bluuebunny/crossref_metadata_2025_split huggingface dataset:
Can Online Customer Reviews Help Design More Sustainable Products? A Preliminary Study on Amazon Climate Pledge Friendly Products
Authors: Saidani Michael , Kim Harrison , Ayadhi Nawres , Yannou Bernard 
Published: November 2021

Abstract
Online product reviews are a valuable resource for product developers to improve the design of their products. Yet, the potential value of customer feedback to improve the sustainability performance of products is still to be exploited. The present paper investigates and analyzes Amazon product reviews to bring new light on the following question: “What sustainable design insights can be identified or interpreted from online product reviews?”. To do so, the top 100 reviews, evenly distributed by star ratings, for three product categories (laptop, printer, cable) are collected, manually annotated, analyzed and interpreted. For each product category, the reviews of two similar products (one with environmental certification and one standard version) are compared and combined to come up with sustainable design solutions. In all, for the six products considered, between 12% and 20% of the reviews mentioned directly or indirectly aspects or attributes that could be exploited to improve the design of these products from a sustainability perspective. Concrete examples of sustainable design leads that could be elicited from product reviews are given and discussed. As such, this contribution provides a baseline for future work willing to automate this process to gain further insights from online product reviews. Notably, the deployment of machine learning tools and the use of natural language processing techniques to do so are discussed as promising lines for future research.
In-Situ Temperature Monitoring of ABS During Fused Filament Fabrication (FFF) Process With Varying Process Parameters
Authors: Mahmoud Youmna , Manoochehri Souran 
Published: November 2021

Abstract
Fused Filament Fabrication (FFF) is presently one of the most commonly used Additive Manufacturing (AM) technology for various engineering applications. However, accuracy and stability remain a major challenge during AM processes. FFF is inherently a thermal process. So, it is important to analyze and monitor the temperature evolution of each deposited filament during and after printing. This work presents an in-situ temperature measurement setup with an infrared camera, used in collecting temperature profiles of printed layers. These temperature profiles were compared to a theoretical 1D heat transfer model, demonstrating good agreement between the two sets of data. The temperature measurement experiment has been repeated for different printing process parameters, namely print speed, flowrate, and bed temperature. The effect of fan cooling is also studied. These data play a significant role in determining the optimal settings needed to achieve the desired bonding between adjacent filaments. This can be concluded by studying the effect of changing the parameters on the cooling of each deposited filament concerning the material’s glass transition temperature. The average temperature of any two adjacent layers in a part has been evaluated and compared to the material’s glass transition temperature to provide a better insight on the quality of adhesion taking place. A visual inspection of the part has also been proven to be useful in evaluating the effect on the final quality.
A MEMS Tunable Capacitor With Dual Deformation Modes and High Tunability and Linearity
Authors: Shahi Mahdi , Shavezipur Mohammad 
Published: November 2021

Abstract
MEMS tunable capacitors have applications in tunable filters and RF circuits where high tunability and Q-factor are desired. Conventional parallel-plate tunable capacitors have a highly nonlinear capacitance-voltage (C-V) response and limited tunability of up to 50% due to fundamental limitation and structural instability. In this work, we present a novel design idea for a parallel-plate tunable capacitor that increases the tuning ratio and provides a smoother (more linear) response. The design uses two modes of deformation, rigid-body displacement of a curved moving electrode before pull-in and deforming the plate after pull-in, and exploits nonlinear structural stiffness to improve the linearity (and the tunability) of the tunable parallel-plate capacitor. The capacitor structure is designed such that when actuation voltage is applied, first the beams holding the moving electrode deform, and capacitance increase similar to conventional design up to pull-in. After the pull-in, the top electrode (which has a curved geometry) is deformed and further increases the capacitance, as the voltage increases. The design may provide an overall simulated tunability of more than 380%, and also has a more linear C-V response. The design is modeled and simulated using ANSYS coupled-field multiphysics solver and the effect of different design parameters are investigated. The simulation results show much high tunability and better linearity than conventional parallel-plate capacitors.
Numerical Analysis and Parameter Optimization of a Portable Two-Body Attenuator Wave Energy Converter
Authors: Capper Joseph , Mi Jia , Li Qiaofeng , Zuo Lei 
Published: November 2021

