bluuebunny/crossref_metadata_2025_split huggingface dataset:
Wave Propagation in a Network of Buckled Beams Directly and Parametrically Excited at One End
Authors: Talebi Bidhendi M. Reza 
Published: November 2020

Abstract
Wave propagation in a network of buckled beams, which represents a finite dissipative periodic structure with quadratic and cubic nonlinearities, is studied. The aforementioned structure is harmonically driven externally and parametrically at one end with forcing frequencies lying within its stop band, one above and one below. Numerical calculations show the occurrence of supratransmission, a sudden increase in the energy transmitted across the finite structure, after a certain forcing amplitude of the external excitation. In essence, this nonlinear wave propagation mechanism for the discrete nonlinear periodic structure occurs due to loss of stability of the periodic solutions that are initially localized to the driven end of the structure (nonlinear instability). It is found that small parametric excitation can considerably decrease the required threshold for the onset of energy transmission within the stop band.
On Periodic Motions in a Periodically Driven van der Pol-Duffing Oscillator
Authors: Xu Yeyin , Luo Albert C. J. 
Published: November 2020

Abstract
In this paper, the symmetric and asymmetric period-1 motions on the bifurcation tree are obtained for a periodically driven van der Pol-Duffing hardening oscillator through a semi-analytical method. Such a semi-analytical method develops an implicit mapping with prescribed accuracy. Based on the implicit mapping, the mapping structures are used to determine periodic motions in the van der Pol-Duffing oscillator. The symmetry breaks of period-1 motion are determined through saddle-node bifurcations, and the corresponding asymmetric period-1 motions are generated. The bifurcation and stability of period-1 motions are determined through eigenvalue analysis. To verify the semi-analytical solutions, numerical simulations are also carried out.
Predicting Build Orientation of Additively Manufactured Parts With Mechanical Machining Features Using Deep Learning
Authors: Eranpurwala Aliakbar , Ghiasian Seyedeh Elaheh , Lewis Kemper 
Published: November 2020

Abstract
Additive Manufacturing (AM) is a revolutionary development that is being viewed as a core technology for fabricating current and future engineered products. While AM has many advantages over subtractive manufacturing processes, one of the primary limitations of AM is to swiftly evaluate precise part build orientations. Current algorithms are either computationally expensive or provide multiple alternative orientations, requiring additional decision tradeoffs. To hasten the process of finding accurate part build orientation, a data-driven predictive model is introduced by mapping standard machining features to build orientation angles. A combinatory learning algorithm of classification and regression is utilized for the prediction of build orientation. The framework uses 54,000 voxelized standard tessellated language (STL) files as input to train the classification algorithm for eighteen standard machining features using a nine-layer 3D Convolutional Neural Network (CNN). Additionally, a multi-machining feature dataset of 1000 voxelized STL files are evaluated in parallel by performing quaternion rotations to obtain build orientation angles based on minimization of support structure volume. A regression model is then developed to establish a relationship between the machining features and orientation angles to predict optimal build orientation for new parts.
An Initial Guess Free Method for Least Squares Parameter Estimation in Nonlinear Models
Authors: Zhang Guanglu , Allaire Douglas , Cagan Jonathan 
Published: November 2020

Abstract
Fitting models to data is critical in many science and engineering fields. A major task in fitting models to data is to estimate the value of each parameter in a given model. Iterative methods, such as the Gauss-Newton method and the Levenberg-Marquardt method, are often employed for parameter estimation in nonlinear models. However, practitioners must guess the initial value for each parameter in order to initialize these iterative methods. A poor initial guess can contribute to non-convergence of these methods or lead these methods to converge to a wrong solution. In this paper, an initial guess free method is introduced to find the optimal parameter estimators in a nonlinear model that minimizes the squared error of the fit. The method includes three algorithms that require different level of computational power to find the optimal parameter estimators. The method constructs a solution interval for each parameter in the model. These solution intervals significantly reduce the search space for optimal parameter estimators. The method also provides an empirical probability distribution for each parameter, which is valuable for parameter uncertainty assessment. The initial guess free method is validated through a case study in which Fick’s second law is fit to an experimental data set. This case study shows that the initial guess free method can find the optimal parameter estimators efficiently. A four-step procedure for implementing the initial guess free method in practice is also outlined.
Investigation of 3:1 Internal Resonance of Electrostatically Actuated Microbeams With Flexible Supports
Authors: Kumar Praveen , Inamdar Mandar M. , Pawaskar Dnyanesh N. 
Published: November 2020

