bluuebunny/crossref_metadata_2025 huggingface dataset:
A Variable Stiffness End-of-Arm Tooling Mechanism to Enhance Dynamic Task Capabilities of Robotic Manipulators
Authors: Jujjavarapu Sri Sadhan , Manjunatha Hemanth , Esfahani Ehsan Tarkesh
Published: November 2022

Variable stiffness end-of-arm actuators can add dynamic manipulation capabilities to stiff manipulators and simultaneously enhance safety. The presence of an elastic element in these actuators can be used for absorbing impact energy; or storing energy and utilizing it for performing explosive tasks. The major challenge with variable stiffness actuators is to control their position and stiffness simultaneously to achieve optimal task performance. In this paper, we present an end-of-arm variable stiffness mechanism (VSM) for performing dynamic tasks. We formulate the task as an optimal control problem and numerically solve for the task-specific stiffness profile. We demonstrate the usability of the optimization problem in exploiting the dynamics of the VSM during an explosive hammering task and demonstrate that the time-varying stiffness profile can store energy and leads to improved task performance. As a result, the hammer attains twice as much velocity with variable stiffness compared to fixed stiffness. The hammering performance is further improved by optimizing task completion time and hammer velocity. Moreover, we demonstrate that the VSM stiffness plays a crucial role in minimizing the impact forces transferred to the robot. This paper presents the optimal trajectory and stiffness profile achieved through numerical optimization and then evaluates the proposed mechanism using experiments.
Dynamic Stiffness Matrix With Timoshenko Beam Theory and Linear Frequency Solution for Use in Compliant Mechanisms
Authors: Ling Mingxiang , Zhou Hao , Chen Liguo
Published: November 2022

The kinetostatic and dynamic formulation of planar-compliant mechanisms is investigated by making use of the dynamic stiffness method based on Timoshenko beam theory. This research is prompted by the significance of considering both the shear deformation and rotary inertia for short and thick flexure beams widely used in compliant mechanisms. We investigate the problem by developing the frequency-dependent dynamic stiffness matrix with the pseudo-static characteristic for a threefold purpose. The first is to show that a closed-form dynamic stiffness matrix of flexure beams in power series of frequency including the shear deformation and rotary inertia is effective that is parameter-insightful and from a computational standpoint concise. Second, a programmable stiffness and mass assembling procedure is developed to build the kinetostatic and dynamic model for compliant mechanisms in a general sense. The third target is to accelerate the calculation efficiency of dynamic stiffness model by employing a linear solution strategy of natural frequencies which is beneficial for parameter optimization iteration. The presented approach is demonstrated by applying the parameter influence analysis and dimension synthesis of a bridge-type compliant mechanism widely used in micro-displacement and/or force amplifications
Teaching Machine Learning for Analysis and Design to Mechanical Engineering Students
Authors: Du Xiaoping , Yin Jianhua
Published: November 2022

Machine learning has become pervasive in mechanical engineering and has been identified as a key skill required by current and future careers. It is crucial to include machine learning in the mechanical engineering curriculum to keep up with the quick-changing pace of the industry. One solution is to offer a new machine learning course specifically targeting mechanical engineering students. In this paper, we discuss the needs and objectives of machine learning education in mechanical engineering. We also show the challenges in applying machining learning in mechanical engineering and how to deal with the challenges in teaching the subject. We then report what we have done in developing a new machine learning course and using machine learning in engineering analysis and design. We demonstrate that using appropriate examples, case studies, and projects is one of the best ways to teach and learn machine learning. Our experiences provide useful information to other instructors who teach or intend to teach the same and related courses.
Non-Smooth Dynamics of Tapping Mode Atomic Force Microscopy
Authors: Belardinelli Pierpaolo , Chandrashekar Abhilash , Alijani Farbod , Lenci Stefano
Published: November 2022

This paper investigates the nonlinear dynamics in tapping-mode atomic force microscopy (AFM) with tip-surface interactions that include Van der Waals and Derjaguin-Müller-Toporov contact forces. We study the periodic solutions of the hybrid system by performing numerical pseudo-arclength continuation. The overall dynamical response scenario is evaluated via bifurcation loci maps in the set of parameters of the discontinuous model. We showcase the influence of different dissipation mechanisms activated when the AFM is in contact or out-of contact with the sample. The robustness of the stable solution in the repulsive regime is studied via local and global analyses. The impacting non-smooth dynamics framed within a higher-mode Galerkin discretization is able to capture windows of irregular and complex motion.
The Amount of Warning Stimulus Time Required to Prepare for a Hard-Breaking Event
Authors: Lenneman Gabe , Lenneman John , Chagdes James
Published: November 2022

