Scholarly Works, Mechanical Engineering

Permanent URI for this collection

https://hdl.handle.net/10919/23262

Research articles, presentations, and other scholarship

Browse

Now showing 1 - 20 of 481

Built On-Orbit Robotically Assembled Gigatruss (BORG): Ground Robotic Demonstration
Chapin, Samantha; Everson, Holly; Chapin, William; Komendera, Erik (MDPI, 2024-05-31)
The next generation of large space infrastructure will require crucial advancements in current technology. Current methodologies focus on large deployable structures folded into cramped payload fairings or revolutionary assembly techniques requiring many moving components. Utilizing both in-space assembly and deployable concepts, a hybrid mixed assembly scheme was posed using smaller deployable units interspersed with rigid connecting elements to assemble these large architectures. The Built On-Orbit Robotically Assembled Gigatruss (BORG) structure allows for modularity in assembly and repair with the number of separate elements comprising the structure to be reduced, compared to strut-by-strut assembly. The following documents the process of constructing and running physical trials on a prototype BORG architecture. Additionally, a Semantic and Fiducial Aided Graph Simultaneous Localization and Mapping (SF-GraphSLAM) approach is taken to verify the relation of assembled and deployed truss elements to aid in error evaluation and state estimation. This technology demonstration stands as a proof of concept in verifying the viability of the BORG architecture as a method for large structure assembly.
Investigation of Viscoelastic Guided Wave Properties in Anisotropic Laminated Composites Using a Legendre Orthogonal Polynomials Expansion–Assisted Viscoelastodynamic Model
Liu, Hongye; Huang, Ziqi; Yin, Zhuang; Sun, Maoxun; Bo, Luyu; Li, Teng; Tian, Zhenhua (MDPI, 2024-06-10)
This study investigates viscoelastic guided wave properties (e.g., complex–wavenumber–, phase–velocity–, and attenuation–frequency relations) for multiple modes, including different orders of antisymmetric, symmetric, and shear horizontal modes in viscoelastic anisotropic laminated composites. To obtain those frequency–dependent relations, a guided wave characteristic equation is formulated based on a Legendre orthogonal polynomials expansion (LOPE)–assisted viscoelastodynamic model, which fuses the hysteretic viscoelastic model–based wave dynamics and the LOPE–based mode shape approximation. Then, the complex–wavenumber–frequency solutions are obtained by solving the characteristic equation using an improved root–finding algorithm, which leverages coefficient matrix determinant ratios and our proposed local tracking windows. To trace the solutions on the dispersion curves of different wave modes and avoid curve–tracing misalignment in regions with phase–velocity curve crossing, we presented a curve–tracing strategy considering wave attenuation. With the LOPE–assisted viscoelastodynamic model, the effects of material viscosity and fiber orientation on different guided wave modes are investigated for unidirectional carbon–fiber–reinforced composites. The results show that the viscosity in the hysteresis model mainly affects the frequency–dependent attenuation of viscoelastic guided waves, while the fiber orientation influences both the phase–velocity and attenuation curves. We expect the theoretical work in this study to facilitate the development of guided wave–based techniques for the NDT and SHM of viscoelastic anisotropic laminated composites.
Robot-assisted chirality-tunable acoustic vortex tweezers for contactless, multifunctional, 4-DOF object manipulation
Li, Teng; Li, Jiali; Bo, Luyu; Bachman, Hunter; Fan, Bei; Cheng, Jiangtao; Tian, Zhenhua (American Association for the Advancement of Science, 2024-05-24)
Robotic manipulation of small objects has shown great potential for engineering, biology, and chemistry research. However, existing robotic platforms have difficulty in achieving contactless, high-resolution, 4-degrees- of- freedom (4-DOF) manipulation of small objects, and noninvasive maneuvering of objects in regions shielded by tissue and bone barriers. Here, we present chirality-tunable acoustic vortex tweezers that can tune acoustic vortex chirality, transmit through biological barriers, trap single micro-to millimeter-sized objects, and control object rotation. Assisted by programmable robots, our acoustic systems further enable contactless, high-resolution translation of single objects. Our systems were demonstrated by tuning acoustic vortex chirality, controlling object rotation, and translating objects along arbitrary-shaped paths. Moreover, we used our systems to trap single objects in regions with tissue and skull barriers and translate an object inside a Y-shaped channel of a thick biomimetic phantom. In addition, we showed the function of ultrasound imaging–assisted acoustic manipulation by monitoring acoustic object manipulation via live ultrasound imaging.
