bluuebunny/crossref_metadata_2025_split huggingface dataset:
Deep Reinforcement Learning Enhanced Convolutional Neural Networks for Robotic Grasping
Authors: Fang Jianhao , Hu Weifei , Wang Chuxuan , Liu Zhenyu , Tan Jianrong 
Published: November 2021

Abstract
Robotic grasping is an important task for various industrial applications. However, combining detecting and grasping to perform a dynamic and efficient object moving is still a challenge for robotic grasping. Meanwhile, it is time consuming for robotic algorithm training and testing in realistic. Here we present a framework for dynamic robotic grasping based on deep Q-network (DQN) in a virtual grasping space. The proposed dynamic robotic grasping framework mainly consists of the DQN, the convolutional neural network (CNN), and the virtual model of robotic grasping. After observing the result generated by applying the generative grasping convolutional neural network (GG-CNN), a robotic manipulation conducts actions according to Q-network. Different actions generate different rewards, which are implemented to update the neural network through loss function. The goal of this method is to find a reasonable strategy to optimize the total reward and finally accomplish a dynamic grasping process. In the test of virtual space, we achieve an 85.5% grasp success rate on a set of previously unseen objects, which demonstrates the accuracy of DQN enhanced GG-CNN model. The experimental results show that the DQN can efficiently enhance the GG-CNN by considering the grasping procedure (i.e. the grasping time and the gripper’s posture), which makes the grasping procedure stable and increases the success rate of robotic grasping.
Multi-Material and Multi-Joint Topology Optimization for Lightweight and Cost-Effective Design
Authors: Crispo Luke , Roper Stephen William Knox , Bohrer Rubens , Morin Rosalie , Kim Il Yong 
Published: November 2021

Abstract
Lightweighting and cost reduction are overarching research themes in aerospace and automotive industries, leading to the exploration of new materials, advanced manufacturing methods, and design optimization algorithms. Multi-material topology optimization is an important tool that can generate unconventional designs leveraging the differing mechanical properties of multiple material types to increase performance. However, these approaches do not consider joining design during optimization, which can ultimately result in higher cost, worse performance, and unrealistic designs that must be altered in the interpretation stage. This work presents a multi-material and multi-joint topology optimization methodology that models joints at the interfaces between dissimilar materials, controls the joining pattern using joint design variables, and reduces cost through the addition of a joining cost constraint. Design variable interpolation schemes, interface detection for unstructured meshes, and sensitivity analysis are outlined in detail in this paper. The approach is applied to a real-world rocker arm geometry to demonstrate the importance of considering joints during multi-material topology optimization. The results of the numerical example indicate that the methodology can successfully detect interfaces in unstructured meshes and strategically place joints to maximize stiffness of the structure. A parameter study of various joining cost constraint levels illustrates how the optimizer alters part topology and joining design to reduce cost.
Remote Emergency Braking System for Autonomous Racing Electric Vehicles
Authors: Sorrentino Gennaro , Danese Luca , Circosta Salvatore , Feraco Stefano , Khan Irfan , Luciani Sara , Bonfitto Angelo , Amati Nicola 
Published: November 2021

Abstract
Advanced brake assist systems can avoid road accidents since the vehicles impact speed can be significantly reduced. To this end, different autonomous emergency braking systems are designed for recent vehicles on the market. This paper presents a pneumo-hydraulic Emergency Braking System (EBS) for autonomous racing vehicles. The purpose of the system is safely stopping the vehicle in case of any failures during autonomous driving. Failures can be detected both by the autonomous system itself and by human supervisors. The actuation system involves passive energy storage of compressed air to directly activate the hydraulic braking lines through pneumo-hydraulic pressure intensifiers. The coupling component between failures detection and actuation is a normally-open solenoid valve. The system is designed to respect deceleration and actuation time requirements, together with packaging constraints due to integration in an existing racing prototype. Specifically, the system requirements are specified by the racing competition rules: the overall reaction time of the retained EBS must be lower than 0.2 s, and the actuated mean deceleration must be greater than 8 m/s2 on a dry track surface while keeping stable driving conditions. The validation and tuning of the system is performed in a simulated environment. Therefore, an extensive experimental validation of the system is required in the real applications.
Multifidelity Uncertainty Quantification for Online Simulations of Automotive Propulsion Systems
Authors: Yang Hang , Gorodetsky Alex , Fujii Yuji , Wang Kon-Well 
Published: November 2021