Autonomous shuttles are becoming a widely used form of public transportation. While autonomous shuttles have advantages to improve safety, potential riders have expressed concerns about falls and injuries sustained during braking events. In the current study we investigated the effects of multiple warning design parameters, including the modality and the temporal aspects of the warning stimulus, on balance maintenance during a simulated hard braking event. Two warning stimuli were used — an auditory stimulus and a visual stimulus. We investigated the effect of the timing of the warning in an attempt to determine how differences in warning time affect preparation for a simulated hard braking event and subsequent maintenance of balance throughout the event. The warning stimuli were presented 200–750 ms prior to the simulated hard braking. We found that participants had more time to prepare for the simulated braking event when presented with the auditory stimulus versus the visual stimulus. We also found that a minimum of a 600 ms warning was required to produce a significant difference in the amount of time to prepare for the simulated breaking event. The results of this current study suggest that given a future automated shuttle that can detect the surrounding environment and reliably predict conditions, then there is a benefit of presenting preemptive warnings for the purpose of maintaining balance and avoiding falls.
Evaluation of Students’ Learning Through Reflection on Doing Based on Sentiment Analysis
Authors: Wu Yupeng , Ming Zhenjun , Allen Janet K. , Mistree Farrokh
Published: November 2022

In this paper, we address the following question:

How can instructors leverage assessment instruments used to process information gleaned from design, build, and test courses to simultaneously improve student outcomes and assess student learning well enough to improve the course in the future?

A Take-away is an unstructured text written by students in AME4163: Principles of Engineering Design offered at the University of Oklahoma, Norman, USA, to record what they understand by reflecting on authentic and immersive experiences throughout the semester. The immersive experiences include lectures, assignments, reviews, building, testing, and a post-analysis for the design of an electro-mechanical system to address a given customer need. In the context of a Take-away, a student then writes a Learning Statement that is a structured sentence written as a triple, i.e., [Experience - Learning - Value]. Between 2019–2021, we collected about 10,000 Take-aways and Learning Statements from almost 400 students.

In this paper, we address the question from the perspective of students’ feelings and use dictionary-based sentiment analysis to evaluate students’ subjective feelings toward what they are learning. Through quantitative results, we provide evidence-based guidance to instructors on improving the delivery of the course in the future. Our focus in this paper is on explaining the method using data from AME4163. The proposed method is general and can be extended for other types of courses.
Anomaly Detection of Laser Powder Bed Fusion Melt Pool Images Using Combined Unsupervised and Supervised Learning Methods
Authors: Sato Matthew M. , Wong Vivian Wen Hui , Law Kincho H. , Yeung Ho , Yang Zhuo , Lane Brandon , Witherell Paul
Published: November 2022

Laser Powder Bed Fusion (LPBF) is one of the most promising forms of Additive Manufacturing (AM), allowing easily customized metal manufactured parts. Industry use is currently limited due to the often unknown and unreliable part quality, which is largely caused by the complex relationships between process parameters that include laser power, laser speed, scan strategy, and other machine settings. Melt pools can be monitored with a camera aligned co-axially with the laser to monitor part quality. However, the number of images acquired can be large, exceeding hundreds of thousands for a single part. This paper investigates how the K-Means algorithm, an unsupervised machine learning method, can be used to cluster images of melt pools based on their shape, including undesirable anomalous melt pools. Another unsupervised learning method in this paper is the U-Net autoencoder, which identifies anomalous melt pools by identifying images with a large reconstruction loss. The K-Means clustering or autoencoder provides labels that can be used for training a convolutional neural network image classifier. The image classifier can then be used to identify anomalous melt pools during the LPBF process. This paper provides a first step for real-time process control of the LPBF process by demonstrating how anomalous melt pools can be automatically identified in real-time.
Considerations for the Testing and Validation of a Mobile Damping Robot for Overhead Power Lines
Authors: Choi Andrew , Kakou Paul-Camille , Barry Oumar
Published: November 2022

While analyses have been performed for fixed masses on power line conductors, they have not been in the context of interactions between the conductor and a mobile damping robot (MDR). There is a need to explore the potential impact of the MDR on the power line and the resulting implications for the MDR’s development as current methods of vibration control do not adequately address fatigue failure caused by wind-induced vibrations (WIV). Fixed passive vibration absorbers (FPVAs) are widely used on power lines, but they are inherently limited by their fixed nature since changes in wind conditions affect absorber performance as conductor mode shapes change. An MDR can overcome these limitations by actively transporting a passive absorber to conductor antinodes where the absorbers can most effectively remove energy from the system. In this paper, we experimentally investigate the effects of an untuned suspended mass on the conductor as an analog for the MDR, and we perform numerical analysis in MATLAB using equations of motion obtained via Hamilton’s Principle. The insights gained from this work lay a foundation to guide future experiments that will better define the operating conditions of the MDR and lead to the creation of an appropriate control framework.
Origami Claw Tessellation and Its Stacked Structure
Authors: Xing David , You Zhong
Published: November 2022

