bluuebunny/crossref_metadata_2025_split huggingface dataset:
Real-Time Measurement Method of Rail Vehicle Wheel Flange Wear Using Inductive and Temperature Sensors
Authors: Ndodana Njaji James , Nkundineza Celestin 
Published: November 2021

Abstract
Railway systems are expected to be in their most safe state ensuring safe operations of trains. The contact between rail and wheel is one of the most fundamental aspects in railway systems. Therefore, instruments and measurement systems that give critical information about operating railway systems are expected to work efficiently and effectively. Previous research on using inductive displacement sensor for wheel flange wear measurement has focused on the light rail vehicles, and considered effect of temperature negligible, as opposed to mainline vehicle where the wheel temperature variation is large. Also, the filtering of noises has been done using non-real time data. This paper focuses on the real-time measurement system for freight vehicle wheel flange wear by fusing measurement data from inductive displacement and thermocouple sensors, and filtering of the noises. A CAD model of the sensor support fixed on the bogie frame is presented. Several experimental measurements are carried in the lab on a moving disk heated at different temperatures. Then multiple regression analysis of the data is carried out to come up with the measurement model equation. This equation is used for calibration in LabVIEW program interfaced with data acquisition system for real time measurement. Experimental results show effect of temperature on the inductive sensor measurement data. This effect is taken into account to quantify the clearance between the disk and the sensor tip. The precision and accuracy are determined to be 0.06 Volts and 0.03 mm, respectively. This system is expected to enhance the real-time and/or online monitoring of the safety of rail vehicles. Also, it can be integrated with Automatic Train Protection (ATP) and the detection of track lateral irregularities from the wheel flange real time measurement data.
The Importance of the Mode II Term on the Analysis of Angled Cracks in Unidirectional Carbon Fiber Composites
Authors: Biddlecom Jacob , Pataky Garrett J. 
Published: November 2021

Abstract
Carbon fiber reinforced polymers (CFRP) have been used in many high-performance applications where strength to weight ratio is an important characteristic. The goal of this research was to analyze the effects of Mode II, also known as shear loading, on the displacement fields surrounding a crack for unidirectional carbon fiber composites. Tensile and fatigue experiments were conducted on angled unidirectional CFRP coupled with digital image correlation (DIC) to analyze the full field displacement. Angled CFRP cracks experienced mixed mode loading which required addition insight due to the complex stresses on the fiber/matrix interface. The experimental displacement fields acquired from DIC were used as inputs for an anisotropic regression analysis to determine the mode I and mode II stress intensity factor ranges. The results from the regression analysis were used to predict the displacement fields around a crack. When comparing the experimental results with the predicted results, the inclusion of Mode II increased the agreement between predicted and experimental displacement fields around a crack tip for two different fiber orientation angles. Crack growth rate analysis and analytical stress intensity factor ranges were used to expand on the agreement of the results as well as bring to light CFRP specific fracture mechanisms that lead to disagreements.
Multi-Sensor Data Fusion for Rotating Machinery Fault Diagnosis Using Residual Convolutional Neural Network
Authors: Xie Tingli , Huang Xufeng , Choi Seung-Kyum 
Published: November 2021

Abstract
Diagnosis of mechanical faults in the manufacturing systems is critical for ensuring safety and saving cost. With the development of data transmission and sensor technologies, the measuring systems can easily acquire multi-sensor and massive data. The traditional fault diagnosis methods usually depend on the features extracted by experts manually. The feature extraction process is usually time-consuming and laborious, which has a significant impact on the final results. Although Deep-Learning (DL) provides an end-to-end way to address the drawbacks of traditional methods, it is necessary to do deep research on an intelligent fault diagnosis method based on Multi-Sensor Data and Data Fusion. In this project, a novel intelligent diagnosis method based on Multi-Sensor Data Fusion and Convolutional Neural Network (CNN) is explored, which can automatically extract features from raw signals and achieve superior recognition performance. Firstly, a Multi-Signals-to-RGB-Image conversion method based on Principal Component Analysis (PCA) is applied to fuse multi-signal data into three-channel RGB images, which can eliminate the effect of handcrafted features and obtain the feature-level fused information. Then, the improved CNN with residual networks and the Leaky Rectified Linear Unit (LReLU) is defined and trained by the training samples, which can balance the relationship between computational cost and accuracy. After that, the testing data are fed into CNN to obtain the final diagnosis results. Two datasets, including the KAT bearing dataset and Gearbox dataset, are conducted to verify the effectiveness of the proposed method. The comprehensive comparison and analysis with widely used algorithms are also performed. The results demonstrate that the proposed method can detect different fault types and outperform other methods in terms of classification accuracy. For the KAT bearing dataset and Gearbox dataset, the proposed method’s average prediction accuracy is as high as 99.99% and 99.98%, which demonstrates that the proposed method achieves more reliable results than other DL-based methods.
Tolerancing for an Apple Pie
Authors: Campbell Joshua Roland , Hazelrigg George A. 
Published: November 2021

