bluuebunny/crossref_metadata_2025 huggingface dataset:
Nonlinear Vibration Analysis of a Flexible Rotor Supported by a Journal Bearing Considering Journal Angular Motion
Authors: Koondilogpiboon Nuntaphong , Inoue Tsuyoshi
Published: November 2019

The effect of bearing length to diameter (L/D) ratio and large disk position on nonlinear vibration (limit cycle and bifurcation type) of a flexible rotor-bearing system is investigated. The rotor consists of a shaft modeled by 1-D finite elements (FE), two small disks and a large disk. It is supported by a self-aligning ball bearing and an axial-groove journal bearing with L/D ratio of 0.4 and 0.6. Two large disk positions: 340 and 575 mm measured from the ball bearing are investigated. The journal angular motion, which is essential for the highly flexible rotor but typically not considered in the previous nonlinear vibration literature; is considered in nonlinear bearing force calculation. The degrees of freedom (DOF) of the rotor-bearing system are reduced to those of the node that the nonlinear journal bearing force and moment act on by real mode component mode synthesis (CMS) that retains only the 1st forward and backward modes. Shooting method and Floquet multiplier analysis are applied to the reduced rotor-bearing system to obtain limit cycles and their stability of each bearing L/D ratio and large disk position case. Numerical results indicate that supercritical Hopf bifurcation only occurs in the case of L/D = 0.4 and large disk position 575 mm, otherwise subcritical occurs. However, if the typical bearing model that does not consider journal angular motion is used, the bifurcation type for the case of L/D = 0.6 with large disk position 575 mm will change to supercritical. Lastly, the experiments with the same L/D ratio and large disk position investigated in the calculation are performed as a validation. The experimental result of each case shows the same bifurcation type as the calculation result using the bearing model that considers the journal angular motion.
Motivations for Users Participating in Co-Innovation Communities: A Case Study of Local Motors
Authors: Zheng Qing , Guo Wei
Published: November 2019

Online user collaborative innovation (co-innovation) community is increasingly used to involve customers in product and service innovation. Why users participate in the innovation is a critical problem for firms managing the community. However, little is known about user motivations. The purpose of this paper is to identify the motivations for users participating in online co-innovation communities. Based on the expectancy theory, this study employs netnography to investigate user motivations with user actual participation behaviors. In accordance with the procedures of netnography, user participation data is collected though web mining. Then, four behavior properties are defined according to behavior characteristics. Finally, six different motivations are identified. The results show that a small number of users are inspired by status and reputation and individuality demands. Monetary rewards is one of the main motivations that make users contributing to the complex technical product process. Social relations and interest and curiosity are also important factors that drive users participating. The most of users partake for interest and curiosity. According to the different motivations, this study provides several managerial insights on how to successfully attract and motivate more users to contribute to product development in co-innovation communities. This paper contributes a systematic and efficient method for identifying user motivations. In addition, the study provides a better theoretical understanding of user motivations and offers practice guidance on inspiring and managing users for co-innovation community practitioners.
Power Loss Measurements of Rolling Element Bearings Subject to Combined Radial and Axial Loads
Authors: Vedera Kevin , Hong Isaac , Talbot David , Kahraman Ahmet , Zhou Sen
Published: November 2019

Power losses of load carrying gear and bearing components of automotive transmissions have become a major research area in recent years. Measurement of power loss of a gearbox is a routine task where losses from rolling element bearings, gear meshes and seals collectively define the total loss. However, separating bearing and gear mesh losses is not possible, as a gear mesh cannot be operated without support bearings. This study aims at developing a methodology for measuring power losses of rolling element bearings of different types operated under realistic load, speed and temperature conditions. A test machine concept is implemented to apply combined radial and axial loads to a pair of test bearings in a stable and repeatable manner, with rotational speed and lubrication parameters controlled tightly during tests. The proposed test methodology is employed to evaluate power loss for three different types of bearings. Load-dependent and load-independent components of power loss are separated, and influence of speed and load values on bearing mechanical loss are quantified. A repeatability study of the machine and methodology is also presented to demonstrate the accuracy of the proposed setup.
Shape Optimization Framework for the Path of the Primary Compliance Vector in Compliant Mechanisms
Authors: Kooistra Hylke , Kim Charles J. , van de Sande Werner W. P. J. , Herder Just L.
Published: November 2019

