bluuebunny/crossref_metadata_2025 huggingface dataset:
Developing Training Tools for Clinicians in LICs: A Qualitative Investigation of the Patient Factors That Influence Prosthetic Prescription
Authors: Walker Michael , Saravanan Pratima , Menold Jessica
Published: November 2020

A prosthesis is a replacement limb that must be functionally sound, comfortably fit, durable, and aesthetically pleasing. Difficulty in prescription is further amplified by each patient’s unique needs and the variability within patient data. The clinician’ s education and prior training is critical in effectively navigating the wealth of patient specific information needed to prescribe a prosthesis and rehabilitation plan that increases the likelihood of long-term patient success. Education and training significantly vary, however, from country to country, and in Lower Income Countries (LIC) a lack of formalized prosthetic training contributes to a lower quality of life for resident amputees. Prosthetists and technicians in LICs face further challenges due to a lack of material resources and formal medical infrastructure. This study was motivated to understand the types of patient information that influence decision-making strategies during prosthetic prescription and compare strategies across expert and novice groups. The results of this study suggest that salient factors are different between each clinician group and is influenced by the complexity of the patients’ case. Activity level of the amputee influenced novices’ prescription, whereas amputee’s motivation, insurance, and health history influenced experts’ prosthetic prescription. Future work exploring the utility of complimentary or supplemental prescription tools, particularly for prosthetists in LICs, is discussed.
Predicting Average Product Lifetime Using Physics-Based Gaussian Process Method in Early Design Stage
Authors: Wei Xinpeng , Han Daoru , Du Xiaoping
Published: November 2020

Average lifetime, or mean time to failure (MTTF), of a product is an important metric to measure the product reliability. Current methods of evaluating MTTF are mainly statistics or data based. They need lifetime testing on a number of products to get the lifetime samples, which are then used to estimate MTTF. The lifetime testing, however, is expensive in terms of both time and cost. The efficiency is also low because it cannot be effectively incorporated in the early design stage where many physics-based models are available. We propose to predict MTTF in the design stage by means of physics-based models. The advantage is that the design can be continually improved by changing design variables until reliability measures, including MTTF, are satisfied. Since the physics-based models are usually computationally demanding, we face a problem with both big data (on the model input side) and small data (on the model output side). We develop an adaptive supervised training method based on Gaussian process regression, and the method can then quickly predict MTTF with minimized number of calling the physics-based models. The effectiveness of the method is demonstrated by two examples.
Investigation of the Effect of Native Oxide Layer on Performance of Interdigitated Impedance-Based Silicon Biochemical Sensors
Authors: Ebrahimpour Tolouei Nadia , Ghamari Shima , Shavezipur Mohammad
Published: November 2020

Chemical and biological detection using Electrochemistry Impedance Spectroscopy (EIS) highly depends on the electrical characteristics of the electrodes used in the measurement process. In this work, the effect of surface coating on behavior of interdigitated impedance-based biochemical sensors is studied. Two interdigitated sensors with the same geometry and different electrode materials are fabricated using a standard process. One electrode is made of gold and the other electrode is made of polycrystalline silicon covered with a thin layer of native silicon dioxide. Different concentrations of di(2-ethylhexyl) phthalate (DEHP) in water are used and the Nyquist responses of the two sensors exposed to these solutions are obtained. The measurement results show that at high frequency both sensors form double-layer capacitance values on their electrode surfaces, however, the silicon sensor has a much lower double-layer capacitance values, because formation of oxide layer adds to the gap between charges at the interface of the electrode and the solution. Moreover, comparing the low frequency regions of the Nyquist plots for two sensors shows that the presence of oxide layer affects the Warburg effect and the charge diffusion near the surface of the electrode, creating an extra capacitive element in series with the diffusion effect. The results of this work may be extended to other interdigitated biochemical sensors that may have other sources of contamination on their surfaces.
Resistive Force Analysis for Design of Rubber Tire Loader Buckets
Authors: Ur Rehman Atta , Awuah-Offei Kwame
Published: November 2020