Abstract
The increasing complexity and demanding performance requirement of modern automotive propulsion systems necessitate more intelligent and robust predictive controls. Due to the significant uncertainties from both unavoidable modeling errors and probabilistic environmental disturbances, the ability to quantify the effect of these uncertainties to the system behaviors is of crucial importance to enable advanced control designs for automotive propulsion systems. Furthermore, the quantification of uncertainty must be computationally efficient such that it can be conducted on board a vehicle in real-time. However, traditional uncertainty quantification methods for complicated nonlinear systems, such as Monte Carlo, often rely on sampling — a computationally prohibitive process for many applications. Previous research has shown promises of using spectral decomposition methods such as generalized Polynomial Chaos to reduce the online computational cost of uncertainty quantification. However, such method suffers from scalability and bias issues. This paper seeks to alleviate these computational bottlenecks by developing a multifidelity uncertainty quantification method that combines low-order generalized Polynomial Chaos with Monte Carlo estimation via Control Variates. Results on the mean and variance estimates of the axle shaft torque show that the proposed method can correct the bias of low-order Polynomial Chaos expansions while significantly reducing variance compared to the conventional Monte Carlo.
Virtual Reality Exergames: Promoting Physical Health Among Industry Workers
Authors: Stranick Thomas , Lopez Christian 
Published: November 2021

Abstract
This work introduces a Virtual Reality (VR) Exergame application to prevent Work Related Musculoskeletal Disorders (WMSDs). WMSDs are an important issue that can have a direct economic impact since they can injure workers, who are then forced to take time off. Exercise and stretching is one method that can benefit workers’ muscles and help prevent WMSDs. While several applications have been developed to prevent WMSDs, most of the existing applications suffer from a lack of immersivity or just focus on education and not necessarily helping workers warm-up or stretch. Hence, this work presents an Exergame application that leverages VR and Depth-sensor technology to help provide users with an immersive first-person experience. The objective of the VR Exergame is to encourage and motivate users to perform full-body movements in order to pass through a series of obstacles. The application implements a variety of game elements to help motivate users to play the game and stretch. While in the game, users can visualize their motions by controlling the virtual avatar with their body movements. It is expected that this immersivity will motivate and encourage the users. Initial findings show the positive effects that the base exergame has on individuals’ motivation and physical activity level. The results indicate that the application was able to engage individuals in low-intensity exercises that produced significant and consistent increases in their heart rate. Lastly, this work explores the development and benefits that this VR Exergame could bring by motivating workers and preventing WMSDs.
A Study of the Multi-Stability in a Non-Rigid Stacked Miura-Origami Cellular Mechanism
Authors: Tao Jiayue , Li Suyi 
Published: November 2021

Abstract
Multi-stable structures have gathered extensive interest because they can provide a broad spectrum of adaptive functions for many engineering systems. Especially, origami sheets with a translational periodicity can be stacked and assembled to form a multi-stable cellular solid, which has emerged as a promising platform to design functional structures. This paper investigates the multi-stability characteristics of a non-rigid stacked Miura-origami mechanism consisting of Miura-ori sheets and accordion-shaped connecting sheets, focusing on the elemental unit cell. A nonlinear mechanical model based on the barhinge approach is established to quantitatively study the unit cell’s multi-stability with intentionally relaxed rigid-folding conditions. Results show that only two stable states are achievable in the unit cell with enforced rigid-folding kinematics. However, if one relaxes the rigid-folding conditions and allows the facet to deform (i.e. non-rigid folding), four stable states are reachable in the unit cell if the crease torsional stiffness of the connecting sheets becomes sufficiently larger than that of the Miura-ori sheets, or the stress-free folding angle deviates away from 0°. A close examination of the potential energy composition of the non-rigid unit cell provides a detailed principle underpinning the multi-stability. By showing the benefits of exploiting facet compliance, this study can become the building blocks for origami-based structures and material systems with a wider variety of novel functionalities.
A Single-Card GPU Implementation of Peridynamics
Authors: Bartlett John D. , Storti Duane 
Published: November 2021

Abstract
The rapid development of parallelization technology over the recent decades has provided a promising avenue for the acceleration of meshfree simulation methods. One such method, peridynamics, is particularly well-suited for parallelization due to the simplicity of the operations which must occur at each material point. However, while MPI-based parallelization (Message-Passing Interface; a method for CPU-based parallelization) of peridynamic problems is commonplace, GPU parallelization of peridynamics has received far less attention. While GPU technology may have once been an inferior option to MPI parallelization for peridynamics, modern GPU cards are more than capable of handling substantially sized peridynamics problems. This paper presents the parallelization of the peridynamic method for single-card GPU computing, providing a schematic for a compact parallel approach. The resulting method is tested with CUDA on a NVIDIA Tesla P100 card with 16 GB of memory. The per-node memory requirements for each data structure used are evaluated, as well as the per-node execution times for each operation in a million-node benchmark test. This setup is shown to provide speedup factors over 200 for problems sized up to several million nodes, therefore indicating such a GPU is more than adequate for the single-card parallelization of the peridynamic method.
Comparing Nuclear and Chemical Power Sources for MEMS/NEMS Applications
Authors: Barham Oliver M. 
Published: November 2021