The primary compliance vector captures the predominant kinematic degree of freedom of a mechanism. Its displacement describes large deformation mechanism behavior and can be an optimization objective in detailed compliant mechanism design. This paper presents a general framework for the optimization of the PCV path of compliant mechanisms using unified stiffness characterization, Fourier descriptors, and surrogate-based optimization. We found a meaningful objective formulation that evaluates PCV path shape independently. Furthermore, we reason that design variables should effect mechanism shape. Lastly, we apply the framework to a design example.
Complex Solutions for Complex Problems?: Exploring the Effects of Task Complexity on Student Use of Design for Additive Manufacturing and Creativity
Authors: Prabhu Rohan , Miller Scarlett R. , Simpson Timothy W. , Meisel Nicholas A.
Published: November 2019

The integration of additive manufacturing (AM) processes in many industries has led to the need for AM education and training, particularly on design for AM (DfAM). To meet this growing need, several academic institutions have implemented educational interventions, especially project- and problem-based, for AM education; however, limited research has explored how the choice of the problem statement influences the design outcomes of a task-based AM/DfAM intervention. This research explores this gap in the literature through an experimental study with 222 undergraduate engineering students. Specifically, the study compared the effects of restrictive and dual (restrictive and opportunistic) DfAM education, when introduced through either a simple or complex design task. The effects of the intervention were measured through (1) changes in student DfAM self-efficacy, (2) student self-reported emphasis on DfAM, and (3) the creativity of student AM designs. The results show that the complexity of the design task has a significant effect on the participants’ self-efficacy with, and self-reported emphasis on, certain DfAM concepts. The results also show that the complex design task results in participants generating ideas with greater median uniqueness compared to the simple design task. These findings highlight the importance of the chosen problem statement on the outcomes of a DfAM educational intervention, and future work is also discussed.
The Prototyping Behaviors of Startups: Exploring the Relationship Between Prototyping Behavior and the Financial Success of Startups
Authors: Nelson Jacob , Mahan Tobias , McComb Christopher , Menold Jessica
Published: November 2019

Prototyping is a crucial part of new product development and engineers and designers rely on prototyping to bring novel technologies to market. In recent years, tech-based startups like Tesla or Udacity have revolutionized their respective industries. However, many tech-based startups are unable to create a viable product with their available resources, and fail before ever making it to market. In this work, we analyze survey responses from 34 startup representatives to investigate the relationship between prototyping practice, startup success, and perceived difficulty of startup tasks. K-means cluster analysis shows three distinct groups, differentiated by (1) their amount of available funding, (2) their use of prototyping best practices, and (3) their reported difficulty in startup tasks. High-performing startups reported having the highest funding, experiencing less difficulty in startup tasks, and using prototyping best practices more frequently than their peers.
But Will it Print?: Assessing Student Use of Design for Additive Manufacturing and Exploring its Effect on Design Performance and Manufacturability
Authors: Prabhu Rohan , Miller Scarlett R. , Simpson Timothy W. , Meisel Nicholas A.
Published: November 2019