Rubber tire loaders are key loading equipment for surface and underground mines and can load up to 35 t of material in a pass. Efficient initial penetration into the pile improves the overall efficiency of the loading operation. For efficient design for good initial penetration, we need to predict accurately the resistive forces encountered by these buckets during initial penetration. This paper examines the resistive forces on the bucket as a function of rake angle, height above the digging floor, speed, and tractive effort during initial penetration. A 1/16th scaled representative model of an 18 t (19.8 ton) capacity load haul dump is used for experimentation to understand the effects of rake angle, height above the floor, speed, and tractive effort. The results show that resistive forces are not associated with rake angle or height above the floor but significantly associated with speed, tractive effort, combined effects of speed and tractive effort, and combined effects of speed and rake angle. Future research should focus on conducting similar experiments on different materials and different buckets to better understand the ground engagement for mining and construction equipment. These insights are important to understand the full range of forces during bucket and equipment design.
Tuning Stiffness Nonlinearity: Theory and Applications
Authors: Zanaty Mohamed , Vardi Ilan , Henein Simon
Published: November 2020

Perfect elasticity is not achievable in real-life situation, so spring stiffness is not perfectly constant. In this paper, we study the effect of modifying non-linear stiffness terms while keeping the nominal stiffness constant. We introduce three methods to design and tune linear and nonlinear elastic behavior in the context of compliant mechanisms and we present mechanical realizations. These designs are modeled using Euler-Bernoulli beam theory. Numerical simulation and experimental measurement show a good match with the theoretical model. We then present applications of our stiffness tuning methods to mechanical meta-materials, mechanical resonators, and mechanical computation.
Multi-Fidelity Surrogate Model-Assisted Fatigue Analysis of Welded Joints
Authors: Zhang Lili , Xie Tingli , Hu Jiexiang , Jiang Ping , Koo Jasuk , Choi Seung-Kyum
Published: November 2020

In this study, an additive scaling function based multi-fidelity (ASF-MF) surrogate model is constructed to fast predict fatigue life as well as the stress distribution for the welded single lap joint. The influence of leg length, leg height, the width of the specimen and load in the fatigue test are taken into consideration. In the construction of the MF surrogate model, the finite element model that is calibrated with the experiment is chosen as the high-fidelity (HF) model. While the finite element model that is not calibrated with the experiment is considered as the low-fidelity (LF) model, aiming to capture the trend of the HF model. The Leave-one-out (LOO) verification method is utilized to compare the prediction performance of the ASF-MF surrogate model with that of the single-fidelity Kriging surrogate model. Results show that the ASF-MF surrogate model can better predict the fatigue life as well as the stress distribution.
A Practical Approach for Managing Uncertainty in Remanufacturing: Identifying Leverage Points Using Design Structure Matrix
Authors: Kimita Koji , Matschewsky Johannes , Sakao Tomohiko
Published: November 2020

Remanufacturing is a crucial component for our societies to move toward a circular economy. Compared to new manufacturing, the distinctive nature of remanufacturing is found to have high variability, high uncertainty and, thereby, complexity. Therefore, remanufacturers need to enhance their ability to adjust their systems flexibly. Especially, the ability to reconfigure the production planning and control is crucial for reacting to the high variability and uncertainty. However, few practical methods to do that are available so far. Therefore, to solve this problem, this paper aims to propose a method for designing processes of production planning and control in remanufacturing based on the concept of loosely coupled systems. In the proposed method, Design Structure Matrix (DSM) is applied to identify loosely coupled subsystems that enable to localize impacts of changes within themselves. These subsystems are also utilized to appropriately determine leverage points, as well as allocate human resource. Through the application to a real case of remanufacturing, the proposed method was found to be effective for reconfiguring teams and processes for production planning and control depending on given uncertainties, as well as performing production planning and control activities efficiently.
Innovative Chassis Made From EPP and CFRP of an Urban-Concept Vehicle
Authors: Stabile P. , Ballo F. , Gobbi M. , Mastinu G.
Published: November 2020

The paper presents a fully new chassis of a high efficiency vehicle for the Shell Eco-marathon competition. The chassis is realized by a sandwich structure with an expanded polypropylene (EPP) core and carbon fiber reinforced plastic (CFRP) external skins. The chassis is connected to the body to realize a safe and stiff structure.