Abstract
Interaction between modes due to internal resonance has many applications in MEMS devices. In this paper, we investigate the modal interaction through 3 : 1 internal resonance of an electrostatically actuated microbeam with flexible supports in the form of rotational and transversal springs. The static displacement and the first three modal frequencies are obtained at the applied DC voltage by a reduced order model for a specified ratio of electrode gap and thickness. We then obtain the value of applied voltage for which 3 : 1 internal resonance exists for four different combinations of unequal end support stiffnesses. We calculate the coefficients of the coupled dynamical equations of first two modes for all the four cases and solve them by using numerical time integration and the method of multiple scales. We observe the interaction between the first and the second mode when each of the modes is independently excited by an external source. When the second mode is externally excited, interestingly, we also find that the undriven mode response amplitude is twice that of the driven mode.
Identification of Rider’s Arms Dynamic Response and Effects on Bicycle Stability
Authors: Doria Alberto , Marconi Edoardo , Massaro Matteo 
Published: November 2020

Abstract
It is known that the way a bicycle or motorcycle rider grasps the handlebar may have implications on the bike stability. Indeed, the rider closes a loop (with his/her hands) between the front assembly and the chassis of the bike, which in turn affects the open-loop stability of the vehicle. The aim of this paper is to experimentally quantify the interaction between the rider and the handlebar through the frequency response function (FRF), and to numerically simulate the effects of such interaction on the bicycle stability. In order to implement the effect of rider’s arms/hands, a mechanical model is employed to fit the experimental FRFs. The parameters of a number of different riders have been derived and used to assess the effect on bike dynamics.
Kinematic Design of Functional Nanoscale Mechanisms From Molecular Primitives
Authors: Tavousi Pouya , Chorsi Meysam T. , Mundrane Caitlyn , Gorbatyuk Vitaliy , Kazerounian Kazem , Ilies Horea 
Published: November 2020

Abstract
Natural nanomechanisms such as capillaries, neurotransmitters, and ion channels play a vital role in the living systems. But the design principles developed by nature through evolution are not well understood and, hence, not applicable to engineered nanomachines. Thus, the design of nanoscale mechanisms with prescribed functions remains a challenge. Here, we present a systematic approach based on established kinematics techniques to designing, analyzing, and controlling manufacturable nanomachines with prescribed mobility and function built from a finite but extendable number of available “molecular primitives.” Our framework allows the systematic exploration of the design space of irreducibly simple nanomachines, built with prescribed motion specification by combining available nanocomponents into systems having constrained, and consequently controllable motions. We show that the proposed framework has allowed us to discover and verify a molecule in the form of a seven link, seven revolute (7R) close loop spatial linkage with mobility (degree of freedom) of one. Furthermore, our experiments exhibit the type and range of motion predicted by our simulations. Enhancing such a structure into functional nanomechanisms by exploiting and controlling their motions individually or as part of an ensemble could galvanize development of the multitude of engineering, scientific, medical, and consumer applications that can benefit from engineered nanomachines.
Topology Optimization of the Pelvic Bone Prosthesis Under Single Leg Stance
Authors: Eswara Sai Kumar Kandula , Rakshit Sourav 
Published: November 2020

Abstract
Pelvic reconstruction is required to restore the functional activity after an internal hemipelvectomy. The available pelvic prosthesis has several complications and long terms issues (such as stability, dislocations etc.) due to non inclusion of the basic mechanics. In this study, a structural optimization based pelvic design is presented for an efficient pelvic prosthesis by including the effect of muscles under single leg stance. The single leg stance loading condition is divided into four categories, (a) all muscles, (b) lower muscles, (c) upper muscles and (d) average value of the muscles for entire stance phase. Thereafter, topology optimization is used to generate four prosthesis for pelvic reconstruction. Titanium alloy (Ti6Al4V) is used to design the pelvic prosthesis. The optimal designs are compared with the natural pelvic bone by measuring the shape similarity index. Results show that the stress and displacement produced by the optimal designs are less than the natural pelvic bone. The induced peak stresses in the optimal designs are low, within linear elastic zone and below the yield strength of the alloy. The moderate to good shape similarity values indicate less complications such as dislocations, stability and constraining the range of hip joint.
Multi-Objective Implementation of Additive Manufacturing in Make-to-Stock Production
Authors: Chua Ping Chong , Moon Seung Ki , Ng Yen Ting 
Published: November 2020

