bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Novel Data Standards Platform Using the ISO Core Components Technical Specification
Authors: Ivezic Nenad , Kulvatunyou Boonserm , Jelisic Elena , Oh Hakju , Frechette Simon , Srinivasan Vijay 
Published: November 2021

Abstract
It is generally observed in inter-organizational communication that present-day data exchange standards are too costly, large, slow to respond to industry demands, and complex to develop and use. These problems in data exchange are felt keenly by the manufacturing industry and its vast supply chains. In addressing these challenges, a successful attempt was recently made that involves two major developments. The first is the Core Components Technical Specification (CCTS), which is an ISO-approved meta-model for data exchange standards. CCTS introduces common data types, uniform structure for data models, and data usage semantics. The second is Score, which is a novel open-source software tool. Score was developed by National Institute of Standards and Technology researchers as a platform to take advantage of the CCTS for data exchange standards development and usage. This paper describes the potential of the CCTS and the Score platform, and describes the current status of the Score platform and Score-enabled industry interactions.
Conceptual Design of a Compliant Hip Orthosis for Trendelenburg Gait
Authors: Vugts P. , Rommers J. , Sterke Bram T. , Herder J. L. 
Published: November 2021

Abstract
Weakness of the hip abduction muscles can result in a gait disorder named Trendelenburg gait, which can lead to problems in the hip joint, knees, and ankles. In this paper, the conceptual design of a compliant hip orthosis to prevent Trendelenburg gait is presented. A theoretical analysis and measurements on a technical prototype show a high stiffness ratio between adduction and flexion-extension of the leg, and minimal shear forces from the orthosis on the human body while staying close to the human body.
Optimal Release Planning Using Machine Learning and Linear Integer Programming for Ideas in a Crowdsourcing Platform
Authors: Absi-Halabi Nour J. , Yassine Ali A. 
Published: November 2021

Abstract
Obtaining and analyzing customer and product information from various sources has become a top priority for major competitive companies who are striving to keep up with the digital and technological progress. Therefore, the need for creating a crowdsourcing platform to collect ideas from different stakeholders has become a major component of a company’s digital transformation strategy. However, these platforms suffer from problems that are related to the voluminous and vast amount of data. Different large sets of data are being spurred in these platforms as time goes by that render them unbeneficial.
The aim of this paper is to propose a solution on how to discover the most promising ideas to match them to the strategic decisions of a business regarding resource allocation and product development (PD) roadmap. The paper introduces a 2-stage filtering process that includes a prediction model using a Random Forest Classifier that predicts ideas most likely to be implemented and a resource allocation optimization model based on Integer Linear Programming that produces an optimal release plan for the predicted ideas. The model was tested using real data on an idea crowdsourcing platform that remains unnamed in the paper due to confidentiality. Our prediction model has proved to be 92% accurate in predicting promising ideas and our release planning optimization problem results were found out to be 85% accurate in producing an optimal release plan for ideas.
Estimation of Chatter Vibration Under End-Milling Process With a Wavelet Transform
Authors: Minetaka Haruki , Ozaki Nobutoshi , Hirogaki Toshiki , Aoyama Eiichi 
Published: November 2021

Abstract
In this study, a new analysis method using a wavelet transform was considered to evaluate the chatter vibration generated during end milling. End milling often generates vibrations between the tool and work material, called chatter vibration, which causes deterioration of the finished surface and breakage of the tool. Therefore, countermeasures to detect chatter vibration at an early stage have been attempted in the past by using fast Fourier transform (FFT) and short-time Fourier transform (STFT) methods and monitoring the dynamic stability of the cutting process. However, the FFT analysis method assumes steady-state vibration, and the STFT method does not have sufficient frequency resolution. In contrast, the wavelet transform is excellent for analyzing non-stationary vibrations and has a high noise separation capability. To fully validate the analysis method, a groove was added to the machined surface, so that the cutting condition changed with time, and the cutting vibration under the condition where the disturbance was involuntary was analyzed. As a result, it was possible to identify minute fluctuations in chatter vibration, which could not be obtained using the STFT method.
An Alternative Formulation for Modeling Self-Excited Vibrations of Drillstring With PDC Bits
Authors: Tian Kaixiao , Detournay Emmanuel , Zhang He 
Published: November 2021