Abstract
Nuclear batteries are a class of power sources that harvest energy from decaying radioactive isotopes to generate electricity for powering sensors and electronics. They are well known in the fields of space exploration and implantable medical devices, but are not widely known to micro or nano-technologists in general. Nuclear batteries are compared against chemical sources of energy applicable to small-scale systems, including energy harvesting prototypes and a mm-scale commercial lithium battery, utilizing the metrics of volumetric power and energy density. Nuclear batteries benefit from orders of magnitude more energy density than power sources derived from chemical reactions, however they also have orders smaller power density. For some sensor applications, nuclear batteries enable capabilities not possible with chemical energy sources, and examples are discussed.
Absolute Nodal Coordinate Formulations for Aeroelastic Analysis of Next-Generation Aircraft Wings
Authors: Otsuka Keisuke , Dong Shuonan , Makihara Kanjuro 
Published: November 2021

Abstract
Future aircraft have a high aspect ratio wing (HARW). The low induced drag of the wing can reduce fuel consumption, which enables eco-friendly flight. HARW cannot be designed by using conventional linear aeroelastic analysis methods because it undergoes very flexible motion. Although absolute nodal coordinate formulations (ANCF) have been widely used for analyzing various flexible structures, their application to HAWR is limited because the derivation of the ANCF elastic force for wing cross section is difficult. In this paper, we first describe three ANCF-based beam models that address the difficulty. The three models have different characteristics. Second, an aeroelastic coupling between the beam models and a medium-fidelity aerodynamic model based on unsteady vortex lattice method (UVLM) is briefly explained. Especially, the advantage of ANCF in the aeroelastic coupling is emphasized. Finally, we newly compare the three ANCF-based models in structural and aeroelastic analyses. From the viewpoint of the convergence performance and calculation time, we found the best ANCF-based beam model among the three models in static structural and aeroelastic analyses, while the three models have comparable performances in dynamic structural and aeroelastic analyses. These findings contribute to the development of aeroelastic analysis framework based on ANCF and the design of next-generation aircraft wings.
Design and Analysis of Flexible Continuum Robot Based on Origami and Mortise-Tenon Structure (FCRBOM)
Authors: Yu Yue , Qiu Lifang , Wang Decheng , Zou Jing 
Published: November 2021

Abstract
The continuum robot is a soft robot with infinite degrees of freedom. Origami has a high capacity for spatial deployment. This paper proposes a flexible continuum robot based on origami and mortise-tenon structure (FCRBOM). The robot consists of some flexible hinges based on origami and mortise-tenon structure (FHBOM). The design process of the FCRBOM is given. The compliance of the FCRBOM is analyzed by the compliance matrix method. The Finite element analysis (FEA) is used to simulate and analyze the FCRBOM, and the correctness of the theoretical analysis is verified. Then a spatial FCRBOM (SFCRBOM) is designed. The impact of key dimensional parameters on the flexibility of SFCRBOM is discussed. Finally, an SFCRBOM with higher flexibility is presented.
Effect of Matching Buckling Loads on Post-Buckling Behavior in Compliant Mechanisms
Authors: Numić A. , Blad T. W. A. , van Keulen F. 
Published: November 2021

Abstract
In this paper, a novel method for stiffness compensation in compliant mechanisms is investigated. This method involves tuning the ratio between the first two critical buckling loads. To this end, the relative length and width of flexures in two architectures, a stepped beam and parallel guidance, are adjusted. Using finite element analysis, it is shown that by maximizing this ratio, the actuation force for transversal deflection in post-buckling is reduced. These results were validated experimentally by identifying the optimal designs in a given space and capturing the force-deflection characteristics of these mechanisms.
Geometry Optimization for Resonator Nonlinearities and Modes Controlling
Authors: Hajjaj Amal Z. , Jaber Nizar 
Published: November 2021

Abstract
In this paper, we utilize a passive technique based on geometry optimization to control the nonlinearities and the dynamical response of MEMS resonators. To achieve this, we propose a new hybrid shape combining a straight and initially curved microbeam. The Galerkin method is employed to solve the beam equation and study the effect of the different design parameters on the ratios of the frequencies and the nonlinearities of the structure. We show by adequately selecting the parameters of the structure; we can realize systems with strong quadratic or cubic nonlinearities or even zero nonlinearity. Also, we investigate the resonator shape effect on breaking the symmetry and explore different linear coupling phenomena: crossing, veering, and mode hybridization. We demonstrate the possibility of controlling the frequencies of the different modes of vibrations to achieve commensurate ratios necessary for activating internal resonance. The ability to activate the nonlinearities and tuning the frequencies is essential for wide range of applications in signal filtering, sensing, timing, and mass and gas sensing. The proposed method is simple in principle, easy to fabricate, and offers a wide range of controllability on the sensor nonlinearities and response. In addition, the passive techniques does not need additional circuits, to control the frequencies, which help reducing the device size, cost, and power consumption.
Potential Energy as Design Criterion in Planar Multistable Mechanisms
Authors: Dold Edward J. , Voglewede Philip A. 
Published: November 2021