Pulse Train Fx-LMS Algorithm for Drive File Identification
Balasubramanya, Bharath; Southward, Steve C. (MDPI, 2024-04-25)
A novel time-domain algorithm is proposed in this paper for the iterative estimation of drive files. A drive file is a synchronized batch of dynamic time series commands that are simultaneously sent to one or more actuators in a test rig that is designed for service environment replication (SER). When drive file commands are input to an SER test rig, the response of the article under test is similar to what was measured in a service environment. The proposed Pulse Train Filtered-X Least Mean Square (PT-Fx-LMS) algorithm is based on methods developed for active noise and vibration control (ANVC). A time-domain PT-Fx-LMS algorithm is shown through several simulation studies to rapidly converge to a dynamic solution in a small number of iterations for a one degree-of-freedom nonlinear suspension. The PT-Fx-LMS algorithm is also shown to enable targeted iteration over isolated time slices within the data set, which challenges conventional frequency-domain techniques.
Autonomous Alignment and Docking Control for a Self-Reconfigurable Modular Mobile Robotic System
Feng, Shumin; Liu, Yujiong; Pressgrove, Isaac; Ben-Tzvi, Pinhas (MDPI, 2024-05-20)
This paper presents the path planning and motion control of a self-reconfigurable mobile robot system, focusing on module-to-module autonomous docking and alignment tasks. STORM, which stands for Self-configurable and Transformable Omni-Directional Robotic Modules, features a unique mode-switching ability and novel docking mechanism design. This enables the modules that make up STORM to dock with each other and form a variety configurations in or to perform a large array of tasks. The path planning and motion control presented here consists of two parallel schemes. A Lyapunov function-based precision controller is proposed to align the target docking mechanisms in a small range of the target position. Then, an optimization-based path planning algorithm is proposed to help find the fastest path and determine when to switch its locomotion mode in a much larger range. Both numerical simulations and real-world experiments were carried out to validate these proposed controllers.
Understanding the Impact of Fuel on Surfactant Microstructure of Firefighting Foam
Islam, Rezawana; Lattimer, Brian Y. (Springer Link, 2024-05-01)
Aqueous film-forming foam is being phased out due to the environmental impacts of fluorinated surfactants contained in the firefighting foams. To develop an environmentally friendly firefighting foam, it is important to understand the factors controlling the firefighting performance of surfactants. Fuel transport through foam has been considered as a dominant mechanism for foam collapse. Therefore, the impact of fuels (heptane, octane and trimethylbenzene (TMB)) on surfactant microstructure was studied for three different types of surfactants (Capstone, Glucopon, and siloxane) that have applications in firefighting foam. Multiple techniques were used to identify the microstructure and interfacial properties of surfactants with and without exposure to liquid fuel. The ignition time of fuel vapor through foam and solubility of fuel through liquid surfactant solution were measured as well. This work shows fuel solubility has an impact on the surfactant microstructure and interfacial properties. In addition, fuel solubility and vapor pressure affect the ignition time of fuel vapors.
