bluuebunny/crossref_metadata_2025_split huggingface dataset:
Inverse Multi-Scale Robust Design of Composite Structures Using Design Capability Indices
Authors: Beemaraj Soban Babu , Pathan Rizwan Khan , Salvi Amit G. , Sharma Gehendra , Mistree Farrokh , Allen Janet K. 
Published: November 2020

Abstract
Composite materials are heterogeneous materials, and are hierarchical in nature consisting of multiple length scales. In the design of structures with composite materials, the micro-structure of the materials have a direct bearing on the final behavior of the structure. The deviations in the bulk material properties are caused due to uncertainties associated with the micro-structures and its propagation through different length scales. Uncertainties in the design parameters (geometry and materials properties etc.) at macro-scale also contribute to variations in the final behavior. Currently, these uncertainties are included as a large factor of safety in deterministic design, which may result in over design of the product. The robust performance of the structure can be achieved by considering these uncertainties explicitly in the design process. In this paper, a method for designing a robust composite structure subjected to different loading conditions is illustrated. Structural models are run to compute robust material properties and geometries for different load scenarios that yield most robust materials and micro-structures. Most robust combination of material and geometries is selected that results in most robust performance under all loading scenarios. These materials are designed using multiscale models in which micro-structural uncertainties are accounted. The uncertainties in the material properties and geometrical parameters at different length scales are explicitly modelled as ranges in the set of input parameters. Final performance variations are calculated using design capability index. Consolidated single material parameters and dimensions are selected using efficiency metrics. Design capability indices are formed as goals and constraints in compromise decision support problem. Robust micro-structures are designed inductively rather than deductively.
A Two-Stage Multi-Fidelity Design Optimization for K-mer-Based Pattern Recognition (KPR) in Image Processing
Authors: Yao Yu-Ta , Wu Yu-Wei , Lin Po Ting 
Published: November 2020

Abstract
Pattern recognition has been widely used in various applications of image processing. It is used to extract meaningful image features from the given image samples and to build classification systems with the intelligence of human recognition. Convolutional Neural Network (CNN) [1] has been one of the most popular and widely used methods for image pattern recognition applications. However, CNN was known not to be rotation-invariant to image patterns. It usually required a larger amount of training image dataset with greater variations in positions and orientations, or additional numerical treatments of spatial transformations [2]. On the other hand, K-mer-based Pattern Recognition (KPR) [3] has been developed to apply an unique way of rotation-invariant sampling to the inspected image pattern and analyze the frequency of the captured pattern features. A classification system was then built based on the K-mer frequency for the desired pattern recognition. In this paper, a series of tests and verifications of the KPR was done. It was found that finding the appropriate design parameters of the KPR for a specific application of image pattern recognition could be costly. A two-stage multi-fidelity design optimization was utilized to improve the efficiency of finding the parameters of KPR. In each iteration of the multi-fidelity design optimization procedure, the first stage was to evaluate the accuracy and efficiency of the design parameters in the K-mer-based pattern classification based on a full set of the given images. The second stage was to find a newer set of design parameters that performed the best based on a smaller set of images, which provided a classification set with lower fidelity than the original one. As a result, the proposed strategy of the multi-fidelity design optimization was more efficient than finding the optimal design parameters based on the full set of the given images.
Visualising Compliance of Composite Shell Mechanisms
Authors: Stacey Jonathan P. , O’Donnell Matthew P. , Schenk Mark , Kim Charles J. 
Published: November 2020

Abstract
In the design of isotropic compliant shell-based mechanisms a desired response of an end-effector is commonly achieved through careful selection of shell geometry and material. However, for applications such as the design of medical support devices the shell must conform to a highly constrained set of permissible geometries, limiting tailorability. One solution to this design challenge is to exploit anisotropic material behaviour.
Advanced composite materials may be elastically tailored by varying the fibre orientation, but at the cost of increased design complexity. Herein we present an approach for capturing the effects of material anisotropy on compliant shell mechanisms by providing the designer with a method for visualising their response in a physically intuitive manner.
We extend the mechanism characterisation technique of Lip-kin and Patterson [1] using eigen-decomposition, and visualise the compliance vectors for structures with material anisotropy. We characterise the behaviour of cantilevered “tape-spring” shell geometries with varying enclosed angles using nonlinear finite element analysis. For small enclosed angles we observe significant reorienting of the compliance vectors due to stiffness anisotropy; as the enclosed angle is increased, geometry dominates the response. However, in an intermediate region both geometric and stiffness effects interact, highlighting the potential richness of the design space.
Quantification of Students’ Learning Through Reflection on Doing Based on Text Similarity
Authors: Peng Shan , Ming Zhenjun , Allen Janet K. , Siddique Zahed , Mistree Farrokh 
Published: November 2020