Abstract
Toggle mechanisms are used throughout engineering to accomplish various tasks, for example residential electrical switching. The design of toggle mechanisms can be broken into three categories: determination of a topology, geometric parameterization, and optimization. While topological determination and optimization have well established processes for use in design, geometric parameterization which includes defining link lengths and spring stiffness has largely been left to engineering judgement. This paper presents a design methodology using potential energy graphs which informs the engineering decisions made in choosing mechanism parameters, giving designers higher confidence in the design. A kinematic analysis coupled with Lagrange’s equation determines the relationship between the mechanism parameters and the potential energy curve. Plotting the potential energy with respect to the generalized coordinate yields a graph with a slope that is the generalized force or moment. The relationships between parameters and their effects on the mechanism are difficult to observe in the equations of motion, but potential energy plots readily provide information pertinent to the design of toggle mechanisms and decouple their effects. The plots also allow design by position rather than time which makes the design process faster. The design process is applied to three examples: a simple toggle mechanism, a compliant mechanism, and a reconfigurable mechanism to show the nuances of the approach.
Understanding Professional Designers’ Knowledge Organization Behavior: A Case Study in Product Teardowns
Authors: Wang Ye , Grandi Daniele , Cui Dixun , Rao Vivek , Goucher-Lambert Kosa 
Published: November 2021

Abstract
Knowledge organization is an essential component of engineering design, and a deeper understanding of how designers organize knowledge could enable more effective insights in support of the design process. To explore this, we examine 23 professional designers’ knowledge organization practices as they virtually engage with data collected during a teardown of a consumer product. Designers organized this data by forming groups of related data, nesting subgroups of data within groups, and creating directional links between groups of data and individual data. Our results indicate three insights about how designers organize and acquire knowledge from product teardowns. First, we observe that while designers find grouping data to be more effective for learning, linking proved more helpful for knowledge transfer. Second, we find that designers employ links between data much more frequently than they do nests, and that links primarily serve to identify trade-offs, requirements, and opportunities for team collaboration. Finally, a graph analysis indicates that design features, product housing, cost, and manufacturing coexist as separate but central groups in designers’ knowledge organization, reflecting the diversity of perspectives on knowledge organization emergent in a constrained teardown activity. These findings provide insight into professional designers’ knowledge organization practices, and represent a preliminary step toward design knowledge bases that more accurately reflect designer behavior, ultimately enabling more effective data-driven support tools for design.
Neural Network Based Transfer Learning for Robot Path Generation
Authors: Tang Houcheng , Notash Leila 
Published: November 2021

Abstract
In this paper, an artificial neural network (ANN) based transfer learning approach of inverse displacement analysis of robot manipulators is studied. ANNs with different structures are applied utilizing data from different end effector paths of a manipulator for training purposes. Four transfer learning methods are proposed by applying pretrained initial parameters. Final training results of ANN with transfer learning are compared with those of ANN with random initialization. To investigate the rate of convergence of data fitting comprehensively, different values of performance targets are defined. The computing epochs and performance measures are compared. It is presented that, depending on the structure of ANN, the proposed transfer learning methods can accelerate the training process and achieve higher accuracy. Depending on the method, the transfer learning improves the performance differently.
Stress and Resistance Relaxation for Carbon Nanoparticle Silicone Rubber Composite Large-Strain Sensors
Authors: Ellingham Richard , Giffney Tim 
Published: November 2021

Abstract
The high stretchability, scalability and bio-compatibility of carbon black nanoparticle (CB)-polydimethylsiloxane (PDMS) elastomer composites are attractive characteristics for diverse applications ranging from the biomedical to aerospace fields. These materials are particularly useful as high strain sensors, but show stress relaxation and resistance relaxation behaviour that must be better understood in order to improve sensing performance and optimize the material design. In this work, we have characterized and modelled the resistance relaxation behaviour of these composites to understand the response of resistance to transient step strain input. CB-PDMS specimens have been fabricated with 7.5 and 10 weight percentage (w.t.%) of CB and subjected to repeated stretching while continually monitoring resistance and stress. A model for the resistive relaxation in time has been developed using 30 relaxations to give maximum coefficients of variance of the constants of 18.54% and 52.72% for 7.5 and 10 w.t.% of CB. The ability to model resistance relaxation is useful for the development of, accurate, highly flexible dynamic strain sensors.