While many classical origami tessellations possess important kinematic properties, including flat-foldability and rigid-foldability, these patterns only possess one DOF. Stacking sheets of these origami tessellations is a fabrication technique to construct foldable meta-materials. In this paper, we present a novel origami pattern called the origami claw tessellation (OCT). This pattern not only preserves the kinematic properties found in classical origami tessellations but also possesses multiple DOFs. We show the OCT is flat-foldable and rigidly foldable through kinematic analysis. Every OCT described in this paper is an M × N grid of claw units, each of which has three DOFs. The exact number of DOFs a given M × N OCT possesses is computed using a numerical approach. When both M and N are at least two, the OCT possesses five DOFs. Each individual DOF is named and analyzed. Furthermore, copies of single-sheet OCTs can stack to form a cellular meta-material. In the simplest case, stacking a claw unit with a copy reduces the number of DOFs from three to one. Additionally, for any positive even L, the L-layer stacked M × N OCT has one DOF, showing that multiple sheets of OCTs, each with multiple DOFs, can stack to form a one DOF structure.
Programming Surfaces by Bistable Star Tiling Kirigami
Authors: Toyoka Ryuya , Tachi Tomohiro
Published: November 2022

Auxetic kirigami patterns have been used for fabricating given target surfaces from flat sheets. In particular, bistable kirigami patterns are useful as they keep the structure stable at the target shapes. In this paper, we propose a family of new bistable kirigami patterns that potentially resolves the limitation of existing bistable kirigami, which are (1) the small range of the scale factor that limits the target surface and (2) the asynchronous deployment with multiple locally stable states other than the target surface. The proposed patterns are obtained by adding bistability-inducing bar elements to star tiling kirigami patterns. The experiments show the improvement of synchronization of our structure. We propose an optimization-based computational design method to obtain curved surfaces by arranging the kirigami patterns and the bar elements such that each unit expands to the given scale factor. We applied this method to several surfaces and validated the physical prototypes.
A Generalized Torsional Dynamics Formulation for Multi-Mesh Gear Trains With Clearances and Torque Fluctuations
Authors: Donmez Ata , Kahraman Ahmet
Published: November 2022

Vibro-impacts of gears are common in various automotive geared drivetrains used in engines and transmissions, causing excessive noise issues coined as gear rattle. External torque fluctuations lead to contact loss at gear mesh interfaces of such systems to result in sequences of impacts. Drivetrain architectures used in these applications vary widely, especially in diesel engine timing gear systems, requiring specific models for each drivetrain configuration considered. This makes dynamics evaluations of different design concepts a difficult task. In this paper, a generalized unified formulation will be presented that is capable of deriving equations of motion of any drivetrain system. This generalized formulation allows a systematic and coding-friendly derivation of system matrices for rattle simulation of any multi-mesh system. The proposed methodology will be applied to example 3-axis idler and torque split configurations to demonstrate its effectiveness. A criterion will be devised to establish boundaries between no-impact and impact motions. No-rattle boundaries will be plotted on parameter maps for example 3-axis meshing configurations. A piecewise linear solution technique will be used to solve governing equations to study the coupling of vibro-impacts at gear contact interfaces. Finally, the multi-mesh discrete model of this configuration will be verified through comparisons to its deformable-body counterpart.
Designing Multistable Mechanisms Using Quadrilateral Boundary Rigid Origami
Authors: Miyajima Yuki , Lee Munkyun , Tachi Tomohiro
Published: November 2022

Multistable origami and its snapping behaviors between the folded states have attracted scientists’ and engineers’ attention as the building block for the design of mechanical devices and metamaterials. We propose a novel method for designing origami-based multistable structures, by which we mean (1) to obtain the prescribed overall motion and (2) to control the stiffness of snapping provided by the elastic strain. We solve this design problem by first representing the desired motion with linkage structures with quadrilateral holes, called the frames, and then filling the frames with origami modules, called quadrilateral boundary modules. By introducing an intentional incompatibility between the motions of the frames and the modules, we design the snapping behavior that follows the linkage motion. We provide the representation model to evaluate the incompatibility and propose an optimization-based framework for the design. We also validate our design through bar-and-hinge analysis and experiments using physical prototypes.
Deep Ensembles for Modeling Uncertain Phase Constraints In Compositionally Graded Alloy Design
Authors: Allen Marshall , Arroyave Raymundo , Malak Richard
Published: November 2022