Abstract
Tolerancing began with the notion of limits imposed on the dimensions of realized parts both to maintain functional geometric dimensionality and to enable cost-effective part fabrication and inspection. Increasingly however, component fabrication depends on more than part geometry as many parts are fabricated as a result of a “recipe” rather than dimensional instructions for material addition or removal. Referred to as process tolerancing, this is the case, for example, with IC chips. There has emerged an extensive literature on both geometric and process tolerancing, much of it with a focus on tolerance optimization. In the case of tolerance optimization, a typical objective is cost minimization while achieving required functionality or “quality.” This paper takes a different look at tolerances, suggesting that rather than ensuring merely that parts achieve a desired functionality at minimum cost, the underlying goal of product design is to make money, and tolerances comprise additional design variables amenable to optimization in a decision theoretic framework. We recognize that a theory of tolerancing must apply equally to processes and procedures, and note that tolerances introduce additional product attributes that relate to product characteristics such as consistency, quality, reliability and durability. These important attributes complicate the computation of the expected utility of candidate designs, requiring additional computational steps for their determination. Nonetheless, their proper consideration is an important element in the evaluation of product design alternatives. The resulting theory of tolerancing presented here is illustrated using the example of tolerancing for an apple pie.
Conformal Topology Optimization of Heat Conduction Problems on Manifolds Using an Extended Level Set Method (X-LSM)
Authors: Xu Xiaoqiang , Chen Shikui , Gu Xianfeng David , Wang Michael Yu 
Published: November 2021

Abstract
In this paper, the authors propose a new dimension reduction method for level-set-based topology optimization of conforming thermal structures on free-form surfaces. Both the Hamilton-Jacobi equation and the Laplace equation, which are the two governing PDEs for boundary evolution and thermal conduction, are transformed from the 3D manifold to the 2D rectangular domain using conformal parameterization. The new method can significantly simplify the computation of topology optimization on a manifold without loss of accuracy. This is achieved due to the fact that the covariant derivatives on the manifold can be represented by the Euclidean gradient operators multiplied by a scalar with the conformal mapping. The original governing equations defined on the 3D manifold can now be properly modified and solved on a 2D domain. The objective function, constraint, and velocity field are also equivalently computed with the FEA on the 2D parameter domain with the properly modified form. In this sense, we are solving a 3D topology optimization problem equivalently on the 2D parameter domain. This reduction in dimension can greatly reduce the computing cost and complexity of the algorithm. The proposed concept is proved through two examples of heat conduction on manifolds.
Exploring Empathy in Student Design Teams
Authors: Apfelbaum Meredith , Sharp Kendra , Dong Andy 
Published: November 2021

Abstract
The objective of this paper is to develop a methodology to better understand behavioral empathy in the design process for the purpose of addressing user needs. To accomplish this, content analysis was conducted on undergraduate student assignments that documented group projects designing a consumer product. Using qualitative data analysis, the assignments and presentations were coded for their levels of behavioral empathy, using a scale that applied psychology and design theories. The Interpersonal Reactivity Index was administered to the students to assess their trait empathy. Results from these two analyses showed little connection between levels of behavioral empathy and self-assessed trait empathy of the student groups. The student assignments did reveal empathic waves that demonstrated comprehension and application of expressed user needs, evidenced by ascending and descending the empathy scale. These results indicate that is it not trait empathy that leads to empathic design, but rather applied empathy in the design process; developing internal empathy is not sufficient if it does not effectively translate user needs to technical requirements in the final design.
“No, We’re Not!” the Classification and Impact of Blocking Behavior in Design Team Meetings
Authors: Mitchell John , Henderson Daniel , Halleran Grace , Singh Aditya , Jablokow Kathryn , Sonalkar Neeraj , Edelman Jonathan 
Published: November 2021