Abstract
This work describes an alternative formulation of a system of nonlinear state-dependent delay differential equations (SDDDEs) that governs the coupled axial and torsional vibrations of a 2 DOF drillstring model considering a Polycrystalline Diamond Compact (PDC) bit with realistic cutter layout. Such considerations result in up to 100 state-dependent delays due to the regenerative effect of the drilling process, which renders the computational efficiency of conventional solution strategies unacceptable. The regeneration of the rock surface, associated with the bit motion history, can be described using the bit trajectory function, the evolution of which is governed by a partial differential equation (PDE). Thus the original system of SDDDEs can be replaced by a nonlinear coupled system of a PDE and ordinary differential equations (ODEs). Via the application of the Galerkin method, this system of PDE-ODEs is transformed into a system of coupled ODEs, which can be readily solved. The algorithm is further extended to a linear stability analysis for the bit dynamics. The resulting stability boundaries are verified with time-domain simulations. The reported algorithm could, in principle, be applied to a more realistic drillstring model, which may lead to an in-depth understanding of the mitigation of self-excited vibrations through PDC bit designs.
Topology Optimization of Self-Supported Enclosed Voids for Additive Manufacturing
Authors: Wang Cunfu 
Published: November 2021

Abstract
The paper proposes a heat-flux based topology optimization approach to design self-supported enclosed voids for additive manufacturing. The enclosed overhangs that require supports in additive manufacturing are removed from the optimized design by constraining the maximum temperature of a pseudo heat conduction problem. In the pseudo problem, heat flux is applied on the non-self-supported open and enclosed surfaces. Since the density-based topology optimization involves no explicit boundary representation, we impose such surface slope dependent heat flux through a domain integral of a Heaviside projected density gradient. In addition, the solid materials and the void materials in the pseudo problem are assumed to be thermally insulating and conductive, respectively. As such, heat flux on the open surfaces can be successfully conducted to external heat sink through the void (or conductive) materials. However, heat flux on the non-self-supported enclosed surfaces is isolated by the solid (or insulating) materials and thus leads to locally high temperature. Hence, by limiting the maximum temperature of the pseudo problem, self-supported enclosed voids can be achieved, and the slope of the open surfaces would not be affected. Numerical examples are presented to demonstrate the validity and effectiveness of the proposed approach in the design of self-supported enclosed voids.
Development of an End-Effector for Mitigation of Collisions
Authors: Tommasino Domenico , Bottin Matteo , Cipriani Giulio , Doria Alberto , Rosati Giulio 
Published: November 2021

Abstract
In robotics the risk of collisions is present both in industrial applications and in remote handling. If a collision occurs, the impact may damage both the robot and external equipment, which may result in successive imprecise robot tasks or line stops, reducing robot efficiency. As a result, appropriate collision avoidance algorithms should be used or, if it is not possible, the robot must be able to react to impacts reducing the contact forces. For this purpose, this paper focuses on the development of a special end-effector that can withstand impacts and is able to protect the robot from impulsive forces. The novel end-effector is based on a bi-stable mechanism that decouples the dynamics of the end-effector from the dynamics of the robot. The intrinsically non-linear behavior of the end-effector is investigated with the aid of numerical simulations. The effect of design parameters and the operating conditions are analyzed and the interaction between the functioning of the bi-stable mechanism and the control system is studied. In particular, the effect of the mechanism in different scenarios characterized by different robot velocities is shown. Results of numerical simulations assess the validity of the proposed end-effector, which can lead to large reductions in impact forces.
Designing Robust Systems Using Bioinspired Product Architecture
Authors: Bhasin Devesh , Staack David , McAdams Daniel A. 
Published: November 2021

Abstract
This work analyzes the role of bioinspired product architecture in facilitating the development of robust engineering systems. Existing studies on bioinspired product architecture largely focus on inspiring biology-like function-sharing in engineering design. This work shows that the guidelines for bioinspired product architecture, originally developed for bioinspiration of function-sharing, may induce robustness to random failures in engineered systems. To quantify such an improvement, this study utilizes Functional Modeling to derive modular equivalents of biological systems. The application of the bioinspired product architecture guidelines is then modeled as a transition from the modular product architecture of the modular equivalents to the actual product architecture of the biological systems. The robustness of the systems to random failures is analyzed after the application of each guideline by modeling the systems as directed networks. A singular robustness metric is then introduced to quantify the degradation in the expected functionality of systems upon increasing severity of random disruptions. Our results show that a system with bioinspired product architecture exhibits a gradual degradation in expected functionality upon increasing the number of failed modules as compared to an equivalent system with a one-to-one mapping of functions to modules. The findings are validated by designing and analyzing a COVID-19 breathalyzer as a case study.
A Methodology for Designing a Nonlinear Feedback Controller via Parametric Optimization: State-Parameterized Nonlinear Programming Control
Authors: Tsai Ying-Kuan , Malak Richard J. 
Published: November 2021