Compositionally graded alloys (CGAs) are a specific class of multi-material functionally graded materials (FGMs) that use spatial variations in alloy composition to meet competing performance requirements in at different locations regions of a single part. Directed energy deposition (DED) metal additive technology has enabled the manufacturing of CGAs, but design these alloys remains a challenge. One important challenge is to avoid alloy compositions that result in the formation of deleterious phases during manufacturing. While designers can use CALculation of PHAse Diagram (CALPHAD) models predict the presence of deleterious phases, these calculations tend to be too costly to incorporate directly in a computational design framework. In this work, we apply deep ensembles, or ensembles of deep artificial neural networks (ANNs), to learn a surrogate model of deleterious phase boundaries based on CALPHAD simulations. The learned model is used as a constraint by a path planning algorithm to identify gradient pathway through metal composition space that can be successfully manufactured. We demonstrate the deep ensemble approach in the Fe-Ni-Cr-Ti quaternary system and benchmark it against individual ANNs and a K-nearest neighbors (KNN) approach reported previously. Additionally, we investigate the use of the predicted class probability threshold as a means for understanding surrogate model uncertainty and reasoning about the design space. Lastly, we illustrate how varying the thresholds on constraint probability results in a trade off between manufacturing risk and identifying solutions through narrow passageways.
Does Design Proficiency Matter in Engineering Design Teams? A Computational Model and Experiments
Authors: Brownell Ethan , Cagan Jonathan , Kotovsky Kenneth
Published: November 2022

A cognitively inspired, agent-based model of engineering design proficiency is introduced in this work. Proficiency is modeled using move selection heuristics and problem space search strategies, both of which were extracted from a prior human subjects study. Agent behavior in the Proficient Simulated Annealing Design Agents (PSADA) Model is validated against human designer behavior and performance. A multi-agent model of design teams is then produced with Monte Carlo simulation methods and it confirmed the previous finding that the most proficient member of a configuration design team has the largest impact (positive or negative) on team performance. Further experiments are conducted with different team characteristics. It is found that the effect of individual team member proficiency is reduced in larger teams, but the highest proficiency team member likely remains the most influential. Nominal teams, where team members do not interact and where the best design is taken as the team output, were found to outperform interacting teams. This was due to high proficiency agents making poor decisions during interactions by switching away from their designs to the designs of their lower proficiency teammates. Conclusions about the role that individual team member proficiency plays in engineering design teams is discussed.
Operational Considerations for Active Electromagnetic Suspension Systems
Authors: Galluzzi Renato , Ramirez-Mendoza Ricardo A. , Circosta Salvatore , Bonfitto Angelo , Amati Nicola , Tonoli Andrea
Published: November 2022

The use of active suspensions has been rekindled by increasingly stringent comfort requirements in light of the diffusion of electric and safe-driving vehicles. In particular, electromagnetic solutions can guarantee intrinsic reversibility and regenerative operation, which would allow for energy harvesting and/or reuse on active quadrants. However, the use of non-ideal actuators has an impact in the regenerative and active capabilities of any active damping solution. To address this shortcoming, this work aims at validating optimal active operation. Hence, a linear quadratic regulator is used to tune a linear optimal control strategy in a quarter-car model. This plant is equipped first with an ideal actuator. Later, this device is enhanced with multiple non-ideal features: inertial and friction contributions of the moving parts, compliance of the parts that exchange forces with the suspension and the resistive load of the motor windings. Details regarding comfort, handling, are compared. Then, the realistic actuator model is compared for comfort- and handling-oriented control tasks. For both strategies, operational aspects like dynamic performance, control calibration, suspension duty cycle, damping force-speed characteristics and power absorption are discussed. It is demonstrated that a significant part of the control effort is used to mitigate non-ideal features of the realistic actuator.
Effect of Varied Additives on the Texture and Shape Stability of 3d Printed Mashed Potato and Pumpkin
Authors: Chirico Scheele Stefania , Egan Paul F.
Published: November 2022

Three-dimensional (3D) food printing has grown exponentially in recent years due to its capabilities for customized food designs, minimal food waste, and personalized nutrition. A current challenge in 3D food printing is the design of extrudable food materials that enable customized shape fabrication and retention due to the complexity of food matrices. Additives such as starches and gums have been employed to improve food mechanical properties and thus printability, however, few studies have investigated further food additives that may affect the manufacturability and lifecycle of printed foods. This study investigates the effect of pea protein, corn starch, citric acid, and butter powder when added in different concentrations on the firmness, total work, and shape stability of mashed potato and pumpkin. Results suggest that pea protein addition increases the firmness and total work, citric acid decreases them, while butter powder and corn starch affect mechanics differently depending on the base material. Overall, the addition of butter powder had the best results for printability and shape retention, with a complete design space analysis suggesting prints should have firmness of at least 150g for high shape stability. Knowledge of food additive effects on texture and shape retention of foods can positively influence the design and manufacturing of personalized foods by reducing reliance on trial-and-error design processes to design new food products.
Dynamics of Interface Relocation in an Electroacoustic Su-Schrieffer-Heeger Topological Lattice
Authors: Kuchibhatla Sai Aditya Raman , Leamy Michael J.
Published: November 2022

