Published: November 2019

This paper developed an analytical model for a frequency-up-conversion piezoelectric energy harvester (PEH) composed of a piezoelectric bimorph and a stopper as shown in Fig.1. The whole system was subjected to a harmonic excitation. A bimodal approach was adopted to animate the beam stopper reaction. When the tip of the bimorph vibrates in free space or impacts with the stopper, a cantilever beam function was adopted. On the other hand, if the tip of the bimorph sticks with the stopper, a clamped-pinned beam function was applied to model the piezoelectric bimorph. To exam the effect of backward mechanical-electric coupling on power output, the dynamics and output energies are compared for two cases: 1) neglecting the backward mechanical-electric coupling effect in the model; 2) including the backward mechanical-electric coupling effect in the model. To obtain maximum output energy, the steady-state analytical solutions were studied to obtain the optimum gap between the piezoelectric beam and the stopper. From the results, we found that if the beam impacts and/or sticks with the stopper, the PEH model without the backward mechanical-electric coupling will exaggerate the output energy.
Proposing a Design for Tangibility Framework: A Digital Payments Case Study
Authors: Li Yanling , Baxter Weston L.
Published: November 2019

This research proposes a framework for designing tangibility into abstract interactions such as managing financial resources, health concerns and environmental impacts. A case study of the tangibility within cashless payments is presented, which adopted this framework to design, fabricate and test a tangible interaction in digital transactions. Digital transactions can significantly influence a consumer’s spending habits and decisions, commonly leading to increased expenditure compared to cash transactions. Several psychological mechanisms which influence conscientious spending due to differing payment methods were analyzed and employed to design a prototype incorporating a tangible interaction that is relevant to the consumer’s spending process. It is proposed that re-introducing tangibility into the payment process can increase one’s psychological ownership over their financial resources and purchased item, as well as the aversion towards paying. Results from the experiments show that the interaction enhances psychological ownership over digital currency but remain inconclusive on behavior change. The broader implications of enhancing tangibility for intangible objects is discussed.
Decentralized Informative Path Planning With Balanced Exploration-Exploitation for Swarm Robotic Search
Authors: Ghassemi Payam , Chowdhury Souma
Published: November 2019

Swarm robotic search is concerned with searching targets in unknown environments (e.g., for search and rescue or hazard localization), using a large number of collaborating simple mobile robots. In such applications, decentralized swarm systems are touted for their task/coverage scalability, time efficiency, and fault tolerance. To guide the behavior of such swarm systems, two broad classes of approaches are available, namely nature-inspired swarm heuristics and multi-robotic search methods. However, simultaneously offering computationally-efficient scalability and fundamental insights into the exhibited behavior (instead of a black-box behavior model), remains challenging under either of these two class of approaches. In this paper, we develop an important extension of the batch Bayesian search method for application to embodied swarm systems, searching in a physical 2D space. Key contributions lie in: 1) designing an acquisition function that not only balances exploration and exploitation across the swarm, but also allows modeling knowledge extraction over trajectories; and 2) developing its distributed implementation to allow asynchronous task inference and path planning by the swarm robots. The resulting collective informative path planning approach is tested on target search case studies of varying complexity, where the target produces a spatially varying (measurable) signal. Significantly superior performance, in terms of mission completion efficiency, is observed compared to exhaustive search and random walk baselines, along with favorable performance scalability with increasing swarm size.
A New Approach for Medical Assessment of Patient’s Injured Shoulder
Authors: Vitali Andrea , Regazzoni Daniele , Rizzi Caterina , Maffioletti Federico
Published: November 2019

Low cost marker-less motion capture (Mocap) systems can be considered an interesting technology for the objective assessment of rehabilitation processes. In particular, this paper presents a feasibility study to introduce a Mocap system as a tool to assess shoulder rehabilitation. The movements of a shoulder are complex and challenging to be captured with a marker-less system because the skeleton avatar usually oversimplifies shoulder articulation with a single virtual joint. The designed solution integrates a low-cost Mocap system with image processing techniques and convolutional neural networks to automatically detect and measure potential compensatory movements executed during an abduction, which is one of the first post-surgery exercises for shoulder rehabilitation.

