bluuebunny/crossref_metadata_2025_split huggingface dataset:
Reinforcement Learning As an Alternative for Parameter Prediction In Design for Sheet Bulk Metal Forming
Authors: Dworschak Fabian , Sauer Christopher , Schleich Benjamin , Wartzack Sandro 
Published: November 2022

Abstract
This contribution presents an approach and a case study to compare Reinforcement Learning (RL) and Genetic Algorithms (GA) for parameter prediction in Sheet Bulk Metal Forming (SBMF). Machine Learning (ML) and Multi-Objective Optimization (MOO) to provide different points of view for the prediction of manufacturing parameters. While supervised learning depends on sufficient training data, GA lack the ability to explain how sufficient parameters were achieved. RL could help to overcome both issues, as it is independent from training data and can be used to learn a policy leading towards suitable parameter combinations, which can be tracked through the solution space. To probe RL in terms of feasibility for parameter prediction and necessary training effort SBMF serves as an appropriate use case because solution and objective function space are multidimensional, and their relations are challenging for MOO. The results of a Reinforcement Learner and a GA are compared and discussed to answer the question under which circumstances RL can provide an alternative for parameter prediction.
Vibration Energy Harvesting in Light Vehicles: Road Tests and Interpretative Models
Authors: Doria Alberto , Moro Federico , Tommasino Domenico 
Published: November 2022

Abstract
The interest towards piezoelectric energy harvesters has been fostered in recent years by the development of ultra-low power electronics. An experimental analysis approach based on modal testing is here proposed in order to detect the best position on a bicycle of a piezo-harvester. Road tests show that vibrations are in the low frequency range so that the energy harvester has to be properly tuned to get its largest electrical response. Different tuning strategies of the same piezo-harvester, mounted in the best location, are tested and compared. The best setup is selected by considering as figures of merit the average stress inside the piezo-layer of the harvester (which affects its lifespan) and the maximum electric power which can be harvested. These figures are both analytically estimated from the measured power spectral density of the output open circuit voltage, which is experimentally determined. It is shown that the configuration with the largest tip mass provides the largest delivered power. Nevertheless, in order to preserve the device lifespan, harvesters with oscillator and liquid tip mass may represent a good tradeoff between the amount of generated stress and delivered power. The methods presented in this paper can be also adopted for predicting the behavior of harvesters mounted on other light vehicles.
Hybrid Predictive Model for Assessing Spinal Loads for 3d Asymmetric Lifting
Authors: Zaman Rahid , Quarnstrom Joel , Xiang Yujiang , Rakshit Ritwik , Yang James 
Published: November 2022

Abstract
In this study, a hybrid predictive model is used to predict 3D asymmetric lifting motion and assess potential musculoskeletal lower back injuries for asymmetric lifting tasks. The hybrid model has two modules: a skeletal module and an OpenSim musculoskeletal module. The skeletal module consists of a dynamic joint strength based 40 degrees of freedom spatial skeletal model. The skeletal module can predict the lifting motion, ground reaction forces (GRFs), and center of pressure (COP) trajectory using an inverse dynamics based optimization method. The equations of motion are built by recursive Lagrangian dynamics. The musculoskeletal module consists of a 324-muscle-actuated full-body lumbar spine model. Based on the generated kinematics, GRFs and COP data from the skeletal module, the musculoskeletal module estimates muscle activations using static optimization and joint reaction forces through the joint reaction analysis tool. Muscle activation results between simulated and experimental EMG are compared to validate the model. Finally, potential lower back injuries are evaluated for a specific-weight asymmetric lifting task. The shear and compression spine loads are compared to NIOSH recommended limits. At the beginning of the dynamic lifting process, the simulated compressive spine load beyond the NIOSH action limit but less than the permissible limit. This is due to the fatigue factors considered in NIOSH lifting equation.
A Computational Framework for the Evaluation of Resilience in Deep Space Habitat Systems
Authors: Behjat Amir , Ibrahimov Roman , Lenjani Ali , Barket Aaron , Martinus Kathleen , Maghareh Amin , Whitaker Dawn , Bilionis Illias , Dyke Shirley 
Published: November 2022