We propose a reconfigurable, electroacoustic topological insulator inspired by the Su-Schrieffer-Heeger (SSH) lattice and then study dynamically-switched topological interfaces. The electroacoustic unit cell design is composed of a host structure with bonded piezoelectric attachments shunted via negative capacitance circuits. We first identify topological behavior via band structure calculations associated with the unit cell. By selectively modifying the elastic properties of the repeated unit cells via attached shunted circuits, we then join two one-dimensional lattices with mirror symmetry to yield a topologically-protected interface mode. We compute the characteristic frequencies and compare the harmonic response of the structures with and without an interface to show amplitude localization at an interface for a frequency within a topological band gap. Control of the shunted circuits using electrical switches allows for reconfiguration of the structure, thereby permitting relocation of the topological interface at an arbitrary time instant. We present numerical studies simulating the diffusion of energy as the interface is shifted from one location to another, exploiting topological-protection to ensure minimal energy loss in spite of a non-smooth change in the elastic properties of the lattice. Our work demonstrates robust, movable interfaces in one-dimensional elastic media which may find applications in communication devices and acoustic tweezing.
Real-Time Simulation of Ground Vehicles on Deformable Terrain
Authors: Serban Radu , Taves Jay , Zhou Zhenhao
Published: November 2022

Sophisticated modeling and simulation, based on rigid and flexible multibody dynamics, are nowadays a standard procedure in the design and analysis of vehicle systems and are widely adopted for on-road driving. Off-road driving for both terrestrial wheeled and tracked vehicles, as well as wheeled and legged robots and rovers for extra-terrestrial exploration pose additional modeling and simulation challenges, a primary one being that of the vehicle-terrain interaction, modeling of deformable terrain, and terramechanics in general.

Techniques for modeling deformable terrain span an entire range varying in complexity, representation accuracy, and ensuing computational effort. While formulations such as fully-resolved granular dynamics, continuum modeling of granular material, or finite element can provide a high level of accuracy, they do so at a significant cost, even when the implementation leverages parallel computing and/or hardware accelerators. Real-time or faster than real-time terramechanics is a highly desired capability (in applications such as training of autonomous vehicles and robotic systems) or critical capability (in applications such as human-in-the-loop or hardware-in-the-loop).

We present a real-time capable deformable soil implementation, extended from the Soil Contact Model (SCM) developed at the German Aerospace Center which in turn can be viewed as a generalization of the Bekker-Wong and Janosi-Hanamoto semi-empirical models for soil interaction with arbitrary three-dimensional shapes and arbitrary contact patches. This SCM implementation is available, alongside more computationally intensive deformable soil representations, in the open-source multi-physics package Chrono. We describe the overall implementation and the features of the Chrono SCM model, the efficient underlying data structures, the current multi-core parallelization aspects, and its scalability properties for concurrent simulation of multiple vehicles on deformable terrain.
Assessing the Sensitivity on State-Space Variables Through Isometric and Anisometric Integrity Measures
Authors: Belardinelli Pierpaolo , Lenci Stefano
Published: November 2022

A recurrent task in mechanical and structural engineering is to estimate safe working regimes. For dynamical systems, it means to guarantee that the designed motion will be maintained despite perturbing events. Therefore, in order to assess the practical stability, a global analysis must be performed by employing basins of attraction. The phase-space topological study of their size, shape and compactness, it quantifies and relates back to the robustness of the attractive state within a multi stable scenario. This work illustrates how isometric and anisometric integrity measures could be utilized to assess the practical stability of a dynamical system. We document the challenges when tackling a multidimensional problem and a dissimilar sensitivity with respect to different state-space variables.
Artificial Intelligence Based State of Health Estimation With Short-Term Current Profile in Lead-Acid Batteries for Heavy-Duty Vehicles
Authors: Luciani Sara , Feraco Stefano , Bonfitto Angelo , Amati Nicola , Tonoli Andrea , Quaggiotto Maurizio
Published: November 2022

The State of Health (SOH) estimation for automotive batteries is currently assessed with different techniques which may involve long testing procedure or require costly hardware to be implemented. This paper aims at contributing to this domain by exploiting the response of a lead-acid battery with respect to a short-term current profile using an Artificial Neural Network (ANN) classifier for SOH estimation. The method is applicable onboard the vehicle and no additional instrumentation is required on the retained vehicle. The design and validation of a SOH method with a short-term current profile using Artificial Intelligence (AI) in lead-acid batteries, which are commonly used in heavy-duty vehicles for cranking and cabin systems, are presented. The paper validates the considered approach with experimental data, which are representative of actual vehicle operations. In detail, the paper describes the retained hardware and software architectures and the design procedure related to the proposed SOH estimation technique based on AI. The retained lead-acid battery 12 V 225 Ah battery used in actual heavy-duty vehicles. The proposed AI-based algorithm relies on an ANN used for SOH classification, which is trained with a sufficient amount of collected data. Specifically, the proposed SOH classifier can process features extracted from buffers of input data that are recorded onboard. When considering the validation dataset only, the resulting SOH estimation algorithm can classify the battery SOH with an overall accuracy equal to 96.7%.
Jamming-Reinforced Inflatable Beams for Mobile Robotics
Authors: Weston-Dawkes William P. , Tolley Michael T.
Published: November 2022