Guest editorial: Special Issue on Artificial Intelligence and Emerging Computational Approaches for Tribology
Zhang, Zhinan; Pan, Shuaihang; Raeymaekers, Bart (2024-04-02)
SARI: Shared Autonomy across Repeated Interaction
Jonnavittula, Ananth; Mehta, Shaunak; Losey, Dylan (ACM, 2024)
Assistive robot arms try to help their users perform everyday tasks. One way robots can provide this assistance is shared autonomy. Within shared autonomy, both the human and robot maintain control over the robot’s motion: as the robot becomes conident it understands what the human wants, it intervenes to automate the task. But how does the robot know these tasks in the irst place? State-of-the-art approaches to shared autonomy often rely on prior knowledge. For instance, the robot may need to know the human’s potential goals beforehand. During long-term interaction these methods will inevitably break down Ð sooner or later the human will attempt to perform a task that the robot does not expect. Accordingly, in this paper we formulate an alternate approach to shared autonomy that learns assistance from scratch. Our insight is that operators repeat important tasks on a daily basis (e.g., opening the fridge, making cofee). Instead of relying on prior knowledge, we therefore take advantage of these repeated interactions to learn assistive policies. We introduce SARI, an algorithm that recognizes the human’s task, replicates similar demonstrations, and returns control when unsure. We then combine learning with control to demonstrate that the error of our approach is uniformly ultimately bounded. We perform simulations to support this error bound, compare our approach to imitation learning baselines, and explore its capacity to assist for an increasing number of tasks. Finally, we conduct three user studies with industry-standard methods and shared autonomy baselines, including a pilot test with a disabled user. Our results indicate that learning shared autonomy across repeated interactions matches existing approaches for known tasks and outperforms baselines on new tasks. See videos of our user studies here: https://youtu.be/3vE4omSvLvc
Additive Manufacturing of Poly(phenylene Sulfide) Aerogels via Simultaneous Material Extrusion and Thermally Induced Phase Separation
Godshall, Garrett F.; Rau, Daniel A.; Williams, Christopher B.; Moore, Robert B. (Wiley-VCH GmbH, 2023-11)
Additive manufacturing (AM) of aerogels increases the achievable geometric complexity, and affords fabrication of hierarchically porous structures. In this work, a custom heated material extrusion (MEX) device prints aerogels of poly(phenylene sulfide) (PPS), an engineering thermoplastic, via in situ thermally induced phase separation (TIPS). First, pre-prepared solid gel inks are dissolved at high temperatures in the heated extruder barrel to form a homogeneous polymer solution. Solutions are then extruded onto a room-temperature substrate, where printed roads maintain their bead shape and rapidly solidify via TIPS, thus enabling layer-wise MEX AM. Printed gels are converted to aerogels via postprocessing solvent exchange and freeze-drying. This work explores the effect of ink composition on printed aerogel morphology and thermomechanical properties. Scanning electron microscopy micrographs reveal complex hierarchical microstructures that are compositionally dependent. Printed aerogels demonstrate tailorable porosities (50.0–74.8%) and densities (0.345–0.684 g cm⁻³), which align well with cast aerogel analogs. Differential scanning calorimetry thermograms indicate printed aerogels are highly crystalline (≈43%), suggesting that printing does not inhibit the solidification process occurring during TIPS (polymer crystallization). Uniaxial compression testing reveals that compositionally dependent microstructure governs aerogel mechanical behavior, with compressive moduli ranging from 33.0 to 106.5 MPa.
Enhancing Autonomous Vehicle Navigation with a Clothoid-Based Lateral Controller
Shaju, Aashish; Southward, Steve; Ahmadian, Mehdi (MDPI, 2024-02-22)
This study introduces an advanced lateral control strategy for autonomous vehicles using a clothoid-based approach integrated with an adaptive lookahead mechanism. The primary focus is on enhancing lateral stability and path-tracking accuracy through the application of Euler spirals for smooth curvature transitions, thereby reducing passenger discomfort and the risk of vehicle rollover. An innovative aspect of our work is the adaptive adjustment of lookahead distance based on real-time vehicle dynamics and road geometry, which ensures optimal path following under varying conditions. A quasi-feedback control algorithm constructs optimal clothoids at each time step, generating the appropriate steering input. A lead filter compensates for the vehicle’s lateral dynamics lag, improving control responsiveness and stability. The effectiveness of the proposed controller is validated through a comprehensive co-simulation using TruckSim^® and Simulink^®, demonstrating significant improvements in lateral control performance across diverse driving scenarios. Future directions include scaling the controller for higher-speed applications and further optimization to minimize off-track errors, particularly for articulated vehicles.