Abstract
As additive manufacturing (AM) develops and matures over the years, it has reached a stage where implementation into a conventional production system becomes possible. With additive manufacturing currently suitable for product personalization/high customization yet small volume production, there are various ways of implementation in a conventional production line. This aim of this paper is to explore the implementation of additive manufacturing in a complementary manner to process internal job orders of large quantities in make-to-stock (MTS) production. Splitting of production orders is allowed and production can be carried out by both injection moulding and additive manufacturing processes simultaneously, with the latter being able to produce various MTS parts in a single build. NSGA-III together with scheduling and rule-based heuristic for allocation of parts on build plate of additive manufacturing process is used to solve the multi-objective implementation problem, with performance measures being cost, scheduling and sustainability. The algorithm will be incorporated with scheduling and rule-based heuristic for allocation of parts on build plate of additive manufacturing process. An experiment using an industry case study is conducted to compare the performance measures with and without implementing additive manufacturing.
Kinematics of the Morph Origami Pattern and its Hybrid States
Authors: Pratapa Phanisri P. , Liu Ke , Paulino Glaucio H. 
Published: November 2020

Abstract
A new degree-four vertex origami, called the Morph pattern, has been recently proposed by the authors (Pratapa, Liu, Paulino, Phy. Rev. Lett. 2019), which exhibits interesting properties such as extreme tunability of Poisson’s ratio from negative infinity to positive infinity, and an ability to transform into hybrid states through rigid origami kinematics. We look at the geometry of the Morph unit cell that can exist in two characteristic modes differing in the mountain/valley assignment of the degree-four vertex and then assemble the unit cells to form complex tessellations that are inter-transformable and exhibit contrasting properties. We present alternative and detailed descriptions to (i) understand how the Morph pattern can smoothly transform across all its configuration states, (ii) characterize the configuration space of the Morph pattern with distinguishing paths for different sets of hybrid states, and (iii) derive the condition for Poisson’s ratio switching and explain the mode-locking phenomenon in the Morph pattern when subjected to in-plane deformation as a result of the inter-play between local and global kinematics.
An Approach to Bayesian Optimization for Design Feasibility Check on Discontinuous Black-Box Functions
Authors: Biswas Arpan , Hoyle Christopher 
Published: November 2020

Abstract
The paper presents a novel approach to apply Bayesian Optimization (BO) in predicting an unknown constraint boundary, also representing the discontinuity of an unknown function, for a feasibility check on the design space, thereby representing a classification tool to discern between a feasible and infeasible region. Bayesian optimization is an emerging field of study in the Sequential Design Methods where we learn and update our knowledge from prior evaluated designs, and proceed to the selection of new designs for future evaluation. It has been considered as a low-cost global optimization tool for design problems having expensive black-box objective functions. However, BO is mostly suited to problems with the assumption of a continuous objective function, and does not guarantee true convergence if the objective function has a discontinuity. This is because of the insufficient knowledge of the BO about the nature of the discontinuity of the unknown true function. Therefore, in this paper, we have proposed to predict the discontinuity of the objective function using a BO algorithm which can be considered as the pre-stage before optimizing the same unknown objective function. The proposed approach has been implemented in a thin tube design with the risk of creep-fatigue failure under constant loading of temperature and pressure. The stated risk depends on the location of the designs in terms of safe and unsafe regions, where the discontinuities lie at the transitions between those regions; therefore, the discontinuity has also been treated as an unknown constraint. The paper focuses on developing BO framework with maximizing the reformulated objective function on the same design space to predict the transition regions as a design methodology or classification tool between safe and unsafe designs, where we start with very limited data or no prior knowledge and then iteratively focus on sampling most designs near the transition region through better prior knowledge (training data) and thereby increase the accuracy of prediction to the true boundary while minimizing the number of expensive function evaluations. The converged model has been compared with the true solution for different design parameters and the results provided a classification error rate and function evaluations at an average of &lt; 1% and ∼150, respectively. The results in this paper show some future research directions in extending the application of BO and considered as the proof of concept on large scale problem of complex diffusion bonded hybrid Compact Heat Exchangers.
Superharmonic Resonance of Third Order of Electrostatically Actuated MEMS Circular Plates: Effect of AC Frequency on Voltage Response
Authors: Beatriz Julio , Caruntu Dumitru I. 
Published: November 2020

Abstract
This paper uses the Reduced Order Model (ROM) as well as the Method of Multiple Scales (MMS) in order to investigate behavior of electrostatically actuated micro-electro-mechanical systems (MEMS) circular plates under superharmonic resonance of third order. ROM is solved using two methods, the first is a continuation and bifurcation approach by using software package called AUTO 07p in order to obtain the voltage response, and the second approach is a numerical integration using the Matlab built in function ode15s for obtaining time responses of the system. Overall MMS and ROM provide similar results, especially in the lower amplitudes. These methods seem to differ at higher amplitudes. The ROM shows a second unstable branch that MMS does not have. The time responses agree with the ROM voltage response. Furthermore, the influences of different parameters such as that of the detuning parameter, and damping are investigated.
Roughing It: Evaluating a Novel Experiential Design Course on Resiliency, Self-Leadership, and Engineering Design Self-Efficacy
Authors: Krishnakumar Sandeep , Sallai Gabriella , Berdanier Catherine , Handley Meg , Lang Dena , Menold Jessica 
Published: November 2020