Abstract
Easily portable, small-sized ocean wave energy converters (WECs) may be used in many situations where large-sized WEC devices are not necessary or practical. Power maximization for small-sized WECs amplifies challenges that are not as difficult with large-sized devices, especially tuning the device’s natural frequency to match the wave frequency and achieve resonance. In this study, power maximization is performed for a small-sized, two-body attenuator WEC with a footprint constraint of about 1m. A thin, submerged tuning plate is added to each body to increase added mass without significantly increasing hydrostatic stiffness in order to reach resonance. Three different body cross-section geometries are analyzed. Device power absorption is determined through time domain simulations using WEC-Sim with a simplified two-degree-of-freedom (2DOF) model and a more realistic three-degree-of-freedom (3DOF) model. Different drag coefficients are used for each geometry to explore the effect of drag. A mooring stiffness study is performed with the 3DOF model to investigate the mooring impact. Based on the 2DOF and 3DOF power results, there is not a significant difference in power between the shapes if the same drag coefficient is used, but the elliptical shape has the highest power after assigning a different approximate drag coefficient to each shape. The mooring stiffness study shows that mooring stiffness can be increased in order to increase relative motion between the two bodies and consequently increase the power.
A Method to Develop Virtual Reality Platforms for the Medical Rehabilitation of Severe Memory Loss After Brain Stroke
Authors: Lanzoni Daniel , Vitali Andrea , Regazzoni Daniele , Rizzi Caterina 
Published: November 2021

Abstract
The paper presents a method to develop Virtual Reality (VR) platforms based on serious games for the rehabilitation of severe memory loss. In particular, it is related to retrograde amnesia, a condition affecting patient’s quality of life usually caused by brain stroke. Nowadays, the standard rehabilitation process consists in showing pictures of patient’s familiar environments in order to recover the memory. Past research works have investigated the use of 3D scanners for the virtualization of real environment and virtual reality for the generation of more immersive interaction to design serious games for neurocognitive rehabilitation. Reached results highlighted a time-consuming development process to interface each new environment with the game logic specifically developed for the serious games. Furthermore, a complete VR platform must also consider the medical monitoring and the data management oriented to a more objective medical assessment.
The proposed method allows the design of VR platforms based on patient-specific serious games for memory loss starting from the 3D scanning acquisition of familiar environments. The 3D acquisition is performed using the Occipital Structure Sensor and the Skanect application. A modular procedure has been designed to interface the virtual objects of each acquired environment with the modules of the game-logic developed with Unity. The immersive Virtual Reality is based on the use of the HTC Vive Pro head mounted display. Furthermore, the method permits to associate the patient-specific serious game to a set of software modules for the medical monitoring and the data management for the generation of reports useful for the evaluation. The solution has been evaluated by measuring the time needed to develop a whole VR platform for two different familiar environments. Less than 5 hours are required to complete the design process.
Manufacturing Fixation in Design: Exploring the Effects of Manufacturing Assumptions on Design Ideas
Authors: Bracken Brennan Jennifer , Miney William B. , Simpson Timothy W. , Jablokow Kathryn W. 
Published: November 2021

Abstract
Designing successfully for any new or unfamiliar manufacturing technology requires an ability to look beyond the manufacturing limitations that have constrained one’s design ideas in the past. However, potential cognitive bias or fixation on familiar manufacturing processes may make this a challenge for designers. In this paper we introduce the novel concept of Manufacturing Fixation in Design (MFD), which we define as unconscious and often unintentional adherence to a limited set of manufacturing processes and/or constraints and capabilities during the design ideation process. This concept is explored as a subset of design fixation, a cognitive bias often experienced by designers and engineers. After reviewing related literature in design fixation, we introduce MFD as a type of design fixation and explore ways in which fixation on manufacturing might be assessed. We then offer an exploratory case study involving design for additive manufacturing, an advanced manufacturing technology that has seen considerable interest lately. The case study involves a Design for Additive Manufacturing workshop given at an aerospace technology company headquartered in the United States with participants who are professional engineering designers. Results from the study are used to explore how MFD manifests and how its impact in design and optimization for manufacturing might be measured. Future research and next steps to validate the existence of MFD are also discussed.
Logic Rules for Automated Synthesis of Function Models Using Evolutionary Algorithms
Authors: Gill Amaninder Singh , Sen Chiradeep 
Published: November 2021

Abstract
This paper presents the development of logic rules for evaluating the fitness of function models synthesized by an evolutionary algorithm. A set of 65 rules for twelve different function verbs are developed. The rules are abstractions of the definitions of the verbs in their original vocabularies and are stated as constraints on the quantity, type, and topology of flows connected to the functions. The rules serve as an objective and unambiguous basis of evaluating the fitness of function models developed by a genetic algorithm. The said algorithm and the rules are implemented in software code, which is used to both demonstrate and validate the efficacy of the rule-based approach of converging function model synthesis using GAs.
Guidelines for Optimizing the Error in Area Ratio Damping Estimation Method
Authors: Santoshkumar Balija , Khasawneh Firas A. 
Published: November 2021