Abstract
This paper proposes a framework through which emergent disagreements during design team meetings may be classified by their tendency to reinforce or challenge the status quo. We investigate the use of the framework in measuring the effects of these disagreements on team productivity. The Interaction Dynamics Notation (IDN) [1] provides the basis for identifying disagreements through blocking behavior. Kirton’s Adaption-Innovation (A-I) Theory [2] is used to classify the style of a Block as Adaptive (status quo-preserving) or Innovative (status quo-challenging). Emergent disagreements like Blocks are potentially risky during team meetings, because they obstruct the flow of discussion. However, certain styles of Blocks, as classified using A-I Theory, may be better suited to idea generation and thus, be worth the risk.
This paper addresses the following questions: (1) Can IDN Blocks be reliably classified using A-I Theory constructs? (2) How do different styles of Blocks impact productivity (e.g., the generation of ideas) during design meetings? A set of 104 Blocks observed in 14 academic teams during design ideation sessions was coded using the proposed framework and reliably determined to be Adaptive, Innovative, or Neither. Blocks were not entirely inhibitory and led to new design ideas under certain conditions. Neither Adaptive nor Innovative Blocks led to more efficient generation of ideas. Innovative Block sequences had greater variance in length than Adaptive Block sequences, while Deflected Blocks more frequently led to idea generation than Overcome or Unresolved Blocks. This research has implications for the productivity of design teams in their problem solving and decision making across settings in academia and industry.
Design and Analysis of a Contact-Aided Variable Stiffness Flexure Hinge (CVSFH)
Authors: Dai Shenyuan , Qiu Lifang , Chen Qichao , Li Yanlin 
Published: November 2021

Abstract
Flexure hinges are the basis of compliant mechanisms. The stiffness is one of the important indexes to evaluate the performance of a flexure hinge, and the rotation angle when the stiffness changes affects its motion characteristics. Thus, based on the constant rectangular cross-section flexure hinge and contact interaction, this paper proposed a contact-aided variable stiffness flexure hinge (CVSFH). With the deformation under an external load, the contact interaction with different parts of the CVSFH itself can achieve the purpose of variable stiffness. The equivalent mechanical model is built and the theoretical equation of the stiffness is given. CVSFHs with different dimensions are designed, and a finite element analysis (FEA) is done. The FEA results of the design examples are coincide with the theoretical results, which verifies the feasibility of the design and the correctness of the theoretical equation.
A Machine Learning Method for State of Charge Estimation in Lead-Acid Batteries for Heavy-Duty Vehicles
Authors: Luciani Sara , Feraco Stefano , Bonfitto Angelo , Tonoli Andrea , Amati Nicola , Quaggiotto Maurizio 
Published: November 2021

Abstract
In the automotive framework, an accurate assessment of the State of Charge (SOC) in lead-acid batteries of heavy-duty vehicles is of major importance. SOC is a crucial battery state that is non-observable. Furthermore, an accurate estimation of the battery SOC can prevent system failures and battery damage due to a wrong usage of the battery itself. In this context, a technique based on machine learning for SOC estimation is presented in this study. Thus, this method could be used for safety and performance monitoring purposes in electric subsystem of heavy-duty vehicles. The proposed approach exploits a Genetic Algorithm (GA) in combination with Artificial Neural Networks (ANNs) for SOC estimation. Specifically, the training parameters of a Nonlinear Auto-Regressive with Exogenous inputs (NARX) ANN are chosen by the GA-based optimization. As a consequence of the GA-based optimization, the ANN-based SOC estimator architecture is defined. Then, the proposed SOC estimation algorithm is trained and validated with experimental datasets recorded during real driving missions performed by a heavy-duty vehicle. An equivalent circuit model representing the retained lead-acid battery is used to collect the training, validation and testing datasets that replicates the recorded experimental data related to electrical consumers and the cabin systems or during overnight stops in heavy-duty vehicles. This article illustrates the architecture of the proposed SOC estimation algorithm along with the identification procedure of the ANN parameters with GA. The method is able to estimate SOC with a low estimation error, being suitable for deployment on common on-board Battery Management Systems (BMS).
LSTM-Enabled Level Curve Tracking in Scalar Fields Using Multiple Mobile Robots
Authors: Parikh Kunj J. , Wu Wencen 
Published: November 2021

Abstract
In this work, we investigate the problem of level curve tracking in unknown scalar fields using a limited number of mobile robots. We design and implement a long short term memory (LSTM) enabled control strategy for a mobile sensor network to detect and track desired level curves. Based on the existing work of cooperative Kalman filter, we design an LSTM-enhanced Kalman filter that utilizes the sensor measurements and a sequence of past fields and gradients to estimate the current field value and gradient. We also design an LSTM model to estimate the Hessian of the field. The LSTM enabled strategy has some benefits such as it can be trained offline on a collection of level curves in known fields prior to deployment, where the trained model will enable the mobile sensor network to track level curves in unknown fields for various applications. Another benefit is that we can train using larger resources to get more accurate models, while utilizing a limited number of resources when the mobile sensor network is deployed in production. Simulation results show that this LSTM enabled control strategy successfully tracks the level curve using a mobile multi-robot sensor network.
A Subspace-Inclusive Sampling Method for the Computational Design of Compositionally Graded Alloys
Authors: Allen Marshall , Kirk Tanner , Malak Richard , Arroyave Raymundo 
Published: November 2021

