bluuebunny/crossref_metadata_2025 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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.
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

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

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

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

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

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

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

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

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 > 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 (< 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

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

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

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

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

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

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.
Type Synthesis of Long Symmetric Planar Shape-Morphing Mechanism Arrays
Authors: Lusk Craig
Published: November 2021

This paper describes a novel type synthesis methodology for creating planar shape-morphing mechanism chains of any length with specified mobility (degree-of-freedom). It is expected that this will be of use in designing complex shape-morphing objects. The methodology is based on using graph theory in conjunction with symmetry theory for borders (Frieze groups). It is shown that the methodology can produce chains of a variety of lengths and mobility, and these chains have symmetric topologies and that symmetric kinematics may be possible for some of the chains, which simplifies their analysis. Techniques for decreasing and increasing the mobility of the chains are discussed. Finally, a gallery of shape morphing chains is given to provide concrete illustrations of the diversity of designs generated using this methodology.