Additive manufacturing (AM) enables engineers to improve the functionality and performance of their designs by adding complexity at little to no additional cost. However, AM processes also exhibit certain unique limitations, such as the presence of support material, which must be accounted for to ensure that designs can be manufactured feasibly and cost-effectively. Given these unique process characteristics, it is important for an AM-trained workforce to be able to incorporate both opportunistic and restrictive design for AM (DfAM) considerations into the design process. While AM/DfAM educational interventions have been discussed in the literature, limited research has investigated the effect of these interventions on students’ use of DfAM. Furthermore, limited research has explored how DfAM use affects the performance of students’ AM designs. This research explores this gap through an experimental study with 123 undergraduate students. Specifically, participants were exposed to either restrictive DfAM or dual DfAM (both opportunistic and restrictive) and then asked to participate in an AM design challenge. The students’ final designs were evaluated for (1) performance with respect the design objectives and constraints, and (2) the use of the various aspects of DfAM. The results showed that the use of certain DfAM considerations, such as minimum feature size and support material mass, successfully predicted the performance of the AM designs. Further, while the variations in DfAM education did not influence the performance of the AM designs, it did have an effect on the students’ use of certain DfAM concepts in their final designs. These results highlight the influence of DfAM education in bringing about an increase in students’ use of DfAM. Moreover, the results demonstrate the potential influence of DfAM in reducing build time and build material of the students’ AM designs, thus improving design performance and manufacturability.
Model Predictive Control of General Fractional Order Systems Using a General Purpose Optimal Control Problem Solver: RIOTS
Authors: Dehghan Sina , Zhao Tiebiao , Chen YangQuan , Homayouni Taymaz
Published: November 2019

RIOTS is a Matlab toolbox capable of solving a very general form of integer order optimal control problems. In this paper, we present an approach for implementing Model Predictive Control (MPC) to control a general form of fractional order systems using RIOTS toolbox. This approach is based on time-response-invariant approximation of fractional order system with an integer order model to be used as the internal model in MPC. The implementation of this approach is demonstrated to control a coupled MIMO commensurate fractional order model. Moreover, the performance and its application process is compared to examples reported in the literature.
Robust Optimization With Mixed Interval and Probabilistic Parameter Uncertainties, Model Uncertainty, and Metamodeling Uncertainty
Authors: Zhang Yanjun , Xia Tingting , Li Mian
Published: November 2019

Various types of uncertainties, such as parameter uncertainty, model uncertainty, metamodeling uncertainty may lead to low robustness. Parameter uncertainty can be either epistemic or aleatory in physical systems, which have been widely represented by intervals and probability distributions respectively. Model uncertainty is formally defined as the difference between the true value of the real-world process and the code output of the simulation model at the same value of inputs. Additionally, metamodeling uncertainty is introduced due to the usage of metamodels. To reduce the effects of uncertainties, robust optimization (RO) algorithms have been developed to obtain solutions being not only optimal but also less sensitive to uncertainties. Based on how parameter uncertainty is modeled, there are two categories of RO approaches: interval-based and probability-based. In real-world engineering problems, both interval and probabilistic parameter uncertainties are likely to exist simultaneously in a single problem. However, few works have considered mixed interval and probabilistic parameter uncertainties together with other types of uncertainties. In this work, a general RO framework is proposed to deal with mixed interval and probabilistic parameter uncertainties, model uncertainty, and metamodeling uncertainty simultaneously in design optimization problems using the intervals-of-statistics approaches. The consideration of multiple types of uncertainties will improve the robustness of optimal designs and reduce the risk of inappropriate decision-making, low robustness and low reliability in engineering design. Two test examples are utilized to demonstrate the applicability and effectiveness of the proposed RO approach.
Effect of Assistive Methods on the Sense of Fulfillment With Agency: Modeling With Flow and Attribution Theories
Authors: Nanno Dan , Yanagisawa Hideyoshi
Published: November 2019

