bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Novel Method for Calculating the Parametric Hypervolume Indicator
Authors: Weaver-Rosen Jonathan M. , Malak Richard J. 
Published: November 2021

Abstract
This paper presents a new methodology for calculating the hypervolume indicator (HVI)for multi-objective and parametric data. Existing multi-objective HVI calculation techniques cannot be directly used for parametric data because designers do not have preferences for parameters like they do for objectives. The novel method presented herein allows for the consideration of both objectives and parameters through the newly introduced hypercone heuristic (HCH). This heuristic relaxes the strict rules of parametric Pareto dominance for a more practical dominance assessment when comparing designs of differing parameter values without violating Pareto dominance rules. A parametric HVI (pHVI) enhances a design engineer’s toolkit by enabling both online and offline evaluation of parametric optimization results. The pHVI measure allows designers to compare solution sets, detect optimization convergence, and to better inform optimization procedures in a parametric context. Results show that the HCH-based pHVI yields a similar quality measure to the existing technique based on a support vector domain description (SVDD) in a fraction of the computational time. Furthermore, the novel HCH-based pHVI technique satisfies multi-objective HVI properties allowing previous applications of the HVI to be applied to multi-objective parametric optimization. This contribution enables the field of parametric optimization, and thus parametric design, to benefit from prior and future advances in the multi-objective optimization domain involving the HVI.
Gravity Balancing Reliability and Sensitivity Analysis of Robotic Manipulators With Uncertainties
Authors: Nguyen Vu Linh , Kuo Chin-Hsing , Lin Po Ting 
Published: November 2021

Abstract
This paper presents the gravity balancing reliability and sensitivity analysis of robotic manipulators with uncertainties. The gravity balancing reliability of the robot is defined as the probability that the reduction torque ratio of the robot reduces below a specified threshold. This index is of great importance for assessing and guaranteeing the balancing performance of the robot in the presence of uncertainties in input parameters. In this work, the balancing design for an industrial robot using the gear-spring modules (GSMs) is proposed with the adoption of a simulation-based analysis of the gravity effect of the robot. The Monte Carlo Simulation (MCS) method with normally distributed variables (i.e., link dimensions, masses, and spring stiffness coefficients) is employed to analyze and simulate the reliability. A case study with an industrial robot is then given to illustrate the reliability performance and the sensibility of the uncertain parameters. It is found that the gravity balancing behavior is achieved even when the uncertainties are applied. The uncertainties could deteriorate the balancing performance when increasing the standard deviations by more than seven percent of their means. The dimensional parameters enjoy the most critical influence on the balancing performance.
Automating Design Requirement Extraction From Text With Deep Learning
Authors: Akay Haluk , Yang Maria , Kim Sang-Gook 
Published: November 2021

Abstract
Nearly every artifact of the modern engineering design process is digitally recorded and stored, resulting in an overwhelming amount of raw data detailing past designs. Analyzing this design knowledge and extracting functional information from sets of digital documents is a difficult and time-consuming task for human designers. For the case of textual documentation, poorly written superfluous descriptions filled with jargon are especially challenging for junior designers with less domain expertise to read. If the task of reading documents to extract functional requirements could be automated, designers could actually benefit from the distillation of massive digital repositories of design documentation into valuable information that can inform engineering design. This paper presents a system for automating the extraction of structured functional requirements from textual design documents by applying state of the art Natural Language Processing (NLP) models. A recursive method utilizing Machine Learning-based question-answering is developed to process design texts by initially identifying the highest-level functional requirement, and subsequently extracting additional requirements contained in the text passage. The efficacy of this system is evaluated by comparing the Machine Learning-based results with a study of 75 human designers performing the same design document analysis task on technical texts from the field of Microelectromechanical Systems (MEMS). The prospect of deploying such a system on the sum of all digital engineering documents suggests a future where design failures are less likely to be repeated and past successes may be consistently used to forward innovation.
Evaluation of Commercial Building Clusters With Energy Storage to Reduce Reliance on Electrical Utility Grids
Authors: Kaminski Gregory , Odonkor Philip 
Published: November 2021