First, we introduce the main steps of a reference roadmap that guided the development of the Mocap solution for rehab assessment of injured shoulder. Then, the acquisition of medical knowledge is presented as well as the new Mocap solution based on the integration of convolutional neural networks and 2D motion tracking techniques. Finally, the application which automatically evaluates abductions and makes available the measurements of the scapular elevations is described. Preliminary study and future works are also presented and discussed.
Multimaterial Topology Optimization of Thermoelectric Generators
Authors: Xu Xiaoqiang , Wu Yongjia , Zuo Lei , Chen Shikui
Published: November 2019

Over 50% of the energy from power plants, vehicles, oil refining, and steel or glass making process is released to the atmosphere as waste heat. As an attempt to deal with the growing energy crisis, the solid-state thermoelectric generator (TEG), which converts the waste heat into electricity using Seebeck phenomenon, has gained increasing popularity. Since the figures of merit of the thermoelectric materials are temperature dependent, it is not feasible to achieve high efficiency of the thermoelectric conversion using only one single thermoelectric material in a wide temperature range. To address this challenge, this paper proposes a method based on topology optimization to optimize the layouts of functional graded TEGs consisting of multiple materials. The objective of the optimization problem is to maximize the output power and conversion efficiency as well. The proposed method is implemented using the Solid Isotropic Material with Penalization (SIMP) method. The proposed method can make the most of the potential of different thermoelectric materials by distributing each material into its optimal working temperature interval. Instead of dummy materials, both the P and N-type electric conductors are optimally distributed with two different practical thermoelectric materials, namely Bi2Te3 & PbTe for P-type, and Bi2Te3 & CoSb3 for N-type respectively, with the yielding conversion efficiency around 12.5% in the temperature range Tc = 25°C and Th = 400°C. In the 2.5D computational simulation, however, the conversion efficiency shows a significant drop. This could be attributed to the mismatch of the external load and internal TEG resistance as well as the grey region from the topology optimization results as discussed in this paper.
A Robotic Tool for Magnetic Micromanipulation of Cells in the Presence of an Ambient Fluid Flow
Authors: Agarwal Dharmveer , Thakur Ajay D. , Thakur Atul
Published: November 2019

This paper reports an automated image-guided microrobotic tool to perform nonprehensile magnetic manipulation of large (of the order of few hundreds of microns) microscopic biological objects in the presence of an ambient fluid flow. The developed tool comprises of ferromagnetic microrobots actuated by electromagnetic coils arranged in a quadrupole configuration, a DC power source, and a pulse width modulation (PWM) based controller to vary the coil currents. In order to accomplish the stated objective of automated micromanipulation task, a two-tier approach is adopted, namely, (1) generation of a feedback planning algorithm that invokes one of the two motion maneuvers, namely, ‘arrest’ and ‘move’ and (2) development of a proportional controller that determines the currents to be passed through the coils based on the maneuver invoked so that the resultant magnetic field actuates the ferromagnetic microrobot in the desired direction. A physical experiment was conducted and reported to authenticate the validity of the developed approach. We believe that the developed tool can be used to perform automated feedback controlled micromanipulation of large biological cells and cell aggregates in the presence of an ambient fluid flow especially in in-vivo environments. The inherent biocompatibility of the microbot material provides a possibility to functionalize it with living cells and/or appropriate chemicals rendering it feasible to implement drug delivery and also perform on-chip biological experiments.
Model Bias Characterization for Effective States Estimation of Lithium-Ion Batteries
Authors: Dahmardeh Modjtaba , Xi Zhimin
Published: November 2019