Numerical analyses have been performed to assess the stiffness, safety and dynamic eigenfrequencies of the chassis. A Finite Element model of the entire chassis and body was developed. The manufacturing process of the entire chassis and body is described in the paper and some data obtained during on-track tests of the vehicle are presented.

The vehicle reached the 4th place at the 2019 edition of the Shell Eco-marathon competition, with an equivalent energy consumption of 184 km/kWh.
Creativity and Engineering Education: Assessing the Impact of a Multidisciplinary Project Course on Engineering Students’ Creativity
Authors: Deo Saurabh , Hölttä-Otto Katja , Filz Günther H.
Published: November 2020

Creativity is the essential driving force, and creative engineers are the drivers entrusted to propel the technology-driven industry to the pinnacle of innovations in all engineering sectors. Accordingly, creativity is being integrated into engineering education in different ways, from a single lecture to more extensive curriculum level approaches. In this paper, we measured the effect of a multidisciplinary project course, a joint effort between the School of Arts, Design and Architecture, and the School of Engineering, on students’ creativity.

In particular, we assessed the Originality, Novelty, and Quantity of solutions produced by participants in two tasks, an Alternate uses test and the ShapeStorm exercise. An alternate uses test assessed the written form of divergent thinking, and the ShapeStorm exercise assessed the visual form of divergent thinking. Statistical analysis of the data indicated that solutions for the written divergent thinking task produced by students post-course were more novel than pre-course. We did not find a statistically significant improvement in the Quantity. Similarly, for the visual divergent thinking task, we see no statistically significant increase.
Sequential Design Decision Making Under the Influence of Competition: A Protocol Analysis
Authors: Shergadwala Murtuza N. , Panchal Jitesh H.
Published: November 2020

In this study, we focus on crowdsourcing contests for engineering design problems where contestants search for design alternatives. Our stakeholder is a designer of such a contest who requires support to make decisions, such as whether to share opponent-specific information with the contestants. There is a significant gap in our understanding of how sharing opponent-specific information influences a contestant’s information acquisition decision such as whether to stop searching for design alternatives. Such decisions in turn affect the outcomes of a design contest. To address this gap, the objective of this study is to investigate how participants’ decision to stop searching for a design solution is influenced by the knowledge about their opponent’s past performance. The objective is achieved by conducting a protocol study where participants are interviewed at the end of a behavioral experiment. In the experiment, participants compete against opponents with strong (or poor) performance records. We find that individuals make decisions to stop acquiring information based on various thresholds such as a target design quality, the number of resources they want to spend, and the amount of design objective improvement they seek in sequential search. The threshold values for such stopping criteria are influenced by the contestant’s perception about the competitiveness of their opponent. Such insights can enable contest designers to make decisions about sharing opponent-specific information with participants, such as the resources utilized by the opponent towards purposefully improving the outcomes of an engineering design contest.
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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.
Automated Classification of Components for Manufacturing Planning: Single-View Convolutional Neural Network for Global Shape Identification
Authors: Barclay Andrew , Corney Jonathan
Published: November 2020