Inflatable limbs have the potential to enable robots that are lightweight, deployable from a small package, and can navigate confined spaces. However, the load bearing capability of slender inflatable tubes is limited by buckling loads that occur with modest loading. In this work we explore the use of inflatable beams with layer-jamming skins that are compliant while deflated, but provide enhanced stiffness and load bearing capabilities when inflated. We first describe a fabrication method for the inflatable beam and present a model to predict the length scales at which jamming contributes to the overall performance of the inflatable beam. We then measure the performance of prototype jamming limbs using bending and tip-deflection tests to evaluate the ability of the jamming skin to mitigate the buckling failure inherent in inflatable beams. Finally, we incorporate four jamming-reinforced limbs into a mobile robotic system. To demonstrate the benefits of a hybrid rigid-soft mobile robotic system, we experimentally test the trade-off between walking more quickly with pressurized limbs (1 cm/s), versus navigating confined spaces with depressurized limbs (0.6 mm/s). Incorporating jamming reinforcement into inflatable beams provides improved stiffness and graceful failure rather than the catastrophic buckling that normally characterizes inflated cylindrical beams. Inflatable limbs augmented with pressure-based jamming are not drop-in replacements for rigid links. However, our results show that using layer jamming to provide nuanced soft to stiff capabilities may be valuable for specific environmental constraints.
Collision-Free Dynamic Window Approach for Moving Obstacles
Authors: Xi Zhimin
Published: November 2022

Dynamic window approach (DWA) is known as collision-free for static obstacles with proper design. Under static operation conditions, if there is no admissible velocity within the velocity dynamic window, the agent reduces the velocity as much as possible. As such, the agent is collision-free as long as the projected time is large enough to make the agent fully stop. Existing work of the DWA considering moving obstacles demands for trajectory prediction of moving obstacles so that admissible velocities of the agent can be determined. However, there is no guarantee for any trajectory prediction method that the predicted trajectory is accurate even considering prediction confidence. Hence, collisions are reported for the DWA considering moving obstacles. This paper proposes collision-free DWA for moving obstacles through determination of the mobility of moving obstacles within each control cycle. Algorithms are proposed to efficiently determine admissible velocity of the agent considering the mobility of moving obstacles. Collision-free results are demonstrated through two case studies including an open space navigation and a merging lane navigation through the comparisons with the DWA based on the trajectory prediction.
Analytic Velocity Obstacle for Efficient Collision Avoidance
Authors: Xi Zhimin , Torkamani Elnaz Asghari
Published: November 2022

Velocity obstacle is one of popular reactive navigation algorithms for path planning of autonomous agents. The collision-free property can be guaranteed if the agent is able to choose a velocity outside the velocity obstacle region under the assumption that obstacles maintain a constant velocity within the control cycle time of the agent. To date, selection of the optimal velocity relies on either sampling or optimization approaches. The sampling approach can maintain the same amount of computation cost but may miss feasible solutions under collision risks with insufficient number of samples. The optimization approach such as the linear programming demands for convexity of the constraints in the velocity space which may not be satisfied considering non-holonomic agents. In addition, the algorithm has varying computation demand depending on the navigation situation. This paper proposes an analytic approach for choosing a candidate velocity rather than relying on the sampling or optimization approaches. The analytic approach can significantly reduce computation cost without sacrificing the performance. Agents with both holonomic and non-holonomic constraints are considered to demonstrate the performance and efficiency of the proposed approach. Extensive comparison studies with static, non-reactive, and reactive moving obstacles demonstrate that the analytical velocity obstacle is computationally much more efficient than the optimization based approach and performs better than the sampling based approach1.
Comparing Nominal and Interacting Sub-Structured Teams in an Interdisciplinary Engineering Design Task
Authors: Gyory Joshua T. , Soria Zurita Nicolás F. , Cagan Jonathan , McComb Christopher
Published: November 2022