State of charge (SOC) estimation of lithium-ion batteries has been extensively studied and the estimation accuracy was mainly investigated through the development of various battery models and dynamic estimation algorithms. All battery models, however, contain inherent model bias due to the simplifications and assumptions, which cannot be effectively addressed through the development of various algorithms such as Kalman filtering (KF) or particle filtering (PF). Consequently, as observed in our study, battery SOC estimation using a typical extended KF in fact is not very accurate where the error could range from 5% to 10% or even more depending on the battery characteristics. This paper proposes bias characterization of the battery model so that accuracy of the baseline model could be significantly improved and eventually SOC estimation could be much more accurate than the one only using the baseline model. This paper reports great potential for improving battery SOC estimation with the bias characterization and proposes two methods for actual bias modeling. In particular, the polynomial regression model and the Gaussian process (GP) regression model are proposed to examine the effects of the two methods on bias modeling and SOC estimation using a typical battery circuit model. Results are demonstrated in lab testing using three battery charging/discharging profiles with the cross-validation technique.
Energetically-Optimal Discrete and Continuous Stabilization of the Rimless Wheel With Torso
Authors: Sirichotiyakul Wankun , Satici Aykut C. , Sanchez Eric S. , Bhounsule Pranav A.
Published: November 2019

In this work, we discuss the modeling, control, and implementation of a rimless wheel with torso. We derive and compare two control methodologies: a discrete-time controller (DT) that updates the controls once-per-step and a continuous-time controller (CT) that updates gains continuously. For the discrete controller, we use least-squares estimation method to approximate the Poincaré map on a certain section and use discrete-linear-quadratic-regulator (DQLR) to stabilize a (closed-form) linearization of this map. For the continuous controller, we introduce moving Poincaré sections and stabilize the transverse dynamics along these moving sections. For both controllers, we estimate the region of attraction of the closed-loop system using sum-of-squares methods. Analysis of the impact map yields a refinement of the controller that stabilizes a steady-state walking gait with minimal energy loss. We present both simulation and experimental results that support the validity of the proposed approaches. We find that the CT controller has a larger region of attraction and smoother stabilization as compared with the DT controller.
Computer Vision-Based Object Recognition and Automatic Pneumatic Soft Gripping
Authors: Shahabi Ebrahim , Lu Wei-Hao , Lin Po Ting , Kuo Chin-Hsing
Published: November 2019

During recent years, soft robotic is a new sub-class of the robots. Soft robotic has several engaging features, such as lightweight, low cost, simple fabrication, easy control, etc. Commercial products such as soft grippers are now available to apply in various fields and applications, for example, agriculture, medicine, machinery, etc. This paper proposes a novel method of grasping in soft robotic fields using computer vision to find the shape, size, and angle of the object to define the best type of grasping mode. Random Sample Consensus (RANSAC) was used to iteratively select randomly sampled 3D points to determine the working plane and identify the randomly placed object. Furthermore, we designed and fabricated a 3D-printed pneumatic soft actuator. The ratio of payload over weight is around 16. Experiments showed the proposed computer vision techniques and pneumatic soft gripper are capable of automatically recognize the object shape and perform soft gripping.
Checking the Automated Construction of Finite Element Simulations From Dirichlet Boundary Conditions
Authors: Chiu Kevin N. , Fuge Mark D.
Published: November 2019

From engineering analysis and topology optimization to generative design and machine learning, many modern computational design approaches require either large amounts of data or a method to generate that data. This paper addresses key issues with automatically generating such data through automating the construction of Finite Element Method (FEM) simulations from Dirichlet boundary conditions. Most past work on automating FEM assumes prior knowledge of the physics to be run or is limited to a small number of governing equations.