Abstract
In this paper we address the following question:
How can instructors leverage assessment instruments in design, build, and test courses to simultaneously improve student outcomes and assess student learning well enough to improve courses for future students?
A learning statement is a structured text-based construct for students to record what they learned by reflecting on authentic immersive experiences in a semester-long engineering design course. The immersive experiences include lectures, assignments, reviews, building, testing, and a post-analysis of an electro-mechanical device to address a given customer need.
Over the past three years, in the School of Aerospace and Mechanical Engineering at the University of Oklahoma, Norman, we have collected almost 30,000 learning statements from almost 400 students. In the past few years, we have analyzed this data to improve our understanding of what students have learned by reflecting on doing and thence how we might improve the delivery of the course.
In an earlier paper, we described a text mining framework to facilitate the analysis of a vast number of learning statements. Our focus, in the earlier paper, was on describing the functionalities (i.e., data cleaning, data management, text analysis, and visualization results) of the framework and demonstrating one of the text quantification methods — term frequency — using the learning statements. In this paper, we focus on demonstrating another text quantification method, namely, text similarity, to facilitate instructors’ gaining new insights from students’ learning statements. In the method of text similarity, we measure the cosine distance between two text vectors and is typically used to compare the semantic similarity between documents. In this paper, we compare the similarity between what students learned (embodied in learning statements) and what instructors expected the students to learn (embodied in the course booklet), thus providing evidence-based guidance to instructors on how to improve the delivery of AME4163 – Principles of Engineering Design.
Unidirectional Acoustic Parametric Amplification in Space-Time Modulated Membrane System
Authors: Zhu Xiaohui , Li Longqiu , Zhang Guangyu 
Published: November 2020

Abstract
Space-time modulation puts a new twist on controlling wave behaviors and opens the door for unprecedented wave manipulation capabilities. Here we propose a space-time modulated membrane system aiming to realize the unidirectional parametric amplification. Two different approaches, continuous model and transfer matrix method, are applied to analyze the acoustic response of the system under a monochromatic incidence. In the proposed space-time modulated membrane system, these two methods are in good agreement with each other. Results show that under a monochromatic incidence from one end of the system, a harmonic is generated, and both the incident wave and generated wave exponentially grow along with the system. Our work demonstrates a metamaterial realization of unidirectional acoustic parametric amplifier via space-time modulated membranes, which offers a design platform for a number of applications in sensing, imaging and communication.
Sampling-Based Reliability Analysis Using Deep Feedforward Neural Network (DFNN)
Authors: Lee Ungki , Lee Ikjin 
Published: November 2020

Abstract
Reliability analysis that evaluates a probabilistic constraint is an important part of reliability-based design optimization (RBDO). Inverse reliability analysis evaluates the percentile value of the performance function that satisfies the reliability. To compute the percentile value, analytical methods, surrogate model based methods, and sampling-based methods are commonly used. In case the dimension or nonlinearity of the performance function is high, sampling-based methods such as Monte Carlo simulation, Latin hypercube sampling, and importance sampling can be directly used for reliability analysis since no analytical formulation or surrogate model is required in these methods. The sampling-based methods have high accuracy but require a large number of samples, which can be very time-consuming. Therefore, this paper proposes methods that can improve the accuracy of reliability analysis when the number of samples is not enough and the sampling-based methods are considered to be better candidates. This study starts with the idea of training the relationship between the realization of the performance function at a small sample size and the corresponding true percentile value of the performance function. Deep feedforward neural network (DFNN), which is one of the promising artificial neural network models that approximates high dimensional models using deep layered structures, is trained using the realization of various performance functions at a small sample size and the corresponding true percentile values as input and target training data, respectively. In this study, various polynomial functions and random variables are used to create training data sets consisting of various realizations and corresponding true percentile values. A method that approximates the realization of the performance function through kernel density estimation and trains the DFNN with the discrete points representing the shape of the kernel distribution to reduce the dimension of the training input data is also presented. Along with the proposed reliability analysis methods, a strategy that reuses samples of the previous design point to enhance the efficiency of the percentile value estimation is explained. The results show that the reliability analysis using the DFNN is more accurate than the method using only samples. In addition, compared to the method that trains the DFNN using the realization of the performance function, the method that trains the DFNN with the discrete points representing the shape of the kernel distribution improves the accuracy of reliability analysis and reduces the training time. The proposed sample reuse strategy is verified that the burden of function evaluation at the new design point can be reduced by reusing the samples of the previous design point when the design point changes while performing RBDO.
A Decision-Making Approach for Procuring Custom-Made Machineries and Components
Authors: Mandolini Marco , Favi Claudio , Campi Federico , Germani Michele 
Published: November 2020