Predicting Ion Sequestration in Charged Polymers with the Steepest-Entropy-Ascent Quantum Thermodynamic Framework
McDonald, Jared; von Spakovsky, Michael R.; Reynolds, William T. (MDPI, 2024-03-01)
The steepest-entropy-ascent quantum thermodynamic framework is used to investigate the effectiveness of multi-chain polyethyleneimine-methylenephosphonic acid in sequestering rare-earth ions (Eu³⁺) from aqueous solutions. The framework applies a thermodynamic equation of motion to a discrete energy eigenstructure to model the binding kinetics of europium ions to reactive sites of the polymer chains. The energy eigenstructure is generated using a non-Markovian Monte Carlo model that estimates energy level degeneracies. The equation of motion is used to determine the occupation probability of each energy level, describing the unique path through thermodynamic state space by which the polymer system sequesters rare-earth ions from solution. A second Monte Carlo simulation is conducted to relate the kinetic path in state space to physical descriptors associated with the polymer, including the radius of gyration, tortuosity, and Eu-neighbor distribution functions. These descriptors are used to visualize the evolution of the polymer during the sequestration process. The fraction of sequestered Eu³⁺ ions depends upon the total energy of the system, with lower energy resulting in greater sequestration. The kinetics of the overall sequestration are dependent on the steepest-entropy-ascent principle used by the equation of motion to generate a unique kinetic path from an initial non-equilibrium state.
Dual-Use Strain Sensors for Acoustic Emission and Quasi-Static Bending Measurements
Stiefvater, Jason; Kang, Yuhong; de Clerck, Albrey; Mao, Shuo; Jones, Noah; Deem, Josh; Wicks, Alfred; Ruan, Hang; Ng, Wing (MDPI, 2024-03-02)
In this paper, a MEMS piezoresistive ultrathin silicon membrane-based strain sensor is presented. The sensor’s ability to capture an acoustic emission signal is demonstrated using a Hsu–Nielsen source, and shows comparable frequency content to a commercial piezoceramic ultrasonic transducer. To the authors’ knowledge, this makes the developed sensor the first known piezoresistive strain sensor which is capable of recording low-energy acoustic emissions. The improvements to the nondestructive evaluation and structural health monitoring arise from the sensor’s low minimum detectable strain and wide-frequency bandwidth, which are generated from the improved fabrication process that permits crystalline semiconductor membranes and advanced polymers to be co-processed, thus enabling a dual-use application of both acoustic emission and static strain sensing. The sensor’s ability to document quasi-static bending is also demonstrated and compared with an ultrasonic transducer, which provides no significant response. This dual-use application is proposed to effectively combine the uses of both strain and ultrasonic transducer sensor types within one sensor, making it a novel and useful method for nondestructive evaluations. The potential benefits include an enhanced sensitivity, a reduced sensor size, a lower cost, and a reduced instrumentation complexity.
Molecular modeling of Poly(methyl methacrylate-block-acrylonitrile) as Precursors of Porous Carbon Fibers
Hao, Xi; Serrano, Joel; Liu, Guoliang; Cheng, Shengfeng (2023-04-22)
MEMTRACK: A Deep Learning-Based Approach to Microrobot Tracking in Dense and Low-Contrast Environments.
Sawhney, Medha; Karmarkar, Bhas; Leaman, Eric J.; Daw, Arka; Karpatne, Anuj; Behkam, Bahareh (2023)
Tracking microrobots is challenging, considering their minute size and high speed. As the field progresses towards developing microrobots for biomedical applications and conducting mechanistic studies in physiologically relevant media (e.g., collagen), this challenge is exacerbated by the dense surrounding environments with feature size and shape comparable to microrobots. Herein, we report Motion Enhanced Multi-level Tracker (MEMTrack), a robust pipeline for detecting and tracking microrobots using synthetic motion features, deep learning-based object detection, and a modified Simple Online and Real-time Tracking (SORT) algorithm with interpolation for tracking. Our object detection approach combines different models based on the object’s motion pattern. We trained and validated our model using bacterial micro-motors in collagen (tissue phantom) and tested it in collagen and aqueous media. We demonstrate that MEMTrack accurately tracks even the most challenging bacteria missed by skilled human annotators, achieving precision and recall of 77% and 48% in collagen and 94% and 35% in liquid media, respectively. Moreover, we show that MEMTrack can quantify average bacteria speed with no statistically significant difference from the laboriously-produced manual tracking data. MEMTrack represents a significant contribution to microrobot localization and tracking, and opens the potential for vision-based deep learning approaches to microrobot control in dense and low-contrast settings. All source code for training and testing MEMTrack and reproducing the results of the paper have been made publicly available https://github.com/sawhney-medha/MEMTrack.