In contrast, we propose three improvements to current methods of automating the FEM: (1) completeness labels that guarantee viability of a simulation under specific conditions, (2) type-based labels for solution fields that robustly generate and identify solution fields, and (3) type-based labels for variational forms of governing equations that map the three components of a simulation set — specifically, boundary conditions, solution fields, and a variational form — to each other to form a viable FEM simulation. We implement these improvements using the FEniCS library as an example case. We show that our improvements increase the percent of viable simulations that are run automatically from a given list of boundary conditions. This paper’s procedures ultimately allow for the automatic — i.e., fully computer-controlled — construction of FEM multi-physics simulations and data collection required to run data-driven models of physics phenomena or automate the exploration of topology optimization under many physics.
A Computational Investigation Into the Impact of Sensor Location on the Acoustics-Based Damage Detection From an Airfoil Structure
Authors: Traylor Caleb , Inalpolat Murat
Published: November 2019

As the use of wind power continues to increase globally, the need for improved structural health monitoring systems for wind turbine blades increases as well. Acoustics-based methods are deemed promising in this endeavor, as they are non-contact, nondestructive and enabling distributed sensing. To systematically design an acoustics-based blade health monitoring system, a representative computational study using a NACA 0012 airfoil was conducted to examine the impact of damage location, damage size, acoustic source location, and microphone placement on the detection rate. Structural-acoustic coupled simulations were performed using a commercially available finite element based tool. Results indicated that sound pressure levels (SPL) did not increase significantly when the damage was not near the source location, indicating that using a change in SPL to identify damage may not be the best method. However, spatial variability of the change in SPL in the blade’s internal cavity was found to be a strong indicator of damage, resulting in a detection rate of 81–92% depending on the damage size. Based on the computationally obtained results, the recommendation is for further investigation into the spatial variation of sound inside the blade cavity using 2–3 microphones per cavity to identify damage, when feasible, in addition to testing the effectiveness of a one microphone per cavity design with different blade geometries. The optimal positions of these microphones and the reliability of this method will continue to be investigated in future work.
Integrating Quality of Life in Sociotechnical Design: A Review of Microgrid Design Tools and Social Indicators
Authors: Suk Hailie , Hall John
Published: November 2019

Microgrid systems can provide distributed power to communities that lack basic access to electricity. Scalability, maintenance, and power management are prominent challenges associated with microgrid resilience. These obstacles can be limiting in the continued development of the community. As progress drives an increase in energy demand, the microgrid needs to continue to meet that demand. Designing systems with the appropriate maintenance levels is another obstacle that impacts resiliency.

There are many design tools that analyze system performance and cost. One goal of this work is to review a set of these technical models. HOMER Energy, System Advisor Model (SAM), and the Microgrid Design Toolkit (MDT) were reviewed. However, technical and economic performance is just one aspect of design. In the design procedure, social parameters also need to be considered. Few, if any, microgrid design tools consider societal dynamics. This work also aims to evaluate social indicators in the context of sustainable microgrid design. Accordingly, relationships are established between common societal indicators and the microgrid challenges of scalability, maintenance, and power management.
Fabrication and Testing of a MEMS System for Injection of DNA Into Plant Cells
Authors: Brown Taylor , Hope Sandra , Jensen Brian D.
Published: November 2019

This paper describes the fabrication and testing of a system to inject DNA into plant leaves. Arrays of silicon lances were made using photolithographic and STS DRIE Bosch techniques. A nanoinjector device was also made to accept the silicon lance arrays and perform nanoinjections. Nanoinjections were performed on Arabidopsis and cotton cotyledons. Changes in the force applied during a nanoinjection and varying the number of repeated nanoinjections on the same cotyledon were observed. Too much force or too many repeated injections caused physical damage to the cotyledon. An optimal force and number of repeated injections can be performed without causing physical damage to the cotyledon. Several injections using DNA were performed without successful transfection of the leaves. Possible reasons for this failure to transfect were explored.
Time Invariant Motion Controller for Physical Human Robot Interaction
Authors: Muñoz Osorio Juan D. , Castañeda Felipe , Allmendinger Felix , Zimmermann Uwe E.
Published: November 2019