Abstract
Despite continuous calls in literature for an increase in women in engineering, U.S. numbers hover around 20% women graduating with undergraduate degrees in engineering. Although diversity, inclusion, and engineering education researchers have made great strides in understanding the development of engineering identity and capturing the experiences of struggling women and underrepresented students, many women students still experience “chilly” climates in daily interactions in engineering education environments. Women need mentorship, peer support, and mastery experiences that build Self-leadership, resilience, and engineering self-efficacy beliefs to persevere and flourish in such environments. The current work explores the effectiveness of a novel experiential design intervention on these factors for women engineering students. Specifically, we assess the value of a unique experiential design course where women engineering students hiked a portion of the Appalachian Trail guided by women engineering faculty members, and then participated in a follow-on design thinking project. Results suggest such interventions may be effective methods to improve student outcomes.
Tooth Meshing Estimation Based on Monitoring Rotational Vibration and Infrared Thermography Image of Hypoid Gear
Authors: Yamashita Ren , Mukaiyama Koki , Sakuda Hiroki , Matsui Shota , Hirogaki Toshiki , Aoyama Eiichi 
Published: November 2020

Abstract
Nowadays, the improvement in Noise Vibration Harshness (NVH) is one of the most important issues for meeting the needs of the user in the automobile industry. It is well-known that the tooth surface accuracy significantly influences the vibration in gear meshing. The hypoid gear used in the automobile differential has a complex shape, and estimation of the contact conditions is difficult. Therefore, we attempt to develop a novel method to analyze the tooth contact conditions by using highly sensitive infrared thermography with a high response; this method acts as a noncontact analysis that is based on monitoring the temperature distribution during meshing between the pinion and the gear surface. In this report, we designed three types of hypoid gears with different offsets to observe changes in the meshing phenomenon, which occurred due to the differences in the offset. Moreover, by employing accelerometers during a driving test, the dynamic behavior was also examined. Simultaneously, a tooth contact analysis was carried out using thermography. Consequently, the relationship between the rise in temperature at the tooth contact area and the tendency of the rotational vibration was elucidated.
Numerical Modelling of the Biaxial Fatigue Test of Aluminium Wheels
Authors: Ballo Federico , Mastinu Giampiero , Previati Giorgio , Gobbi Massimiliano 
Published: November 2020

Abstract
The paper is devoted to the numerical simulation of fatigue life of lightweight aluminium wheels subject to biaxial fatigue test. A numerical model based on finite elements is developed for the scope. The model receives as input the test load sequences and outputs the fatigue life of wheel. Two different methods for modelling the load transfer mechanism of the tyre have been analysed, i.e. how the tyre-drum contact forces are transferred to the wheel rim. The first method consists of a simple cosine loading function acting on a fixed arch of the wheel rim. The second method relies on a physical model of the tyre that is fixed at the tyre-rim interface surface; the computed reaction forces are fed as input to the wheel model. The fatigue life of the wheel is estimated by using the Palmgrem-Miner approach. Both the Sines fatigue criterion and the Papadopoulos critical plane with gradient effect criterion are used and the results are compared. Experimental tests have been performed on an actual wheel mounted on the biaxial test bench for a preliminary validation of the method.
Sequential Sampling Based Reliability Analysis for High Dimensional Rare Events With Confidence Intervals
Authors: Xu Yanwen , Wang Pingfeng 
Published: November 2020

Abstract
Analysis of rare failure events accurately is often challenging with an affordable computational cost in many engineering applications, and this is especially true for problems with high dimensional system inputs. The extremely low probabilities of occurrences for those rare events often lead to large probability estimation errors and low computational efficiency. Thus, it is vital to develop advanced probability analysis methods that are capable of providing robust estimations of rare event probabilities with narrow confidence bounds. Generally, confidence intervals of an estimator can be established based on the central limit theorem, but one of the critical obstacles is the low computational efficiency, since the widely used Monte Carlo method often requires a large number of simulation samples to derive a reasonably narrow confidence interval. This paper develops a new probability analysis approach that can be used to derive the estimates of rare event probabilities efficiently with narrow estimation bounds simultaneously for high dimensional problems. The asymptotic behaviors of the developed estimator has also been proved theoretically without imposing strong assumptions. Further, an asymptotic confidence interval is established for the developed estimator. The presented study offers important insights into the robust estimations of the probability of occurrences for rare events. The accuracy and computational efficiency of the developed technique is assessed with numerical and engineering case studies. Case study results have demonstrated that narrow bounds can be built efficiently using the developed approach, and the true values have always been located within the estimation bounds, indicating that good estimation accuracy along with a significantly improved efficiency.