Inducing stratification of colloidal mixtures with a mixed binary solvent
Liu, Binghan; Grest, Gary S.; Cheng, Shengfeng (Royal Society of Chemistry, 2023-12-06)
Molecular dynamics simulations are used to demonstrate that a binary solvent can be used to stratify colloidal mixtures when the suspension is rapidly dried. The solvent consists of two components, one more volatile than the other. When evaporated at high rates, the more volatile component becomes depleted near the evaporation front and develops a negative concentration gradient from the bulk of the mixture to the liquid-vapor interface while the less volatile solvent is enriched in the same region and exhibit a positive concentration gradient. Such gradients can be used to drive a binary mixture of colloidal particles to stratify if one is preferentially attracted to the more volatile solvent and the other to the less volatile solvent. During solvent evaporation, the fraction of colloidal particles preferentially attracted to the less volatile solvent is enhanced at the evaporation front, whereas the colloidal particles having stronger attractions with the more volatile solvent are driven away from the interfacial region. As a result, the colloidal particles show a stratified distribution after drying, even if the two colloids have the same size.
Chain conformations and phase separation in polymer solutions with varying solvent quality
Huang, Yisheng; Cheng, Shengfeng (Wiley, 2021-10-02)
Molecular dynamics simulations are used to investigate the conformations of a single polymer chain, represented by the Kremer-Grest bead-spring model, in a solution with a Lennard-Jones liquid as the solvent when the interaction strength between the polymer and solvent is varied. Results show that when the polymer-solvent interaction is unfavorable, the chain collapses as one would expect in a poor solvent. For more attractive polymer-solvent interactions, the solvent quality improves and the chain is increasingly solvated and exhibits ideal and then swollen conformations. However, as the polymer-solvent interaction strength is increased further to be more than about twice the strength of the polymer-polymer and solvent-solvent interactions, the chain exhibits an unexpected collapsing behavior. Correspondingly, for strong polymer-solvent attractions, phase separation is observed in the solutions of multiple chains. These results indicate that the solvent becomes effectively poor again at very attractive polymer-solvent interactions. Nonetheless, the mechanism of chain collapsing and phase separation in this limit differs from the case with a poor solvent rendered by unfavorable polymer-solvent interactions. In the latter, the solvent is excluded from the domain of the collapsed chains while in the former, the solvent is still present in the pervaded volume of a collapsed chain or in the polymer-rich domain that phase separates from the pure solvent. In the limit of strong polymer-solvent attractions, the solvent behaves as a glue to stick monomers together, causing a single chain to collapse and multiple chains to aggregate and phase separate.
Detection of passageways in natural foliage using biomimetic sonar
Wang, Ruihao; Liu, Yimeng; Müller, Rolf (IOP, 2022-08-10)
The ability of certain bat species to navigate in dense vegetation based on trains of short biosonar echoes could provide for an alternative parsimonious approach to obtaining the sensory information that is needed to achieve autonomy in complex natural environments. Although bat biosonar has much lower data rates and spatial (angular) resolution than commonly used human-made sensing systems such as LiDAR or stereo cameras, bat species that live in dense habitats have the ability to reliably detect narrow passageways in foliage. To study the sensory information that the animals may have available to accomplish this, we have used a biomimetic sonar system that was combined with a camera to record echoes and synchronized images from 10 different field sites that featured narrow passageways in foliage. The synchronized camera and sonar data allowed us to create a large data set (130 000 samples) of labeled echoes using a teacher-student approach that used class labels derived from the images to provide training data for echo-based classifiers. The performance achieved in detecting passageways based on the field data closely matched previous results obtained for gaps in an artificial foliage setup in the laboratory. With a deep feature extraction neural network (VGG16) a foliage-versus-passageway classification accuracy of 96.64% was obtained. A transparent artificial intelligence approach (class-activation mapping) indicated that the classifier network relied heavily on the initial rising flank of the echoes. This finding could be exploited with a neuromorphic echo representation that consisted of times where the echo envelope crossed a certain amplitude threshold in a given frequency channel. Whereas a single amplitude threshold was sufficient for this in the previous laboratory study, multiple thresholds were needed to achieve an accuracy of 92.23%. These findings indicate that despite many sources of variability that shape clutter echoes from natural environments, these signals contain sufficient sensory information to enable the detection of passageways in foliage.