Abstract
The decreasing cost of implementation and increasing regulatory incentive to lower energy use have led to an increased adoption of distributed energy resources in recent years. This increased adoption has been further fueled by a surge in energy consciousness and the expansion of energy-saving products and technologies. To lower reliance on the electrical grid and fully realize the benefits of distributed energy resources, many consumers have also elected to use battery systems to store generated energy. For owners of multiple buildings, or multiple owners willing to share the operational cost, building clusters may be formed to more effectively take advantage of these distributed resources and storage systems. The implementation of these systems in existing buildings introduces the question of what makes a “good” building cluster. Furthermore, the scalable nature of distributed energy sources and storage systems create countless possibilities for system configuration. Through comparison of unique two-building clusters from a stock of five buildings with a given distributed energy resource (in this case, a solar photovoltaic panel array) and energy storage system, we develop a fundamental understanding of the underlying factors that allow building clusters to be less reliant on the utility grid and make better use of energy generation and storage systems.
Modulating Functions Based Fast and Robust Estimation for a Class of Fractional Order Vibration Systems
Authors: Wang Zhi-Bo , Liu Da-Yan , Boutat Driss , Tian Yang , Liu Hao-Ran 
Published: November 2021

Abstract
This paper aims to fast and robustly estimate the fractional integrals and derivatives of positions from noisy accelerations for a class of fractional order vibration systems defined by the Caputo fractional derivative. The main idea is to convert the original issue into the estimation of the fractional integrals of accelerations and the ones of the unknown initial conditions, on the basis of the additive index law. Being proper integrals, the fractional integrals of accelerations can be estimated via a numerical method. Consequently, solving the original problem boils down to estimating the unknown initial values. To this end, the modulating functions method is adopted. By constructing appropriate modulating functions, the unknown initial values are exactly given in terms of algebraic integral formulas in different situations. Finally, two illustrations are presented to verify the correctness and robustness of the proposed estimators.
Topology Optimization With Locally Evaluable Complement Space Connectivity
Authors: Morris Clinton B. , Mirzendehdel Amir M. , Behandish Morad 
Published: November 2021

Abstract
Enforcing connectivity of parts or their complement space during automated design is essential for various manufacturing and functional considerations such as removing powder, wiring internal components, and flowing internal coolant. The global nature of connectivity makes it difficult to incorporate into generative design methods that rely on local decision making, e.g., topology optimization (TO) algorithms whose update rules depend on the sensitivity of objective functions or constraints to locally change the design. Connectivity is commonly corrected for in a post-processing step, which may result in suboptimal designs. We propose a recasting of the connectivity constraint as a locally differentiable violation measure, defined as a “virtual” compliance, modeled after physical (e.g., thermal or structural) compliance. Such measures can be used within TO alongside other objective functions and constraints, using a weighted penalty scheme to navigate tradeoffs. By carefully specifying the boundary conditions of the virtual compliance problem, the designer can enforce connectivity between arbitrary regions of the part’s complement space while satisfying a primary objective function in the TO loop. We demonstrate the effectiveness of our approach using both 2D and 3D examples, show its flexibility to consider multiple virtual domains, and confirm the benefits of considering connectivity in the design loop rather than enforcing it through post-processing.
Infinite Energy Problem of Fractional Circuit Elements: Overview and Perspectives
Authors: Wei Yiheng , Chen YangQuan , Chen Yuquan , Zhang Hui 
Published: November 2021

Abstract
Fractional circuit elements become increasingly popular due to their versatility in various applications. However, the bottleneck in deploying these tools in practice is related to an open problem, i.e, infinite energy problem. On this topic, many valuable achievements have been made. Some scholars don’t dare to use fractional circuit elements because of the infinite energy problem while some scholars believe that there is no paradox compared with classical finite energy or even some scholars think that this problem has been successfully solved. However, there is still no consensus on this topic and confusion remains widespread. Consequently, a comprehensive review on infinite energy problem is needed imperatively. At this point, this paper reviews the consequences, root causes, and potential mitigation approaches through the modeling analysis and literature survey. This review starts with the fractional capacitors. Subsequently, other fractional circuit elements and fractional order operators/systems are considered. Finally, the main technical challenges as well as future researches on this topic are highlighted carefully.
Local Redesign for Additive Manufacturability of Compliant Mechanisms Using Topology Optimization
Authors: Koppen Stijn , Hoes Emma , Langelaar Matthijs , Frecker Mary I. 
Published: November 2021

Abstract
Compliant mechanisms are crucial components in current and future high-precision applications. Topology optimization and additive manufacturing offer freedom to design complex compliant mechanisms that were impossible to realize using conventional manufacturing. Design for additive manufacturing constraints, such as the maximum overhang angle and minimum feature size, tend to drastically decrease the performance of topology optimized compliant mechanisms. It is observed that, among others, design for additive manufacturing constraints are only dominant in the flexure regions. Flexures are most sensitive to manufacturing errors, experience the highest stress levels and removal of support material carries the highest risk of failure. It is crucial to impose these constraints on the flexure regions, while in others part of the compliant mechanism design, these constraints can be relaxed. We propose to first design the global compliant mechanism layout in the full domain without imposing any design for additive manufacturing constraints. Subsequently we redesign selected refined local redesign domains with design for additive manufacturing constraints, whilst simultaneously considering the mechanism performance. The method is applied to a single-input-multi-output compliant mechanism case study, limiting the maximum overhang angle, introducing manufacturing robustness and limiting the maximum stress levels of a selected refined redesign domain. The high resolution local redesigns are detailed and accurate, without a large additional computational effort or decrease in mechanism performance. Thereto, the method proves widely applicable, computationally efficient and effective in its purpose.
Towards Generalization of Intelligent Fault Detection for Roller Element Bearings via Distinct Dataset Transfer Learning
Authors: Larocque-Villiers Justin , Dumond Patrick 
Published: November 2021