Abstract
The logarithmic decrement (log-dec) is one of the most popular methods for viscous damping estimation in linear, single degree of freedom systems. It estimates the damping ratio by examining the decay in the amplitude between two peaks some number of cycles apart. The accuracy in the estimation is sensitive to the chosen number of cycles, where the latter can be optimized such that the uncertainty in the estimation is minimized. However, the log-dec method is not suitable for systems with high damping ratios (approximately &gt; 0.3). Another recent approach for damping estimation is based on considering a ratio of the amplitudes of the positive and negative areas in the free response of the oscillator. Although prior works on the areas method only tested lightly damped systems, we show here that — in contrast to log-dec — this approach can estimate the damping ratio over the whole range of underdamped linear oscillators. However, in contrast to log-dec, there are no available guidelines on how many areas to include in the damping estimation. In this work, we derive uncertainty analysis expressions for the areas method and we utilize them to obtain the optimal number of areas to use. Our results show that for a very low damping ratio (&lt; 0.01), choosing more than two areas in the estimation increases the uncertainty. In contrast, for moderate to high damping (between 0.05 and 1), we need to consider all the available areas in the estimation. One caveat in the range of high damping (between 0.3 and 1) is that while it is desirable to include all the available areas, uncertainty increases when considering up to 3 areas. Therefore, if only 4 areas are available in this range, then to reduce the uncertainty in the estimate only the first two areas must be considered. The results are verified using a large number of numerical simulations including different levels of noise.
Value of Information for Continuous Monitoring Systems in Recurrent Maintenance Decision Scenarios
Authors: Liu Xinyang , Wang Pingfeng 
Published: November 2021

Abstract
Monitoring systems play a crucial role in improving system failure resilience and preventing tragic consequences brought by unexpected system failure and saving the consequential high cost. Continuous monitoring systems have been applied to diversified systems for well-informed operations. Although plenty research has devoted to predicting system states using the continuous data flow, there still lacks a systematic decision-making framework for system designers and engineering system owners to maximize their benefits on adopting monitoring systems. This paper constructs such a decision-making framework, with which system owners can evaluate the operation cost change under specific operation modes considering the effectiveness of continuous monitoring systems in predicting system failures. Two case studies have been conducted to illustrate the value evaluation of the monitoring information and the system maintenance process with the aid of different prognostic results based on the monitoring data. The first case study considers a health-state prediction with fixed accuracy while the second one incorporates the accuracy improvement as the monitoring data accumulates. Results show that the value of monitoring systems will be influenced by the deviation among the equipment group, the accuracy of system-state prediction, and different types of cost involved in the operating process. And the adjustment of maintenance actions based on monitoring and prognosis information will help improve the value of monitoring systems.
SuperMeshing: A New Deep Learning Architecture for Increasing the Mesh Density of Metal Forming Stress Field With Attention Mechanism And Perceptual Features
Authors: Xu Qingfeng , Nie Zhenguo , Xu Handing , Zhou Haosu , Liu Xinjun 
Published: November 2021

Abstract
In stress field analysis, the finite element method is a crucial approach, in which the mesh-density has a significant impact on the results. High mesh density usually contributes authentic to simulation results but costs more computing resources, leading to curtailing efficiency. To eliminate this drawback, we propose a new data-driven mesh-density boost model named SuperMeshingNet that strengthens the advantages of finite element analysis (FEA) with low mesh-density as inputs to the deep learning model, which consisting of Res-UNet architecture, to acquire high-density stress field instantaneously, shortening computing time and cost automatically. Moreover, the attention mechanism and the perceptual features are utilized, enhancing the performance of SuperMeshingNet. Compared with the baseline that applied the linear interpolation method, SuperMeshingNet achieves a prominent reduction in the mean squared error (MSE) and mean absolute error (MAE) on the test data, which contains prior unseen cases. Based on the dataset of the plane stress fields in sheet metal forming, the comparative experiments are proceeded to demonstrate the high quality and superior precision of the reconstructed results generated by the proposed model. The well-trained model can successfully show more excellent performance than the baseline models on the multiple scaled mesh-density, including 2×, 4×, and 8×. Enhanced by SuperMeshingNet with broaden scaling of mesh density and high precision output, FEA can be accelerated with seldom computational time and cost. We publicly share our work with full detail of implementation at https://github.com/zhenguonie/2021_SuperMeshing_2D_Metal_Forming.
Identify Finger Rotation Angles With ArUco Markers and Action Cameras
Authors: Yuan Tianyun , Song Yu (Wolf) , Kraan Gerald A. , Goossens Richard H. M. 
Published: November 2021