Generating ultrasonic foliage echoes with variational autoencoders
Goldsworthy, Michael; Mueller, Rolf (2024-01-30)
Navigation through dense foliage presents a fundamental challenge to autonomous systems, and achieving a performance level similar to echolocating bats could have important applications in areas such as forestry and farming. However, the clutter echoes originating from such environments have been difficult to analyze. To study the problem of sonar-based navigation in dense foliage in simulation, an artificial generation system for leaf impulse responses (IRs) based on variational auto-encoders is proposed. The system is to aid the construction of artificial foliage echo environments. A dataset of leaf echoes was collected in an anechoic chamber and convolved with the original signal to estimate the IR of each leaf. A modified version of the conditional variational autoencoder - generative adversarial network (cVAE-GAN) architecture was trained successfully on this dataset to produce a generative model that was conditional on leaf viewing angles, size, and species. The IRs generated by the model capture quantitative and qualitative similarity to the measured IRs. It surpasses the previous state of the art foliage echo model based on reflecting disks. The model’s computational efficiency and its success suggest its potential use for simulating large environments of foliage to study bat biosonar and aid in engineering biomimetic sonar devices.
Mapping mechanical stress in curved epithelia of designed size and shape
Marin-Llaurado, Ariadna; Kale, Sohan; Ouzeri, Adam; Sunyer, R.; Torres-Sanchez, A.; Latorre, E.; Gomez-Gonzales, M.; Roca-Cusach, P.; Arroyo, M.; Treapat, X. (2023-07-07)
The function of organs such as lungs, kidneys and mammary glands relies on the three-dimensional geometry of their epithelium. To adopt shapes such as spheres, tubes and ellipsoids, epithelia generate mechanical stresses that are generally unknown. Here we engineer curved epithelial monolayers of controlled size and shape andmap their state of stress. We design pressurized epithelia with circular, rectangular and ellipsoidal footprints. We develop a computational method, called curved monolayer stress microscopy, to map the stress tensor in these epithelia. This method establishes a correspondence between epithelial shape and mechanical stress without assumptions of material properties. In epithelia with spherical geometry we show that stress weakly increases with areal strain in a size-independent manner. In epithelia with rectangular and ellipsoidal cross-section we find pronounced stress anisotropies that impact cell alignment. Our approach enables a systematic study of how geometry and stress influence epithelial fate and function in three dimensions.
Using covariant Lyapunov vectors to quantify high-dimensional chaos with a conservation law
Barbish, John; Paul, Mark R. (American Physical Society, 2023-11-02)
We explore the high-dimensional chaos of a one-dimensional lattice of diffusively coupled tent maps using the covariant Lyapunov vectors (CLVs). We investigate the connection between the dynamics of the maps in the physical space and the dynamics of the covariant Lyapunov vectors and covariant Lyapunov exponents that describe the direction and growth (or decay) of small perturbations in the tangent space. We explore the tangent space splitting into physical and transient modes and find that the splitting persists for all of the conditions we explore. In general, the leading CLVs are highly localized in space and the CLVs become less localized with increasing Lyapunov index. We consider the dynamics with a conservation law whose strength is controlled by a parameter that can be continuously varied. Our results indicate that a conservation law delocalizes the spatial variation of the CLVs. We find that when a conservation law is present, the leading CLVs are entangled with fewer of their neighboring CLVs than in the absence of a conservation law.

Browse

Recent Submissions