Abstract
Resilience is a vital consideration for designing and operating a deep space habitat system. The numerous hazards that may affect a deep space habitat and its crew during its lifecycle need to be considered early in the design. Trade-off studies are the typical method used to assess the cost and value of different design choices. Here we develop a modular dynamic computational framework intended for rapid simulation and evaluation of the resilience of different system configurations. The framework uses a system-level phenomeno-logical Markov model of the habitat systems, enabling us to assess multiple habitat configurations and evaluate their performance in the presence of several hazards and user-defined control policies. System fault detection and repairs are modeled. External disturbances, including meteorites impact, temperature fluctuations, and dust, are modeled based on the lunar environment, envisioning a deep space habitat design. We use a reflexive health management subsystem that prioritizes recovery actions based on the robotic agent’s availability to close the loop. In addition to performance, a resilience metric is included to quantify the system’s resilience over the design lifecycle. We illustrate the use of the framework for supporting early-stage design decisions of a habitat system. Our case study focuses on designing the power generation system considering cost and energy efficiency.
On Practical Aspects of Variational Consistency in Contact Dynamics
Authors: Recuero Antonio , Lindsay Alexander 
Published: November 2022

Abstract
Usage of contact mechanics methodologies is a pervasive modeling requirement in dynamic simulations. While for some trivial problems, solutions taken from analytical geometry are available, use of a finite element framework is common to achieve formulation generality. This work explores two dynamic contact formulations: one based on the traditional node-to-segment (NTS) approach, and a variationally consistent segment-to-segment (STS) mortar formulation. The NTS formulation employed here enforces the constraints kinematically (i.e., the interpenetration is enforced to the solver tolerance), whereas the mortar approach uses Lagrange multipliers to enforce the contact constraints. Both approaches are implemented in the open-source finite element framework Multiphysics Object-Oriented Simulation Environment (MOOSE). The results highlight two relevant contact-interface-related dynamic phenomena in finite element simulations. First, stabilization of contact constraints is discussed, taking into account the evolution of the total energy in a benchmark problem. Second, the influence of finite element discretization on both of the aforementioned contact formulations is analyzed by exercising a large-deformation example with continuous relative sliding. Variationally consistent contact approaches such as the mortar formulation lead to improved energy preservation and avoid spurious excitation of the system’s frequencies. This is especially relevant in settings where inertia and vibrations are of importance.
Tablets, Pens, and Pencils: The Influence of Tools on Sketching in Early Stage Design
Authors: Das Madhurima , Huang May , Yang Maria C. 
Published: November 2022

Abstract
Sketching is often used as a tool during idea generation in the early stages of engineering design. At the same time, the rapid increase in remote engineering and design work paves the way for an increased reliance on digital design tools and technologies throughout the design process. Sketch quality and quantity have been shown to be linked to design perceptions and design outcomes. Additionally, sketch quality and style have been linked with the gender of the sketcher. Thus, there are financial, social, and ethical motivations for understanding how digital design tools influence sketching during early stages of engineering design.
In this study, we investigate the influence of hand sketching on a tablet as compared to pencil or pen and paper on early stage sketching for concept generation. We also assess the influence of gender on sketching attributes. We ask three mixed gender groups of participants (using pen, pencil, and a tablet) to sketch concepts in response to an open-ended design prompt. We hypothesize that participants using tablet and pencil will produce fewer drawings than those using pen due to their ability to erase and focus on perfecting drawings, and we expect participants using the tablet to produce cleaner drawings with smoother strokes due to the tablet’s ability to auto-smooth lines. Based on prior literature, we posit that women and gender minorities will produce fewer sketches and use the feathering style of sketching more than men.
We find that sketch quality is highest for pencil drawings and lower for tablet and pen drawings. We also find that concept novelty is negatively correlated with how understandable the concept is. Participants reported that experiences of drawing on paper and on the tablet differ in terms of missing the feeling of friction on the tablet and finding that too many features in a tool can be distracting during concept generation. Interestingly, participants from all groups mentioned that they were not good at sketching. Finally, we find several interesting results with respect to gender differences in sketching performance: namely, women and other gender minorities produced a higher quantity of sketches but did not have any difference in their sketch quality or style.
Practical End-Effector Development Through Task Interface Taxonomy Analysis
Authors: Sharp Andrew , Lovan Austin , Herrera Eduardo , Laske Evan 
Published: November 2022