Abstract
This paper introduces a new technique for designing nonlinear feedback controllers that can effectively and efficiently control nonlinear and unstable dynamical systems. The technique, called State-Parameterized Nonlinear Programming Control (sp-NLPC), constructs an optimal control strategy that is a function of dynamical system states. This is achieved through an offline parametric optimization process using the predictive parameterized Pareto genetic algorithm (P3GA) and representing the optimized state-varying policy using radial basis function (RBF) metamodeling. The sp-NLPC technique avoids many limitations of alternative methods, such as the need to make strong assumptions about model form (e.g., linearity) and the demands of online optimization processes. The proposed method is benchmarked on the problems of controlling the highly nonlinear and inherently unstable systems: single and double inverted pendulums on a cart. Performance and computational efficiency are compared to several competing control design techniques. Results show that sp-NLPC outperforms and is more efficient than competing methods. The parametric solution strategy for sp-NLPC lends itself to use in Control Co-Design (CCD). Such extensions are discussed as part of future work.
Fractional Order Chaotic Model Based Enhanced Equilibrium Optimization Algorithm for Controller Design of 3 DOF Hover Flight System
Authors: Ates Abdullah , Chen YangQuan 
Published: November 2021

Abstract
In this study, the K feedback gain vector parameters that are used for the control of three degree of freedom four rotor quadcopter system (3 DOF Hover) are optimized with the Enhanced Equilibrium Optimization Algorithm (E2O). The E2O algorithm is proposed with using parameters obtained from fractional order chaotic oscillator models instead of random variables.
The Basic EO algorithm is inspired by volume-mass balance. In EO algorithm, each particle is called a motion that searches a parameter vector space. However, random coefficients derived from uniform distribution are used in the parameters updating process or in the generation of the initial population. The E2O algorithm was proposed by using vectors obtained from fractional order chaotic oscillators instead of stochastic coefficients in the basic Equilibrium optimization algorithm.
Genesio Tesi, Rössler, Lotka Volterra fractional-order chaotic oscillator models were used in the E2O algorithm to optimize K feedback gain vector of 3 DOF Hover. The order and initial conditions the fractional chaotic oscillator models were experimentally adjusted for the control of 3 DOF problem. Thus, suitable fractional-order chaotic models for the problem were obtained. The E2O algorithm results are compared with the Stochastic Multi Parameter Optimization (SMDO) and Discreet Stochastic Optimization (DSO) algorithms for the system’s pitch, roll and yaw angles.
Økoengineer – A Web-Based Game Platform for Guided Discovery-Based Sustainability Learning in Engineering Curricula
Authors: Abu-Ghaida Haitham , Leka Serena , Kunrath Kamila , Thostrup Rune , Ramanujan Devarajan 
Published: November 2021

Abstract
A growing body of academic research and educational institutions are recognizing the need to incorporate sustainability learning into fundamental engineering courses. Furthermore, the recent COVID-19 global pandemic has created a sudden need to augment traditional lecture-based teaching with effective web-based learning platforms. In light of these challenges the engineering education community needs to place a greater focus on designing and testing interactive, information technology-based tools for sustainability learning. To this end, our paper presents the design and implementation of Økoengineer: a web-based game platform that supports guided discovery-based learning of sustainability concepts in engineering curricula. Økoengineer is designed to familiarize engineering students with sustainability concepts and provide them with an understanding of how sustainability can be considered in the engineering design process. Therefore, Økoengineer hosts a collection of open-ended design tasks in multiple engineering disciplines. Students can solve these tasks iteratively and receive guidance through a combination of pre-recorded lectures on sustainability concepts and discussions with domain experts. Økoengineer also aims to increase students’ learning outcomes through the use of gamified elements, including high-score leaderboards, formative feedback & peer discussions, and rewarding achievements through virtual collectible badges. The Økoengineer platform is architected for scalability and allows course instructors to easily add new learning materials and design tasks.
Model-Based Design and Optimization of Passive Shoulder Exoskeletons
Authors: Nasr Ali , Ferguson Spencer , McPhee John 
Published: November 2021