Abstract
Compositionally graded alloys, a special class of functionally graded materials (FGMs), utilize localized variations in composition within a single metal part to achieve higher performance than traditional single-material parts. In previous work [1], the authors presented a computational design methodology that avoids common issues which limit a gradient alloy’s usefulness or feasibility, such as deleterious phases or properties, and also optimizes gradients for performance objectives. However, the previous methodology only samples the interior of a composition space, meaning designed gradients must include all elements in the space at every step in the gradient. Because the addition of even a small amount of an alloying element can introduce a new deleterious phase, this characteristic often neglects potentially simpler solutions to otherwise unsolvable problems and, consequently, discourages the addition of new elements to the state space. The present work improves upon the previous methodology by introducing a sampling method that includes subspaces with fewer elements in the design search. The new sampling method samples within an artificial expanded form of the state space and projects samples outside the true region to the nearest true subspace. This method is evaluated first by observing the distribution of samples in each subspace of a 2-D, 3-D, and 4-D state space. Next, a parametric study in a synthetic 2-D problem compares the performance of the new sampling scheme to the previous methodology. Lastly, the updated methodology is applied to design a gradient from stainless steel to equiatomic NiTi that has practical uses such as embedded shape memory actuation and for which the previous methodology fails to find a feasible path.
Modal Analysis to Interpret Localization Phenomena in Two Nonlinear Tuned Mass Dampers
Authors: Harata Yuji , Ikeda Takashi 
Published: November 2021

Abstract
This study investigates localization phenomena in two identical nonlinear tuned mass dampers (TMDs) installed on an elastic structure, which is subjected to external, harmonic excitation. In the theoretical analysis, the mode shapes of the system are determined, and the modal equations of motion are derived using modal analysis. These equations are demonstrated as forming an autoparametric system in which external excitation directly acts on the first and third vibration modes, whereas the second vibration mode is indirectly excited due to the nonlinear coupling with the other modes. Van der Pol’s method is employed to obtain the frequency response curves for both physical and modal coordinates. The two TMDs vibrate in phase for the first and third modes, but vibrate out of phase for the second mode. Consequently, when all modes appear, the two TMDs may vibrate at different amplitudes, i.e., localization phenomena may occur because the TMD motions are expressed by the summation of motions for all modes. The numerical calculations clarify that the localization phenomena may occur in the two TMDs when all three modes appear simultaneously. Moreover, there are two steady-state solutions of the harmonic oscillations for the second mode with identical amplitudes; however, their phases differ by π. Hence, which TMD vibrates at higher amplitudes depends on which of these two steady-state solutions for the phase.
Measuring Designers’ Empathic Understanding of Users by a Quick Empathic Accuracy (QEA)
Authors: Li Jie , Surma-aho Antti , Hölttä-Otto Katja 
Published: November 2021

Abstract
Empathy is an essential ability for designers to step into users’ shoes and potentially discover their latent needs. However, although empathy helps designers to better understand users, the degree to which designers can actually understand them remains unclear. Consequently, it is essential to measure the accuracy of designers’ empathic understanding. In our previous work, we have adopted an experimental procedure from psychology to quantify designers’ empathic accuracy. However, the measure as such is time-consuming. Therefore, we attempted to shorten the experimental time while retaining the validity of the measure. This paper reports on the process of shortening the measure and compares the original instrument with the shortened one. The data collected from the shortened instrument shows excellent internal consistency and between subject variance and is able to produce similar results to the original longer measure.
Direct Longitudinal Force Feedback for High-Performance Vehicle Dynamics Control Systems
Authors: Riva Giorgio , Mozzarelli Luca , Corno Matteo , Formentin Simone , Savaresi Sergio M. 
Published: November 2021

Abstract
State of the art vehicle dynamics control systems do not exploit tire road forces information, even though the vehicle behaviour is ultimately determined by the tire road interaction. Recent technological improvements allow to accurately measure and estimate these variables, making it possible to introduce such knowledge inside a control system. In this paper, a vehicle dynamics control architecture based on a direct longitudinal tire force feedback is proposed. The scheme is made by a nested architecture composed by an outer Model Predictive Control algorithm, written in spatial coordinates, and an inner longitudinal force feedback controller. The latter is composed by four classical Proportional-Integral controllers in anti-windup configuration, endowed with a suitably designed gain switching logic to cope with possible unfeasible references provided by the outer loop, avoiding instability. The proposed scheme is tested in simulation in a challenging scenario where the tracking of a spiral path on a slippery surface and the timing performance are handled simultaneously by the controller. The performance is compared with that of an inner slip-based controller, sharing the same outer Model Predictive Control loop. The results show comparable performance in presence of unfeasible force references, while higher robustness is achieved with respect to friction curve uncertainties.