Abstract
This paper was motivated by the need for reliable robotic agents to maintain spacecraft systems in orbit around the moon, Gateway, and other distant locations, that will be uninhabited for long periods. This effort outlines an analysis methodology for determining the manipulation potential required of a robotic gripper based on a set of tasks. The core of the method is to determine all the possible ways for manipulating the task set interfaces (turning a knob) and defining each of the motions (grasping) in terms of taxonomy (contact, prehensile, motion). By eliminating all but the lowest taxonomy, the minimum manipulation potential can be determined. Using the minimum manipulation potential to choose the gripper means it can be the most robust possible, something vital to long-term distant missions. The methodology was applied to a set of interfaces drawn from !SS and expected Gateway operations. The next step was to apply the methodology to each interface and filter the results. Several interfaces with complicated motions were replaced with simpler interfaces that fulfill the same role. The group also agreed that other interfaces, drills, and fire extinguishers should be specially designed robotic tools. After the analysis, a Commercial off-the-shelf gripper’s fingers were specially designed for the operation interfaces. This approach effectively resulted in a co-design of the gripper and the environment. Several successful demonstrations (first using commercial software and then using ROS, Movelt, and NASA software) were performed to ensure the analysis method and gripper were fully capable.
A pnh-Adaptive Refinement Procedure for Numerical Optimal Control Problems
Authors: Bartali Lorenzo , Gabiccini Marco , Guiggiani Massimo 
Published: November 2022

Abstract
This paper presents an automatic procedure to enhance the accuracy of the numerical solution of an optimal control problem (OCP) discretized via direct collocation at Gauss-Legendre points. First, a numerical solution is obtained by solving a nonlinear program (NLP). Then, the method evaluates its accuracy and adaptively changes both the degree of the approximating polynomial within each mesh interval and the number of mesh intervals until a prescribed accuracy is met. The number of mesh intervals is increased for all state vector components alike, in a classical fashion. Instead, improving on state-of-the-art procedures, the degrees of the polynomials approximating the different components of the state vector are allowed to assume, in each finite element, distinct values. This explains the pnh definition, where n is the state dimension. Instead, in the literature, the degree is always raised to the highest order for all the state components, with a clear waste of resources.
Numerical tests on three OCP problems highlight that, under the same maximum allowable error, by independently selecting the degree of the polynomial for each state, our method effectively picks lower degrees for some of the states, thus reducing the overall number of variables in the NLP. Accordingly, various advantages are brought about, the most remarkable being: (i) an increased computational efficiency for the final enhanced mesh with solution accuracy still within the specified tolerance, (ii) a reduced risk of being trapped by local minima due to the reduced NLP size.
Control Co-Design Under Uncertainties: Formulations
Authors: Azad Saeed , Herber Daniel R. 
Published: November 2022