In this paper, a new method to perform time-invariant motion is proposed. The approach uses the advantages of variable impedance control to have full interactivity with the human while tracing a desired path. The path is followed at maximum velocity while the human can disturb the robot at any instant and manipulate it far from the desired path without changing the control law. The method was proven in simulation and on a real torque-controlled serial manipulator (KUKA LWR iiwa). The chosen paths were designed to perform robotic assisted ultrasound scanning for breast cancer diagnosis. Results show that interactivity is allowed while keeping a low kinetic energy of the robot. Despite of human disturbances, the robot can keep tracing the path after the interaction is finished.
Design of a Two DOF Laminate Leg Transmission for Creating Walking Robot Platforms
Authors: Shuch Benjamin D. , Shafa Taha , Rogers Eric , Aukes Daniel M.
Published: November 2019

In this article we present a low-cost, two degree-of-freedom laminate robot transmission for legged locomotion applications. This transmission is specifically applied in the design of a quadrupedal robot, and has the potential to be used in other multi-legged systems. It offers a complex control space with a variety of different programmable gait trajectories, while leveraging low-cost linkages made using laminate approaches. The two-degree-of-freedom kinematics of the leg are subsequently modeled in Python, and the workspace of the robot is then experimentally verified on an initial quadrupedal design. Critical design considerations include the laminate design, the rigidity of the materials that make up the laminate, and the range of motion the device can undergo.
Using Bayesian Optimization With Knowledge Transfer for High Computational Cost Design: A Case Study in Photovoltaics
Authors: Kaya Mine , Hajimirza Shima
Published: November 2019

Engineering design is usually an iterative procedure where many different configurations are tested to yield a desirable end performance. When the design objective can only be measured by costly operations such as experiments or cumbersome computer simulations, a thorough design procedure can be limited. The design problem in these cases is a high cost optimization problem. Meta model-based approaches (e.g. Bayesian optimization) and transfer optimization are methods that can be used to facilitate more efficient designs. Transfer optimization is a technique that enables using previous design knowledge instead of starting from scratch in a new task. In this work, we study a transfer optimization framework based on Bayesian optimization using Gaussian Processes. The similarity among the tasks is determined via a similarity metric. The framework is applied to a particular design problem of thin film solar cells. Planar multilayer solar cells with different sets of materials are optimized to obtain the best opto-electrical efficiency. Solar cells with amorphous silicon and organic absorber layers are studied and the results are presented.
Coordinated Model Predictive Control on Multi-Lane Roads
Authors: Goulet Nathan , Ayalew Beshah
Published: November 2019

There are significant economic, environmental, energy, and other societal costs incurred by the road transportation sector. With the advent and penetration of connected and autonomous vehicles there are vast opportunities to optimize the control of individual vehicles for reducing energy consumption and increasing traffic flow. Model predictive control is a useful tool to achieve such goals, while accommodating ego-centric objectives typical of heterogeneous traffic and explicitly enforcing collision and other constraints. In this paper, we describe a multi-agent distributed maneuver planning and lane selection model predictive controller that includes an information sharing and coordination scheme. The energy saving potential of the proposed coordination scheme is then evaluated via large scale microscopic traffic simulations considering different penetration levels of connected and automated vehicles.
Design and Making of a Hand-Held Spillage-Free Specimen Retrieval Robot for Laparoscopic Surgery
Authors: Tavakkolmoghaddam Farid , Kaipa Krishnanand , Dong Siqin , Audette Michel , Mesar Tomaz , Feliberti Eric C.
Published: November 2019

This paper presents the design and making of RoboCatch, a novel hand-held robot that aids surgeons in performing spillage-free specimen retrieval in laparoscopic surgery. The proposed design significantly modifies and extends conventional instruments that are currently used for this task. Specimen retrieval is initiated by introducing the robot’s trocar into the surgical site. A webbed three-fingered grasper slides out of the trocar and unfolds in an umbrella like-fashion. A pair of atraumatic forceps, concentrically aligned with the grasper, slide outward to grasp an excised specimen and retract it into the center of the grasper, which then closes to achieve a secured containment of the specimen. Finally, the robot is manually removed from the abdominal cavity. Physical validation experiments involving specimen retrieval of porcine meat samples revealed that the robot could retrieve specimens of masses ranging from 1 gram to 40 grams.
A Self-Supervised Learning Technique for Road Defects Detection Based on Monocular Three-Dimensional Reconstruction
Authors: Hu Yazhe , Furukawa Tomonari
Published: November 2019