Teams are common throughout engineering practice and industry when solving complex, interdisciplinary problems. Previous works in engineering problem solving have studied the effectiveness of teams and individuals, showing that in some circumstances, individuals can outperform collaborative teams working on the same task. The current work extends these insights to novel team configurations in large interdisciplinary teams. In these team configurations, the meta-team as a whole can interact but the sub-teams within them may or may not. Here, team performance and process are studied within the context of a complex drone design and path-planning problem. Via a collaborative research platform called HyForm, communication and behavioral patterns can be tracked and analyzed throughout problem solving. This works shows that nominal sub-structured teams, where members work independently, outperform interacting sub-structured teams. While problem-solving actions remain consistent, communication patterns significantly differ, with nominal sub-structured teams communicating significantly less. Questionnaires reveal that the manager roles in the nominal sub-structured teams, which are more central in communication and information flow, experience a greater cognitive and workload burden than their counterparts in the interacting sub-structured teams. Overall, this work adds to the literature on nominal versus interacting problem-solving teams, extending the finding to larger, interdisciplinary teams.
Design, Analysis, and Prototyping of a Novel Single Degree-of-Freedom Index Finger Exoskeleton Mechanism
Authors: Xu Wenda , Liu Yujiong , Ben-Tzvi Pinhas
Published: November 2022

This paper presents a novel index finger exoskeleton mechanism for patients who suffer from brachial plexus injuries, which takes advantage of our previously proposed rigid coupling hybrid mechanism (RCHM) concept used for robotic tail mechanisms. The core idea of this concept is to drive the (i+1)-th link using the motions of the i-th link, instead of the traditional way of transmitting motion directly from the base. This specific configuration allows designing a single degree of freedom (DOF) bending mechanism using a low-profile rack and pinion mechanism and makes the proposed exoskeleton system compact, lightweight, and portable, which are highly desired features for daily usages of exoskeleton gloves. The mechanism is optimized to mimic the grasping motions of human fingers and the sensitivity analysis of its critical design variables is then conducted to explore the performance of the optimization results. The results show that for the current design, the tip position accuracy is mainly affected by the distance between the rack and the corresponding joints. A proof-of-concept prototype was built to verify the novel mobility of the proposed mechanism and to evaluate its performance on a human finger. The index finger exoskeleton experiments demonstrate the new mechanism’s ability to grasp small objects.
Design of Three-Dimensional Bi-Continuous Silicon Based Electrode Materials for High Energy Density Batteries
Authors: Zheng Zhuoyuan , Liu Zheng , Wang Pingfeng , Li Yumeng
Published: November 2022

Silicon-based anode is a promising candidate for next generation lithium-ion batteries (LIBs) with improved energy and power density. However, the practical application of Si anode is hindered by their major reliability issue that Si experiences significant volume change during its lithiation/delithiation cycles, leading to high stress, degradation, and pulverization of the anode. With the development of advanced electrode fabrication technologies, structured Si anodes with delicately designed architectures have been proposed. This study focuses on five triply periodic minimal surface (TPMS) based 3D bi-continuous porous Si anodes, which consist of the nano structured metal scaffolds and conformally coated Si layers and explores their lithiation-induced stresses via numerical methods. The multi-physics based finite element (FE) models are firstly built to simulate the deformation and stress of Si anodes during lithiation processes. Afterwards, the Gaussian Processes (GP) based surrogate model is developed to assist the design optimization of the Si anodes within the design space. It is found that, the inverse FCC and diamond surface-based Si anodes show better performances with the lowest stress concentration. In addition, with the decrease of Si phase volume fraction and increase of scaffold fraction, the stresses can be further reduced.
Visual Reasoning for Design by Analogy: Fuse Visual and Semantic Knowledge
Authors: Jin Yan , Zhang Zijian
Published: November 2022

Design by analogy is a design ideation strategy to find inspiration from source domains to generate design concepts in target domains. Recently, many computational methods were proposed to measure similarities between source domains and target domains to build connections between them. However, most existing methods only explore either visual or semantic cues of the concepts in source and target domains but ignore the integration of both modalities. In fact, humans have remarkable visual reasoning ability to transfer knowledge learned from objects in familiar categories (source domains) to recognize objects from unfamiliar categories (target domains). In this paper, we propose a visual reasoning framework to support design by visual analogy. The challenge of this research is how computation methods can mimic the process of humans’ visual reasoning, which fuses visual and semantic knowledge. In the framework, the convolutional neural network (CNN) is applied to learn visual knowledge from objects in familiar categories. The hierarchy-based graph convolutional network (HGCN) is proposed to transfer learned visual knowledge from familiar categories to unfamiliar categories by their semantic distances. Finally, the unfamiliar objects can be reasoned and recognized based on the transferred visual knowledge. Extensive experimental results on one mechanical component benchmark dataset demonstrate the favorable performance of our proposed methods.
Conceptual Design of Foldable Truck Trailer
Authors: Temmerman Sjors , Sharma Saurav , Kumar Siddhant , Jovanova Jovana
Published: November 2022

Commercial trucks are not always transporting goods at full capacity, which means that the box-shape trailer is partially occupied or even completely empty on occasions. In this work, we explore foldable trailer design to improve truck efficiency and sustainability. To achieve the folding feature we explore active compliant hinges in origami-inspired design.