Abstract
To physically assist workers in reducing musculoskeletal strain or to develop motor skills for patients with neuromuscular disabilities, recent research has focused on Exoskeletons (Exos). Designing active Exos is challenging due to the complex human geometric structure, the human-Exoskeleton wrench interaction, the kinematic constraints, and the selection of power source characteristics. Because of the portable advantages of passive Exos, designing a passive shoulder mechanism has been studied here. The study concentrates on modeling a 3D multibody upper-limb human-Exoskeleton, developing a procedure of analyzing optimal assistive torque profiles, and optimizing the passive mechanism features for desired tasks. The optimization objective is minimizing the human joint torques. For simulating the complex closed-loop multibody dynamics, differential-algebraic equations (DAE)s of motion have been generated and solved. Three different tasks have been considered, which are common in industrial environments: object manipulation, over-head work, and static pointing. The resulting assistive Exoskeleton’s elevation joint torque profile could decrease the specific task’s human shoulder torque. Since the passive mechanism produces a specific torque for a given elevation angle, the Exoskeleton is not versatile or optimal for different dynamic tasks. We concluded that designing a passive Exoskeleton for a wide range of dynamic applications is impossible. We hypothesize that augmenting an actuator to the mechanism can provide the necessary adjustment torque and versatility for multiple tasks.
A Study on the Acoustic Signal Based Frameworks for the Real-Time Identification of Geometrically Defective Wire Arc Bead
Authors: Surovi Nowrin Akter , Dharmawan Audelia G. , Soh Gim Song 
Published: November 2021

Abstract
In Wire Arc Additive Manufacturing (WAAM), weld beads are deposited bead-by-bead and layer-by-layer, leading to the final part. Thus, the lack of uniformity or geometrically defective bead will subsequently lead to voids in the printed part, which will have a great impact on the overall part quality and mechanical strength. To resolve this, several techniques have been proposed to identity such defects using vision or thermal-based sensing, so as to aid in the implementation of in-situ corrective measures to save time and cost. However, due to the environment that they are operating in, these sensors are not an effective way of picking up irregularities as compared to acoustic sensing. Therefore, in this paper, we seek to study into three acoustic feature-based machine learning frameworks — Principal Component Analysis (PCA) + K-Nearest Neighbors (KNN), Mel Frequency Cepstral Coefficients (MFCC) + Neural Network (NN) and Mel Frequency Cepstral Coefficients (MFCC) + Convolutional Neural Network (CNN) and evaluate their performance for the real-time identification of geometrically defective weld bead. Experiments are carried out on stainless steel (ER316LSi), bronze (ERCuNiAl) and mixed dataset containing both stainless steel and bronze. The results show that all three frameworks outperform the state-of-the-art acoustic signal based ANN approach in terms of accuracy. The best performing framework PCA+KNN outperforms ANN by more than 15%, 30% and 30% for stainless steel, bronze and mixed datasets, respectively.
Prediction of Production Performance in Smart Manufacturing Using Multivariate Adaptive Regression Spline
Authors: Chua Ping Chong , Moon Seung Ki , Ng Yen Ting , Ng Huey Yuen 
Published: November 2021

Abstract
With the dynamic arrival of production orders and ever-changing shop-floor conditions within a production system, production scheduling presents a challenge for manufacturing firms to ensure production demands that are met with high productivity and low operating cost. Before a production schedule is generated to process the incoming production orders, the production planning stage must take place. Given the large number of input parameters involved in production planning, it is important to understand the interactions of input parameters between production planning and scheduling. This is to ensure that production planning and scheduling could be determined effectively and efficiently in achieving the best or optimal production performance with minimizing cost. In this study, by utilizing the capabilities of data pervasiveness in smart manufacturing setting, we propose an approach to develop a surrogate model to predict the production performance using the input parameters from a production plan. Based on three categories of input parameters, namely current production system load, machine-based and product-based parameters, the prediction is performed by developing a surrogate model using multivariate adaptive regression spline (MARS). The effectiveness of the proposed MARS model is demonstrated using an industrial case study of a wafer fabrication production through the random sampling of varying numbers of training data set.
Non-Linear Random Vibrations Using Second-Order Adjoint and Projected Differentiation Methods
Authors: Papadimitriou Dimitrios , Mourelatos Zissimos P. , Hu Zhen 
Published: November 2021

Abstract
This paper proposes a new computationally efficient methodology for random vibrations of nonlinear vibratory systems using a time-dependent second-order adjoint variable (AV2) method, and a second-order projected differentiation (PD2) method. The proposed approach is called AV2-PD2. The vibratory system can be excited by stationary Gaussian or non-Gaussian random processes. A Karhunen-Loeve (KL) expansion expresses each input random process in terms of standard normal random variables. A second-order adjoint approach is used to obtain the required first and second-order output derivatives accurately by solving as many sets of equations of motion (EOMs) as the number of KL random variables. These derivatives are used to compute the marginal CDF of the output process with second-order accuracy. Then, a second-order projected differentiation method calculates the autocorrelation function of each output process with second-order accuracy, at an additional cost of solving as many sets of EOM as the number of outputs of interest, independently of the time horizon (simulation time). The total number of solutions of the EOM scales linearly with the number of input KL random variables and the number of output processes. The efficiency and accuracy of the proposed approach is demonstrated using a non-linear Duffing oscillator problem under a quadratic random excitation.