Several assistive technologies for users’ operations have been recently developed. A user’s sense of agency (SoA) decreases with increasing system assistance, possibly resulting in a decrease in the user’s sense of fulfillment. This study aims to provide a design guideline for an assistive method to maintain and improve the sense of fulfillment with SoA. We propose a mathematical model describing the mechanisms by which the assistive method affects SoA and SoA induces a sense of fulfillment. The experience in the flow state is assumed to be a sense of fulfillment. The assistance effect on the skill-challenge plane in flow theory is defined as an increase in skill and decrease in challenge. The factor that separates the two effects from attribution theory is the locus of causality, which is matched to the judgement of agency (JoA) from the two-step account of agency. We hypothesized that the assistance increases the perception of skill and sense of fulfillment is greater when the locus of causality is internal, rather than external. To verify this hypothesis, a game task experiment was conducted with assistance that varied with the ease of recognition. We hypothesized that a player’s JoA is internal for hard-to-recognize assistance, resulting in a high sense of fulfillment. Experimental results supported this hypothesis.
Improving Hole-Searching Accuracy for Peg-in-Hole Assembly With Manipulator Harmonic Drive Uncertainty
Authors: Chen Po-Yu , Chan Kuei-Yuan
Published: November 2019

Uncertainties of a robot manipulator include the inaccurate transformation between each coordinate, (e.g., robot base, flange, camera, tool, and workpiece) and the inherent variations within each links and mechanical components. In this work we consider the impact of harmonic drive uncertainty in peg-in-hole assembly. We investigate the accumulations of transmission errors within robot dynamic and nonlinear process in rotation. Models of manipulators with uncertainty parameters in harmonic drive systems are developed. The parameters in our harmonic drive model with uncertainty are identified through testing. The transformation metrics between each coordinate are also determined by detecting fiducial pattern and marker with well-calibrated camera. As a result we have a virtual robot model with parameters and uncertainties much closer to the real system. The simulation results show that the accuracy of hole-searching can reach 0.50 mm position error and 0.16 degree orientation error for hole locations that are not known as a priori.
Development of a Compact and Cost Effective MSL System Based on a Compliant Nanomanipulator
Authors: Liu Zheng , Zhang Zhen , Guan Yingchun
Published: November 2019

This paper proposes a compact and cost-effective MSL (Micro Stereo Lithography) prototyping system. A self-developed compliant XY nanomanipulating stage is responsible for the fabrication quality in one layer. And a blu-ray LED laser module is utilized as a cost-effective light source. The experimental results show that the MSL prototyping system is capable of achieving ∼ 2 μm cured width for circular patterns.
Reconciling Co-Evolving Engineering and Customer Requirements With a Looped Bayesian Model
Authors: Slon Christopher , Pandey Vijitashwa
Published: November 2019

Engineering and manufacturing abilities of firms evolve with every passing year and so do the preferences of the customers buying their products. Reconciling this coevolution is essential to staying competitive in the marketplace. In this paper, we provide a looped Bayesian framework to accomplish this so that designs can evolve as engineering capabilities increase and customer preferences change. We begin with an approach to incorporating the voice of the customer through the multi-attribute utility function, the core of decision-based design. We consider the utility to be a stochastic function governed by shape parameters that are random variables. Typically, a representative preference or utility function is used or the function is aggregated over many decision makers and regarded as a deterministic function of specified shape parameters. In our approach, the shape parameters represent the stochastic nature of preference behavior either due to variation in a decision maker’s state of mind from one decision to another, or due to a multiplicity of decision makers. The novelty of this approach is in taking a Bayesian perspective on the stochastic utility function. We consider the utility distribution in the design phase as a prior distribution and we update the prior to a posterior with feedback on the actual product in production. The method is valuable in providing a means to improve the level of informativeness of the design level utility function for adjustments to the design or for the next design revision in the cycle of continuous improvement. We present our approach on a real-life assembly problem in an automotive manufacturing floor.
Design and Analysis of a Tensegrity Mechanism for a Bio-Inspired Robot
Authors: Venkateswaran Swaminath , Furet Matthieu , Chablat Damien , Wenger Philippe
Published: November 2019