Abstract
This article explores various uncertain control co-design (UCCD) problem formulations. While previous work offers formulations that are method-dependent and limited to only a handful of uncertainties (often from one discipline), effective application of UCCD to real-world dynamic systems requires a thorough understanding of uncertainties and how their impact can be captured. Since the first step is defining the UCCD problem of interest, this article aims at addressing some of the limitations of the literature by identifying possible sources of uncertainties in a general UCCD context and then formalizing ways in which their impact is captured through problem formulation alone (without having to immediately resort to solution strategies). We first develop a universal UCCD formulation and discuss its fundamental elements. Issues such as the treatment of the objective function, the challenge of the analysis-type equality constraints, and various formulations for inequality constraints are discussed. Then, more specialized problem formulations such as the risk-neutral and risk-averse stochastic, worst-case robust, probabilistic robust, fuzzy expected value, and possibilistic chance-constrained UCCD formulations are presented. Key concepts from these formulations, along with insights from closely-related fields, such as robust and stochastic control theory, are discussed, and future research directions are identified.
Optimizing Intentional Islanding Design Strategies for Enhanced Failure Resilience of Power Systems
Authors: Wu Jiaxin , Chen Xin , Zhang Jie , Wang Pingfeng 
Published: November 2022

Abstract
Establishing cleaner energy generation therefore improving the sustainability of power systems is a crucial task in this century. One of the key strategies being pursued is to shift the dependence on fossil fuel to renewable technologies such as wind, solar, and nuclear. However, with the increasing number of heterogeneous components included in the power system, the complexity of the hybrid energy system becomes more significant, which makes it prone to system failures. The complex system imposes a more stringent requirement of the contingency plan to enhance the overall system resilience against potential system disruptive events. Among different strategies to ensure a resilient system, intentional islanding is commonly applied in practical applications for power systems. In this study, we address the challenges of intentional islanding design considering high penetration of renewable energy sources, and develop a hierarchical clustering-based design method for optimized intentional islanding strategies at the transmission level of a power system. To incorporate the renewable generation that relies on the inverter technology, the frequency measurements are considered to represent the transient response and further used as embedded information in the clustering algorithm. And in the case study, the stabilized post-disruption performance of the modified IEEE-9 bus test system demonstrates the capability of the proposed method for resilience enhancement.
Explore the Beauty of Cam Geometry: The Necessary and Sufficient Design Conditions for the Cam Mechanism With Flat Face Follower
Authors: Chen Yen-Nien , Hsu Kuan-Lun 
Published: November 2022

Abstract
Cam, a mechanism usually used to transform a rotary motion into desired output motion, has been commonly used in modern industry. In practice, a variety of practical methods have been discussed to improve the cam mechanism. In theory, however, the mobility of cam mechanism is seldom mathematically addressed. This paper mathematically discusses the necessary and sufficient design conditions for two kinds of form-closed cam mechanism (a cam with a translating flat-faced follower and a cam with an oscillating flat-faced follower) to be mobile. For the former cam mechanism, the relation between the design conditions and the cam mobility is derived by proving the identical cam contour enveloped by the flat faces. The result shows that the cam mechanism is mobile if and only if it satisfies the design conditions: (a) distance between the two follower flat faces is a constant value (twice base circle radius + total stroke of follower) and (b) summation of the displacement functions being 180 degrees out of phase must be the total stroke of the follower. For the later cam mechanism, the relation is derived by transforming the cam contour into a geometric layout associated with orthoptic curves. The result shows that the cam mechanism is mobile if and only if it satisfies the design conditions: (a) the flat faces are orthogonal, (b) the cycle of the displacement function has to be 180 degrees, (c) low dwell period and high dwell period are identical, (d) difference of rising and returning period has to be twice the stroke of follower, and (e) only one of the rising or falling period can be designed.
Task Modification Scheme for a Collaborative Robot
Authors: Singh Hardeep , Kumar Rajesh , Saha Subir Kumar 
Published: November 2022