Abstract
Measuring the motion of human hand joints is a challenging task due to the high number of DOFs. In this study, we proposed a low-cost hand tracking system built on action cameras and ArUco markers to measure finger joint rotation angles. The lens distortion of each camera was corrected first via intra-calibration and the videos of different cameras were aligned to the reference camera using a dynamic time warping based method. Two methods were proposed and implemented for extracting the rotation angles of finger joints: one is based on the 3D positions of the markers via inter-calibration between cameras, named pos-based method; the other one is based on the relative marker orientation information from individual cameras, named rot-based method. An experiment was conducted to evaluate the effectiveness of the proposed system. The right hand of a volunteer was included in this practical study, where the movement of the fingers was recorded and the finger rotation angles were calculated with the two proposed methods, respectively. The results indicated that although using the rot-based method may collect less data than using the pos-based method, it was more stable and reliable. Therefore, the rot-based method is recommended for measuring finger joint rotation in practical setups.
Multiple Harmonic Lissajous Scanning Patterns for Endomicroscopy With Parametrically-Resonant Micro-Mirrors
Authors: Chan Nicholas , Lee Miki , Lee Haijun , Wang Thomas D. , Oldham Kenn R. 
Published: November 2021

Abstract
Parametrically-resonant micro-mirrors have proven highly effective in generating large amplitude, high-frequency motion for laser scanning in endoscopic microscopy, using Lissajous scan patterns to create a 2D image. However, Lissajous scanning produce low scanning density at the center of an image. In this work, a strategy for exciting additional harmonic behavior in a parametrically-resonant electrostatic micro-mirror is identified. The mirror’s nonlinear dynamics produce substantial changes in natural frequency with bias voltage. This allows a biased, duty-cycled square wave to produce higher harmonic oscillations that can be naturally synchronized with primary resonance in integer ratios. This in turn allows for alternate scanning distributions in 2D imaging that increase pixel sampling near the center of an image. In simulation, use of a biased square wave signal is found to be capable of producing steady-state oscillations at first and third harmonics of natural frequency for a micro-mirror operated via parametric resonance. Multiple harmonic motion is also realized without increasing input voltage amplitude beyond nominal amplitude, with a greater than 50% reduction fill time for the center of the imaging region. Parameterized relationships between input and output oscillations are identified, sensitivity of output trajectory to model variation, and impact on scanning density for a 2D MEMS micro-mirror are briefly described.
Seamless Shifting Control Based on Power Balance Method in Emergency Braking Condition
Authors: Lu Zhenghong , Song Jian , Yu Liangyao 
Published: November 2021

Abstract
Implementation of multi-speed transmission for battery electric vehicle (BEV) is considered as one of effective methods in improving dynamic capability, reducing power consumption and downsizing the powertrain. Considering two trends of automotive technology, advanced driving assistance system (ADAS) and BEV electric powertrain, equipped with a two-speed transmission, can enhance the performance of automatic emergency braking systems (AEBS). When downshift operation is executed during regenerative braking, the multi-speed transmission in first gear can provide adequate brake force capacity for hard braking requirement, improving efficiency of energy recovery of drive motor as well as eliminating the dynamic lag of active braking booster. To ensure both the braking command and downshift requirement, a seamless clutch-to-clutch shifting control strategy based on power balance method is developed by analyzing the frictional work of two friction clutches inside the gear box and the transformation process of kinetic energy of rotational mechanical elements. To maintain the total energy balance of the powertrain as the control objective, the active power compensation of the drive motor during shifting fills the energy gap of the whole transmission system. With the estimation of clutches’ friction torque and shifting stage analyzer, optimal drive motor torque command can be numerically solved and a complete shifting control flow is designed in this study. Simulation results demonstrate the effectiveness of proposed control method in achieving seamless downshifting during regenerative braking, enhancing AEBS performance and improving energy recovery efficiency.
Machine Learning to Predict Medical Devices Repair and Maintenance Needs
Authors: Liao Hao-yu , Boregowda Karthik , Cade Willie , Behdad Sara 
Published: November 2021

Abstract
Products often experience different failure and repair needs during their lifespan. Prediction of the type of failure is crucial to the maintenance team for various reasons, such as realizing the device performance, creating standard strategies for repair, and analyzing the trade-off between cost and profit of repair. This study aims to apply machine learning tools to forecast failure types of medical devices and help the maintenance team properly decides on repair strategies based on a limited dataset. Two types of medical devices are used as the case study. The main challenge resides in using the limited attributes of the dataset to forecast product failure type. First, a multilayer perceptron (MLP) algorithm is used as a regression model to forecast three attributes, including the time of next failure, repair time, and repair time z-scores. Then, eight classification models, including Naïve Bayes with Bernoulli (NB-Bernoulli), Gaussian (NB-Gaussian), Multinomial (NB-Multinomial) model, Support Vector Machine with linear (SVM-Linear), polynomial (SVM-Poly), sigmoid (SVM-Sigmoid), and radical basis (SVM-RBF) function, and K-Nearest Neighbors (KNN) are used to forecast the failure type. Finally, Gaussian Mixture Model (GMM) is used to identify maintenance conditions for each product. The results reveal that the classification models could forecast failure type with similar performance, although the attributes of the dataset were limited.