An experienced engineer can glance at a component and suggest appropriate methods for its manufacture. This skill has been difficult to automate but in recent years Neural Networks have demonstrated impressive image recognition capabilities in many applications. Consequently, this work is motivated by the goal of automating shape assessment for manufacturing. Specifically the reported work investigates the feasibility of training a convolutional neural network (CNN) to recognize 2D images of shapes associated with particular Near Net Shape (NNS) manufacturing processes such as casting, forging, or flow forming. The system uses multiple images generated from 3D CAD models (each manually associated with specific NNS processes) as training data and a single shop floor photograph as a classification query. While multiple views are used to train the CNN only a single view is used to assess the accuracy of the classification. Such single-view classification is designed to support the easy assessment of physical parts observed in manufacturing facilities where it would often be impractical to create an array of images from many viewpoints. The result suggests that despite limitations, single-view CNNs can classify real engineering components for manufacture.
A Novel Bi-Stable Piezoelectric Energy Harvester Inspired by the Venus Flytrap
Authors: Qian Feng , Zuo Lei
Published: November 2020

This paper studies the nonlinear dynamics and energy harvesting performance of a novel bi-stable piezoelectric energy harvester inspired by the rapid shape transition of the Venus flytrap leaves. The harvester is composed of a piezoelectric MFC transducer, a tip mass, and two sub-beams. The two sub-beams are akin to the bidirectionally curved Venus flytrap leaves that could rapidly snap-through from the open state to the closed state. To realize the bistability of the Venus flytrap leaves induced by the stored potential energy, an in-plane pre-displacement constraint is applied to the free ends of the sub-beams. The pre-displacement constraint leads to bending and twisting deformations and creates the potential energy in the harvester. The bio-inspired design is introduced in detail and a prototype is fabricated to validate the conceptual design. The nonlinear dynamics of the bio-inspired bi-stable piezoelectric energy harvester is investigated under base acceleration excitations. Results show that the sub-beams of the harvester experience more complicated local vibrations containing broadband high-frequency components as the snap-through motion happens. The energy harvesting performance of the harvester is evaluated at different excitation levels. The broadband energy harvesting is achieved at higher excitation levels and an average power output of 0.193 mW is attained under the excitation of 10 Hz and 4.0 g.
Static Balancing of Four-Bar Linkages With Torsion Springs by Exerting Negative Stiffness Using Linear Spring at the Instant Center of Rotation
Authors: Franco Jorge A. , Gallego Juan A. , Herder Just L.
Published: November 2020

A design approach for the quasi-static balancing of four-bar linkages with torsion springs is proposed. Such approach is useful on the design of statically balanced compliant mechanisms by setting the stiffness of the Pseudo-Rigid-Body-Model. Here the positive stiffness exhibited by torsion springs at the R-joints is compensated by a negative stiffness function. The negative stiffness is created by a non-zero-free-length linear spring connected between the coupler link and the ground, and where both connecting points trace a line directed to the coupler link’s instant center of rotation. An example of the static balancing of the Burmester’s linkage for straight path generation is developed, where actuation energy is reduced in 94% for a range of motion of 80 degrees for the input link.
Triple-Cell Origami Structure for Multistable Transition Sequences
Authors: Liu Zuolin , Fang Hongbin , Xu Jian , Wang K. W.
Published: November 2020

With the infinite design space and the excellent folding-induced deformability, origami has been recognized as an effective tool for developing reconfigurable structures. Particularly, the multistable origami structure, which possesses more than one stable configuration that is distinct in shape and mechanical properties, has received wide research attention. Generally, the origami structure reaches a kinematic singularity point when switching among different stable configurations. At this critical state, multiple switching sequences are possible, and the actual transition is generally hard to predict. In this paper, evolving from the conventional bistable Miura-ori unit, a triple-cell origami structure with eight potential stable configurations is proposed, which serves as a platform for investigating the transition sequences among different stable configurations. To quantify the overall elastic potential of the structure, besides the conventional elastic energy originating from the rigid folding creases, extra elastic potential induced by the mismatch among the cells are introduced, so that folding of the triple-cell structure is no longer a strict single degree-of-freedom mechanism. Instead, the three cells can deform asynchronously to avoid reaching the kinematic singularity point. Hence, under displacement loading, the transition sequence of the multistable structure is predicted by performing optimization on the elastic potential energy. It shows that sequences with multifarious characteristics are possible, including reversible and irreversible transitions, and transitions with symmetric and asymmetric energy barriers. Considering that the fundamental transition mechanisms are of great significance in understanding the quasi-static and dynamic behaviors of multistable structures, the results could be potentially employed for developing morphing structures, adaptive metamaterials, and mechanical logic gates.
A Framework for a Motorcycle Design Computer-Based Intelligent Tool
Authors: Agius Sean , Farrugia Philip , Francalanza Emmanuel
Published: November 2020