Abstract
The paper formulates an algorithm for online modification of the trajectory of a collaborative robot. A sliding mode control algorithm is developed to identify the human intent, under ignorance of the mass and inertia of the element attached to the end effector of the collaborative robot (cobot) and also under ignorance of the human behaviour while applying force to the end effector of the cobot. The sliding mode control methodology allows a human user to modify the trajectory of the cobot while in motion. The controller is developed based on the assumption that the force applied by the human agent is an outcome of a Proportional-Derivative (PD) based dynamics. Although, the human dynamics is assumed, the parameters of the assumed PD control is unknown. However, the boundedness of the human dynamics’ gain matrices lead to the need of a sliding mode controller. Subsequently, the traditional Dynamic Motion Primitives (DMPs) have been used to rapidly train the desired behaviour of a human user. Then, the local planner is modified based on the task modification. The utility of the sliding mode control algorithm and the proposed DMP approach is demonstrated by experiments on the Kinova Gen2 cobotic Arm. Also, the sliding mode control algorithm is shown to converge asymptotically under bounded human action.
Does Synchronous Collaboration Improve Collaborative Computer-Aided Design Output: Results From a Large-Scale Competition
Authors: Deng Yuanzhe , Marion Tucker , Olechowski Alison 
Published: November 2022

Abstract
With the growing demand for distributed collaboration for large and complex design in modern engineering, the collaboration inefficiencies of traditional computer-aided design (CAD) tools are increasingly conspicuous. Emerging cloud-based multi-user computer-aided design (MUCAD) platforms bring a new working style for CAD in the form of real-time synchronous collaboration. Little research exists to characterize collaboration in CAD, and specifically the synchronicity of collaboration has yet to be examined. In this study, we analyzed the backend action logs of 101 teams’ design processes from a large-scale virtual robotic design competition, where all designs were modelled in a commercially available MUCAD platform. Metrics of interest were analyzed with regression and mediation analyses to uncover factors that correlated to a team’s success in the competition. Results show that team size is a positive predictor of team performance. Large teams, which tend to see a large amount of time commitment from members, were more likely to perform more CAD actions and achieve high scores from the competition. This suggests that the benefits of collaboration (e.g., economies of division of labor, learning) outweigh the potential downsides (e.g., coordination overhead, free riding) in this context. While controlling for team size, increased synchronous collaboration occurrences were observed to negatively correlate to teams’ performance — a novel finding which we discuss in detail. Thus, we conclude that although large teams benefited from the MUCAD environment, a tendency for synchronous real-time collaboration did not coincide with higher performance. This study provides important evidence in the ongoing design and innovation research fields aiming to better understand collaboration. Future research should investigate the characteristics of effective collaboration strategies in MUCAD environments to develop best practice for the increasing number of design teams moving to such tools.
Partition-Aware Stability Control for Humanoid Robot Push Recovery
Authors: Song Hyunjong , Peng William Z. , Kim Joo H. 
Published: November 2022

Abstract
For successful push recovery control in response to unpredicted perturbations, a humanoid robot must select the appropriate stabilizing action from a wide range of available strategies. Existing approaches are restricted to a limited range of balancing motions because they are often derived from reduced-order models and ignore system-specific aspects such as swing leg dynamics or joint kinematic/actuation limits. In this work, a novel partition-aware push recovery controller is introduced that can select between the ankle, hip, and captured stepping strategies for balance by evaluating the robot’s center-of-mass (COM) state with respect to different levels of criteria. The criteria are the partition-based stability regions in the augmented COM state space, which are numerically constructed for each balancing strategy. For stepping, both free- and fixed-arm capturability regions were obtained to quantify the effect of arm momenta on balancing capability. The regions are pre-computed for control with an optimization-based method that incorporates whole-body system dynamics, contact interactions with the ground, system-specific characteristics, and requirements of the corresponding strategy. Through simulation experiments, the proposed approach was demonstrated to allow the controller to fully exploit a humanoid robot’s balancing capability and validate the use of pre-computed stability regions as explicit criteria to initiate a proper balancing motion, in contrast to the use of incomplete or implicit criteria in existing controllers.