Abstract
The paper wants to improve the procurement processes for custom-made machineries and components. Indeed, the current methods and software platforms adopted by industries for purchasing machineries do not consider value generated through the entire lifecycle. Furthermore, the purchasing process of custom-made components from external suppliers is often and still characterized by several negotiation activities.
This paper wants to improve this context by proposing two approaches to fostering the procurement processes. The first objective is to define an approach for standardizing the method for configuring machineries to be supplied from suppliers and to establish an approach for estimating their costs. The most important benefits of such an approach consist of (i) machinery selection based on the Total Value of Ownership (TVO), and (ii) optimized suppliers’ selection by strengthening relationships between customers and suppliers.
The second objective is to define a method and a software platform for managing the procurement phase of custom-made components. The most important benefits of this approach consist of (i) the standardization of procurement policies for custom and standard components, and (ii) the faster budgeting process.
Future work consists of defining a reference model for gathering primary data required for TVO computation and defining standard agreements between suppliers and customers.
A Study on the Machine Learning Framework for the Geometric Modelling of Wire Arc Bead Profile
Authors: Oh Xi Yu , Soh Gim Song 
Published: November 2020

Abstract
Wire Arc Additive Manufacturing (WAAM) is a manufacturing process that deposits weld beads layer-by-layer in a planar fashion, leading to a final part. Thus, the accuracy of the printed geometry is largely dependent on the knowledge of the bead profile employed, which by itself is dependent on a variety of process parameters, such as wire feedrate and torch speed. Existing models for modelling bead profile are based on its width and height, which do not necessarily capture the geometry of the weld bead accurately. This could affect the step over increment strategy, which dictates the geometry of the resulting overlapping valley.
In this paper, we formulate and evaluate the performance of a variety of machine learning framework for predicting the bead cross-sectional profiles. To model the geometry of a bead, we explored direct cartesian representations using polynomials and vertical coordinates, as well as a higher dimensional representation using planar quaternions for supervised learning. Experiments are conducted on single bead SS316L material to compare the various framework performance. We found that among these, the planar quaternion representation with a non-linear neural network framework captures and retains the curvature characteristics of the bead during the learning and prediction process most accurately with a mean Chi-Square goodness of fit of 0.026.
Shaking Force Balancing of the Delta Robot
Authors: Geng Jing , Arakelian Vigen , Chablat Damien 
Published: November 2020

Abstract
The paper deals with the shaking force balancing of the DELTA robot. The balancing of the shaking force of the DELTA robot is carried out through the center of mass acceleration minimization. The trajectories of the total mass center of moving links are defined as straight lines between the initial and final positions of the platform. Then, the motion between these positions are parameterized with “bang-bang” motion profiles. Such a motion generation allows the reduction of the maximal value of the center of mass acceleration and, consequently, leads to the reduction in the shaking force. A main advantage of this method is its simplicity and versatility. It is carried out without any modification of mass redistribution of the initial robot structure, i.e. without adding counterweights. In the case of changing trajectories or payloads, it is just necessary to provide the initial and final positions of the platform, calculate the input parameters according to the proposed method and implemented them in the robot control system. Numerical simulations illustrate the efficiency of the suggested approach.
Active Aerodynamics Through Active Body Control: Modelling and Static Simulator Validation
Authors: de Carvalho Pinheiro Henrique , Russo Francesco , Sisca Lorenzo , Messana Alessandro , De Cupis Davide , Ferraris Alessandro , Airale Andrea Giancarlo , Carello Massimiliana 
Published: November 2020

Abstract
Active aerodynamics is a growing field in the race car and high-performance vehicles segments, since each situation on the track may require different aero forces to achieve the best vehicle dynamics performance.
This paper presents an active aerodynamics control system developed through the active control of the body trim. By interchanging four different setups on the suspension heights with a fuzzy logic control, relevant advantage is obtained in terms of lap time reduction.
Two systems, a PID and a Feedforward logic, are studied to implement the control strategy and important differences are found in the stability of tire-ground forces benefiting the latter.
Furthermore, the system was validated in a Driver-In-the-Loop (DIL) static simulator with a more realistic road conditions and important insights in terms of subjective evaluation.
Effects of Flight Controls and Cockpit Layout Design in Rotorcraft-Pilot Couplings: A Computational Approach
Authors: Cocco Alessandro , Zanoni Andrea , Muscarello Vincenzo , Masarati Pierangelo 
Published: November 2020