Abstract
Through the intelligent classification of bearing faults, predictive maintenance provides for the possibility of service schedule, inventory, maintenance, and safety optimization. However, real-world rotating machinery undergo a variety of operating conditions, fault conditions, and noise. Due to these factors, it is often required that a fault detection algorithm perform accurately even on data outside its trained domain. Although open-source datasets offer an incredible opportunity to advance the performance of predictive maintenance technology and methods, more research is required to develop algorithms capable of generalized intelligent fault detection across domains and discrepancies. In this study, current benchmarks on source–target domain discrepancy challenges are reviewed using the Case Western Reserve University (CWRU) and the Paderborn University (PbU) datasets. A convolutional neural network (CNN) architecture and data augmentation technique more suitable for generalization tasks is proposed and tested against existing benchmarks on the Pb U dataset by training on artificial faults and testing on real faults. The proposed method improves fault classification by 13.35%, with less than half the standard deviation of the compared benchmark. Transfer learning is then used to leverage the larger PbU dataset in order to make predictions on the CWRU dataset under a challenging source-target domain discrepancy in which there is minimal training data to adequately represent unseen bearing faults. The transfer learning-based CNN is found to be capable of generalizing across two open-source datasets, resulting in an improvement in accuracy from 53.1% to 68.3%.
Optimal Performance Comparison of Nonlinear Energy Sinks and Linear Tuned Mass Dampers
Authors: Yegorov (Egorov) Ivan , Uden Austin , Yurchenko Daniil 
Published: November 2021

Abstract
This paper studies a targeted energy transfer (TET) mechanism for a two-degree-of-freedom (TDOF) model in free vibration. The model comprises a primary linear system and a secondary system in the form of an energy sink which can be nonlinear. The free vibrations are considered subject to an impulsive excitation exerted on the primary system, leading to a nonzero initial velocity. The goal is to obtain the spring parameters in the nonlinear energy sink (NES) so as to maximize an energy dissipation measure (EDM) representing the percentage of impulsive energy that is absorbed and dissipated in the NES. A global optimization algorithm is used for this purpose. The optimal performance is assessed for the purely linear, linear-cubic, and purely cubic configurations of the spring connecting the primary and secondary systems. The corresponding results are compared with each other. The optimization process is performed for the EDM averaged over given ranges of the initial impulse and natural frequency in the primary system. It is shown that the type of the optimal configuration can vary depending on these ranges.
Approximating Hinges With Multimaterial Compliant Joints
Authors: Talken Independence , Liang Zijuan , Plecnik Mark 
Published: November 2021

Abstract
This paper investigates the use of multimaterial compliant joints produced through additive manufacturing in order to approximate a revolute joint. Compliant joints benefit from low friction and reduced wear, but at the cost of increased joint stiffness, reduced range of motion, and a reduced ability to resist loading. In addition, they might also provide a poor approximation of the revolute joints they intend to replace. In this paper, we experiment with three multimaterial compliant joint configurations. The first joint emphasizes accurate kinematics, the second joint aims to reduce axis-aligned stiffness, and the third joint compromises between the two. Samples were fabricated on a desktop 3D printer using PLA (polylactic acid) as the rigid material and TPU (thermoplastic polyurethane) for its flexibility. Samples were measured for tensile stiffness, torsional stiffness, range of motion, and approximation of a hinge motion. Our results indicate design trade offs where joints that measure most ideal for one property will be least ideal for another. The most novel design in this paper straddles this trade off. In the end, the suitability of each joint design is determined by the loading, accuracy, and range of motion requirements posed by a given application.
Coupled Lateral and Longitudinal Control for Trajectory Tracking, Lateral Stability, and Rollover Prevention of High-Speed Automated Vehicles Using Minimum-Time Model Predictive Control
Authors: Chen Shuping , Chen Huiyan , Pletta Alex , Negrut Dan 
Published: November 2021