Motorcycle riders’ road experience, attitude and position have a large impact on safety. Besides these aspects, a motorcycle designer has to consider the aesthetical emotional value of such artefacts. This paper contributes a novel framework architecture supports designers to develop a rider-centred, safer motorcycle design, while at the same time considering human factors and the emotional values of such artefacts. This paper explores the requirements for this framework through a validated mixed method approach, gaining input from interviewed designers, stakeholders as well as surveyed riders. The proposed framework takes a user-centred approach, placing designers and riders at the framework’s core. Riders are an essential aspect as they assist in generating the knowledge which is critical to the operation of the framework. The framework acts as a support to the motorcycle designer, where it couples the intellectual resources of the designer with the knowledge capabilities of the framework to proactively support motorcycle design decision making. The proposed framework is driven by a harmonisation engine, where the aesthetic, ergonomic, persona and market trend domains are harmonised to achieve a balanced motorcycle design solution. The framework architecture will be employed to implement an intelligent computer-based motorcycle design support tool, in future work.
Passive Mechanical Metamaterial Sensor and Actuator
Authors: Waheed U. , Myant C.
Published: November 2020

In recent years, and with the continual development of additive manufacturing technologies, mechanical metamaterials have been explored for their programmable nature. This has opened a new design space into devices using functional materials. In this paper, a novel mechanical metamaterial device is designed, combining anisotropic 3D unit cells to slender beams. By controlling the separation distance between the fixed ends of a slender beam, the mechanism can be tuned to transition between monostable and bistable states. This behaves as a sensor and actuator, allowing mechanical signals to pass only when the correct actuation pattern is received. The device is shown to be inherently passive as it returns to a monostable state after actuation. Two different designs have successfully demonstrated this repeatable behaviour. A multi-material PolyJet printed mechanism joining unit cells to a Von Mises Truss, and an SLA printed compliant mechanism coupling unit cells to thin slender beams. A novel approach in performing AND/OR mechanical logic has also been successfully demonstrated by manipulating the mechanical metamaterial when in a bias state. The proposed devices have application in soft robotic systems, the aerospace industry and in the nuclear sector, where there is a need for passive safety systems that are not reliant on electronic systems, and respond to environmental stimuli. The printed mechanisms highlight the potential for mechanical metamaterials to be used as tunable sensors and actuators for future engineering applications.
Building Block Based Topology Synthesis Algorithm to Optimize the Natural Frequency in Large Stroke Flexure Mechanisms
Authors: Fix Mathijs E. , Brouwer Dannis M. , Aarts Ronald G. K. M.
Published: November 2020

Flexure based compliant mechanisms suited for a large range of motion can be designed by handling the challenges arising from combining low compliance in the desired directions, high support stiffness, low stresses and high unwanted natural frequencies. Current topology optimization tools typically can’t model large deflections of flexures, are too conceptual or are case specific. In this research, a new spatial topological synthesis algorithm based on building blocks is proposed to optimize the performance of an initial design. The algorithm consists of successive shape optimizations and layout syntheses. In each shape optimization the dimensions for some layout are optimized. The layout synthesis strategically replaces the most “critical” building block with a better option. To maximize the first unwanted natural frequency the replacement strategy depends the strain energy distribution of the accompanying mode shape. The algorithm is tested for the design of a 1-DOF flexure hinge. The obtained final layout agrees with results known from literature.