Abstract
Rotorcraft-Pilot-Coupling (RPC) is a dynamic phenomenon in which the rotorcraft vibrations are transmitted through the cockpit, the seat and the control inceptors to the helicopter pilot and to the passengers. Handling qualities are affected by the proneness of the of rotorcraft to give rise to adverse interactions, an unwanted quality that can be captured by the so called biodynamic feedthrough. In this work, a multibody model of the whole upper body, developed by the authors, is used in order of evaluate the effects of several parameters influencing cockpit layout design: namely, the pilot seat backrest angle, compliance, and connection to the cockpit floor. As a representative parameter of the flight controls design, the effects related to the characteristics of the trim spring is also investigated. Simulations encompass subjects of different anthropometric data, in order to represent possible intra-subject variations. Biomechanical feedthroughs at the collective and cyclic commands, in response to vertical acceleration inputs, are discussed, along with single-harmonic, high magnitude input responses that highlight the presence and importance of nonlinear effects.
Computational Modal Analysis of Half Scale Generic Business Jet Substructures
Authors: Lam Chris B. , Mechefske Chris K. 
Published: November 2020

Abstract
The primary objective of this work was to determine the modal parameters of two substructures of a half scale generic business jet model with pre-stressed skin panels. The effect that pre-stiffened skin panels has on the modal parameters of an aircraft fuselage subsection is not well documented in the literature. First, bending pre-stress on stiffened plates was empirically determined to increase stiffness without changing mode shapes. Second, preliminary finite element models of the substructures determined that the effect of skin pre-stress was significant in one of the two substructures. Finally, an updating technique to account for stiffening effects was proposed and validated to be effectively used in the substructure, improving computational results across all metrics. It is recommended that the model updating procedure developed in this work be used to model skin pre-stress for aircraft fuselage substructures. The improved accuracy of the updated computational model should be of significant interest to the aerospace industry. Future work can be performed to further develop the model updating technique introduced in this work to allow for widespread application.
Design and Development of Hardware to Analyze and Categorize the Condition of Batteries With the Aim of Enabling Their Re-Use
Authors: Wilson Andrew , Wang Qing 
Published: November 2020

Abstract
This paper covers the design and implementation of an accurate, yet flexible test system for Lithium-Ion batteries. The system makes use of linear charge and discharge circuitry to ensure a low noise control, and can support the simultaneous and independent testing of six cells. The system is controlled and data collected by specialist MatLab© software with a user-friendly GUI. Experimental data is processed within the same environment to obtain the desired information. The system makes use of Full Cycle Coulomb Counting and Pulsed DC Load Analysis to obtain estimates for the State of Health (SoH) and State of Charge (SoC) of various cells, and to examine the effect of different use cases on cell performance through repeated testing.
Virtual Reality to Simulate an Inflatable Modular Hydroponics Greenhouse on Mars
Authors: Bruno Francesca , Ceriani Alessandro , Zhan Zhang , Caruso Giandomenico , Del Mastro Antonio 
Published: November 2020

Abstract
A human mission to Mars has long been advocated. As each year the scientific researches bring mankind closer to establishing human habitats on Mars, the question of how astronauts can sustain themselves whilst away from the blue planet becomes crucial.
The project presented in this paper aims at designing and developing the Virtual Reality (VR) simulation of an inflatable modular greenhouse featuring a system that manages the growth of the plants and helps the astronauts control and monitor the whole greenhouse more extensively. The use of VR technology allows simulating an immersive environment of a Mars habitat highlighting its greenhouse overcoming the limitation of physical locations. Wearing the Oculus Rift head-mounted display (HMD) while holding Oculus Rift Touch Controllers, astronauts or Mars exploration enthusiasts could experience the highly interactive and realistic environment. Its goal is to provide training and evaluative simulations of astronauts’ basic tasks and performances in the greenhouse on Mars while testing the growing method of hydroponics equipped with a smart growing controlling and monitoring system.
Variable Degree-of-Freedom Spatial Mechanisms Composed of Four Circular Translation Joints
Authors: Kong Xianwen 
Published: November 2020

Abstract
This paper deals with the construction and reconfiguration analysis of a spatial mechanism composed of four circular translation (G) joints. Two links connected by a G joint, which can be in different forms such as a planar parallelogram, translate along a circular trajectory with respect to each other. A spatial 4G mechanism, which is composed of four G joints, usually has 1-DOF (degree-of-freedom). Firstly, a 2-DOF 4G mechanism is constructed. Then a novel variable-DOF spatial 4G mechanism is constructed starting from the 2-DOF 4G mechanism using the approach based on screw theory. Finally, the reconfiguration analysis is carried out in the configuration space using dual quaternions. The analysis shows that the variable-DOF spatial 4G mechanism has one 2-DOF motion mode and one to two 1-DOF motion modes and reveals how the 4G mechanism can switch among these motion modes. By removing one link from two adjacent G joints each and two links from each of the remaining two G joints, we can obtain a queer-rectangle and a queer-parallelogram, which are the generalization of the queer-square or derivative queer-square in the literature. The approach in this paper can be extended to the analysis of other types of coupled mechanisms using cables and gears and multi-mode spatial mechanisms involving G joints.