Active origami engineering relies on two-dimensional smart hinges able to interact in a three-dimensional design without external forces. The truck trailer is envisioned to be able to fold itself into an aerodynamic wedge shape when the trailer is (partially) empty. This design can be made lighter and more environmentally friendly than the original trailer design and the use of such a foldable trailer can reduce fuel consumption. Smart material layered hinges made from dielectric elastomers actuate the plastic faces of the construction to achieve the folding and deploying of the adaptable trailer design. Initial studies of geometry, materials, and actuation power as well as the potential for energy savings are presented. The drag coefficient is reduced by 33.77% when folded into an aerodynamic shape which leads to an annual decrease in fuel consumption of 6.32% for the average truck that drives around empty 20% of the time and 15.8% for one way transport trucks that drive around empty 50% of the time.
TRIZ Mapping and Novelty Detection of Engineering Design Patents Using Machine Learning
Authors: Hall Sawyer , Mollan Calahan , Pandey Vijitashwa , Mourelatos Zissimos
Published: November 2022

Published resources such as technical literature and patent documents are extremely useful in engineering design and form an important input to methods such as TRIZ. Often, design engineers will investigate these resources when working on new design problems. Aside from getting technical information and even direct design solutions, they may find the design principles used in each patent document a useful design stimulus. Unfortunately, patents are not classified based on such “design useful” characterizations. Using unsupervised clustering and Latent Dirichlet Allocation, this paper investigates four hypotheses using engineering patents in informing TRIZ based design. It first investigates the optimal number of TRIZ topics present in a corpus. Using this information, it attempts to map the TRIZ methods to the individual patents using unsupervised machine learning. Both rejected and accepted patents are then tested to determine if an autoencoder can successfully differentiate between the two, just from the text of the document. The autoencoder reconstruction errors of “Vehicle Brake Control” patents are also examined for possible correlation between reconstruction error and patent citation count. Finally, by combining the TRIZ clustering and the trained autoencoder, we show that high reconstruction error patents may be harder to assign to TRIZ methods than low reconstruction error patents.
Modeling and Validation of Compliant Mechanisms and Soft Robots in MATLAB Simscape
Authors: Contreras-Esquen Andrea , Martinez Jose Bonilla , Garcia Martin , Moghadam Amir Ali Amiri , Tekes Ayse
Published: November 2022

This paper reports on Simscape modeling and experimental validation of compliant mechanisms and soft robotic systems. During the past decade, there has been a lot of advancements in terms of the development of novel soft robotic systems, actuators, and sensors. However, efficient modeling of these systems is still a challenge for researchers. This is due to the existence of nonlinearities such as large deformation in the structure of soft robotic systems and compliant mechanisms. While analytical models can be used for conventional rigid robots, the application of these models for soft robots is limited. Simscape is an efficient tool for modeling soft robotic systems and compliant mechanisms as it can take into account large deformation. To validate the developed Simscape models we used a 6 DOF electromagnetic position sensor for each of the example systems such as a compliant bistable mechanism, compliant linear actuator, and a soft Delta robot.
Particle Swarm Optimization / PID-Computed Torque Control for a Manipulator
Authors: Badkoobehhezaveh Hedieh , Fotouhi Reza , Zhang Qianwei
Published: November 2022

Dynamic modelling and motion control of a newly developed 5-DOF robot manipulator is presented here. Optimized PID gains are obtained using PSO (Particle Swarm Optimization) method. a PID and a PID-CTC (Computed Torque Control) controller with optimized gains are examined for reference trajectory tracking control for the manipulator. Controllers’ performance for unit step inputs are evaluated using computer simulations. It is shown that this PSO optimized gain PID-CTC controller makes the manipulator follow its trajectory with no steady-state error. Controllers lead to small tracking errors, which is suitable for intended application; the PID-CTC controller provides better overall transient responses. The simulation model presented here, can be used for other applications. Contributions of this paper is mostly on application of PSO for the large 5-DOF manipulator with uncertain settings.
Nonlinear Dynamics of Human Balancing
Authors: Tóth Rudolf R. , Molnar Csenge A. , Stepan Gabor
Published: November 2022

A nonlinear mechanical model of human balancing is established with the help of ankle stiffness measurements available in the literature. The stability analysis of quiet standing is studied analytically in the presence of the neural reaction delay. This analysis is followed by the investigation of the nonlinear dynamics: increasing reaction time results in supercritical Hopf bifurcation. Still, since saturation limits the control torque, loss of balance occurs for large enough perturbations. The insights provided by the nonlinear analysis of human balancing help in preventing and predicting accidents, particularly for elderly people.