This paper presents a self-supervised learning technique for road surface defects detection using a monocular camera. The uniqueness of the proposed technique relies on its self-supervised learning structure which is achieved by combining physics-driven three-dimensional (3D) reconstruction with data-driven Convolutional Neural Network (CNN). Only images from one camera are needed as the inputs to the model without human labeling. The 3D point cloud are reconstructed from input images based on a near-planar road 3D reconstruction process to self-supervise the learning process. During testing, the network receives images and predicts the images as defect or non-defect. A refined class prediction is produced by combining the 3D road surface data with the network output when the belief of original network prediction is not strong enough to conclude the classification. Experiments are conducted on real road surface images to find the optimal parameters for this model. The testing results demonstrate the robustness and effectiveness of the proposed self-supervised road surface defects detection technique.
A Two-Stage Optimization Procedure for the Design of an EAP-Actuated Soft Gripper
Authors: Zhang Wei , Hong Jonathan , Ahmed Saad , Ounaies Zoubeida , Frecker Mary
Published: November 2019

An increasing range of engineering applications require soft grippers, which use compliant mechanisms instead of stiff components to achieve grasping action, have high conformability and exert gentle contact with target objects compared to traditional grippers. In this study, a three-fingered gripper is first designed based on a notched self-folding mechanism actuated using an electrostrictive PVDF-based terpolymer. Then the design optimization problem is formulated, where the design objectives are to maximize the free deflection Δfree and the blocked force Fb. A computationally efficient two-stage design optimization procedure is proposed and successfully applied in the gripper design. NSGA-II is adopted as the optimization algorithm for its capacity to deal with multi-objective optimization problems and to find the global optima with high design variables and large design domains. In stage one, computationally less expensive analytical models are developed based on Bernoulli-Euler beam theory and Castigliano’s theorem to calculate Δfree and Fb. Utility function is applied to determine the best design in the last generation of stage one. In stage two, 3D FEA models are developed, using the dimensions determined by the best design from stage one, to investigate effect of the shape of segment surfaces on the design objectives. Overall, the proposed two-stage optimization procedure is successfully applied in the actuator design and shows the potential to solve a wide range of structural optimization problems.
From Scan Strategy to Melt Pool Prediction: A Neighboring-Effect Modeling Method
Authors: Yang Zhuo , Yan Lu , Yeung Ho , Krishnamurty Sundar
Published: November 2019

The quality of AM built parts is highly correlated to the melt pool characteristics. Hence melt pool monitoring and control can potentially improve AM part quality. This paper presents a neighboring-effect modeling method (NBEM) that uses scan strategy to predict melt pool size. The prediction model can further assist in scan strategy optimization and real-time process control. The structure of the proposed model is formulated based on the physical principles of melt pool formation, while experimental data is used to identify the optimal coefficients. Compared to the traditional power-velocity prediction models, NBEM model introduces the cumulative neighboring-effect factors as additional input variables. These factors represent the neighborhood impact of scan path on the focal point melt pool formation from time and distance perspective. Two experiments use different scan strategies to collect in-situ measurements of melt pool size for model construction and validation. By introducing the neighboring-effect factors, the global Normalized Root Mean Square Error (NRMSE) is improved from around 0.10 to 0.08. More importantly, the local NRMSE of irregular melt pool area prediction is improved to around 0.15 for more than 50% improvement. The case studies verify that the proposed method can predict the melt pool variations by seamlessly integrating scan strategy considerations.