Abstract
Most controllers concerning lateral stability and rollover prevention for autonomous vehicles are designed separately and used simultaneously. However, roll motion influences lateral stability in cornering maneuvers, especially at high speed. Typical rollover prevention control stabilizes the vehicle with differential braking to create an understeering condition. Although this method can prevent rollover, it can also lead to deviation from a reference path specified for an autonomous vehicle. This contribution proposes and implements a coupled longitudinal and lateral controller for path tracking via model predictive control (MPC) to simultaneously enforce constraints on control input, state output, lateral stability, and rollover prevention. To demonstrate the approach in simulation, an 8 degrees of freedom (DOF) vehicle model is used as the MPC prediction model, and a high-fidelity 14-DOF model as the plant. The MPC-based lateral control generates a sequence of optimal steering angles, while a PID speed controller adjusts the driving or braking torque. The lateral stability envelope is determined by the phase plane of yaw rate and lateral velocity, while the roll angle threshold is derived from the load transfer ratio (LTR) and tire vertical force under the condition of quasi-steady-state rollover. To track the desired trajectory as fast as possible, a minimum-time velocity profile is determined using a forward-backward integration approach, subject to tire friction limit constraints. We demonstrate the approach in simulation, by having the vehicle track an arbitrary course of continuously varying curvature thus highlighting the accuracy of the controller and its ability to satisfy lateral and roll stability requirements. The MATLAB® code for the 8-DOF and 14-DOF vehicle models, along with the implementation of the proposed controller are available as open source in the public domain.
The Missing Link Between Project and Prototype: Teaching Student Designers to Navigate the Prototyping Process
Authors: Arndt Hansen Camilla , Eifler Tobias , Deininger Michael 
Published: November 2021

Abstract
It has been well established that prototyping is an important part of development projects. Prototypes are used in engineering design courses but teaching students the strategic skills needed to decide what to prototype during development projects is often not a part of the curriculum.
This paper proposes a model that uses the prototyping process to link individual prototyping activities and project goals. The proposition is that when designers work intentionally with prototyping as a process, they will be able to make strategic decisions about how and when to use prototypes.
To teach students a prototyping process mindset, a simple prototype prioritization tool, the Prototyping Target, was developed and integrated into project-based product development courses. Fifteen student teams used the Prototyping Target to decide which project questions to answer with prototypes every week over three weeks. Results show that criticality to project success is a good starting point but not the only parameter to consider when deciding what to prototype.
A comparison between prototyping plans made with and without the Prototyping Target shows that student teams selected a clearer purpose for their prototypes when they used the prioritization tool before planning specific prototyping activities.
Overall, this paper demonstrates the benefits of teaching student designers to navigate the prototyping process.
Modelling the Axis Drift of Short Wire Flexures and Increasing Their Support Stiffness Using Polymers
Authors: Daan Boris , Rommers Jelle , Herder Just L. 
Published: November 2021

Abstract
For steel flexures, complex geometries are required to reach high support stiffness and limit axis drift over large ranges of motion. These complex flexures are expensive and difficult to manufacture. This paper presents a method of designing short, polymer wire flexures with high support stiffness and modelling their axis drift using a novel method, the arc method. The arc method is validated against finite element methods (FEM) and physical tests, showing at least a factor 10 lower error than existing pseudo-rigid-body models (PRBM) at 70° deflection, while maintaining a simple modelling approach. The use of polymers increases support stiffness of wire flexures by a factor 7800 with respect to steel at 70° deflection, even though the material stiffness is substantially lower. This is due to the large allowed strain of polymers increasing the possible diameter by a factor 110.
Conceptual Configuration Synthesis of Line-Foldable Deployable Space Truss Structures Utilizing Graph Theory and Entropy
Authors: Wang Shoufei , Zhao Yong 
Published: November 2021

Abstract
From the perspective of the truss as a whole, this research presents an approach to synthesizing conceptual configurations for deployable space truss structures that are line-foldable with the help of graph theory and entropy. First, according to graph theory, the bijection between a truss and its graph model is established by defining a bijective mapping between set elements. Therefore, operations can be performed based on graph models. Second, the principle of configuration evolution is interpreted by employing Maxwell’s rule, it also discusses the necessary and sufficient condition of configuration evolution. Configurations of evolution belong to three phases: space configuration, transformation configuration, and linear configuration. And it finds that the reasonable transformation configuration plays a key role. Further, maximum clique detection depending on backtracking is used to screen out unreasonable transformation configurations. Third, it introduces entropy, and the phenomenon of entropy change in configuration evolution is revealed and induction weights of rigid links are defined. It calculates the weight value of a transformation configuration by adding up induction weights of rigid links removed, also, weight values are used to classify transformation configurations. Finally, based on the previous analysis, a procedure to synthesize transformation configurations is formed and it is verified by a truss model with 7 nodes. This research lays the foundation for geometric dimension design and engineering applications.