Piping inspection robots are of greater interests in industries such as nuclear, chemical and sewage. The design of such robots is highly challenging owing to factors such as locomotion inside pipes with varying diameters, cable management, and complex pipe bends (or) junctions. A rigid bio-inspired caterpillar type piping inspection robot was developed at LS2N, France. By introducing tensegrity mechanisms and four-bar wheel mechanisms, the design of this robot is modified into a reconfigurable system. The tensegrity mechanism employs a passive universal joint with three tension springs and three cables for actuation. The positioning of the end effector with respect to the base of the mechanism plays an important role in determining the maximum tilt angle (or) bending limit of the system. By workspace analysis of three case studies, the best solution is chosen which generates the maximum tilt. A static force analysis is then performed on the mechanism to determine its stability under the influences of preload. By the modification of design parameters, stable configurations are determined followed by which cable actuation of mechanism is analyzed for estimating applied forces.
Exploiting Multi-Modal Sensing for Increased Detection Fidelity of Pipeline Leakage
Authors: Lanjile Apeksha , Younis Mohamed , Kim Seung-Jun , Lee Soobum
Published: November 2019

Long distance transportation of various fluid commodities like water, oil, natural gas liquids is achieved through a distribution network of pipelines. Many of these pipelines operates unattended in harsh environments. Therefore, pipes are often susceptible to corrosion, leakage, cracking and third party damage leading to economic and resource infrastructure losses. Thus, early detection and prevention of any further losses is very important. Although many pipeline monitoring techniques exist, the majority of them are based on single sensing modality like acoustic, accelerometer, ultrasound, pressure. This makes the existing techniques unreliable, sensitive to noise and costly. This paper describes a methodology to combine accelerometer and acoustic sensors to increase the detection fidelity of pipeline leakages. The sensors are mounted on the pipe wall at multiple locations. Vibrational and acoustic characteristics obtained from these sensors are fused together through wavelet analysis and classified using kernel SVM and Logistic Regression in order to detect small bursts and leaks in the pipe. The simulation results have confirmed the effectiveness of proposed methodology yielding 90% leak detection accuracy.
Programmable Kirigami: Cutting and Folding in Science, Technology and Architecture
Authors: Liu Jingyang , Chuang Grace , Sang Hun Chun , Sabin Jenny E.
Published: November 2019

This paper investigates the potential of kirigami-folding with the addition of strategically placed cuts at multiple scales through both computational design and physical prototyping. The study develops a novel method and workflow for generating two-dimensional (2D) kirigami patterns developed from doubly curved three-dimensional (3D) surfaces (Inverse process). Corresponding simulations of the kirigami folding motion from 2D pattern to 3D goal shape are presented (Forward process). The workflow is based on a reciprocal feedback loop including computational design, finite element analysis, dynamic simulation and physical prototyping. Extended from previous research on kirigami geometry, this paper incorporates material properties into the folding process and successfully develops active kirigami models from the DNA scale to human scale. The results presented in this paper provide an attractive method for kirigami design and fabrication with a wide range of scales and applications.
Meshing Theory of ZC1 Worm Drive
Authors: Zhao Yaping
Published: November 2019

The toroidal enveloping cylindrical worm drive, also called the ZC1 worm drive, is grinded by the toroidal grinding wheel. In this paper, the meshing theory for this worm drive is systematically established. According to this meshing theory, the meshing function, the meshing limit function, the equations of the worm helicoid and the worm gear tooth surface are obtained. A method for computing the normal vector of the instantaneous line of the ZC1 worm pair is proposed. Due to this method, the curvature interference limit function and the meshing quality parameters can be more simply and clearly obtained. Based on above results, the methods of the numerical calculation of the instantaneous lines and the conjugate zone are proposed. The initial values of the nonlinear equation systems, computed the conjugate zone and the contact lines, are detected and solved by the method based on the elimination method and geometric construction. The results of numerical example clearly reflect that the conjugate zone can almost cover the whole tooth surface of the worm gear and the effective working length of the worm cannot nearly exceed the half of its thread length. The values of the induced principle curvature and the sliding angle show that the lubrication performance is poor and the stress level is higher, near the meshing limit line and at the dedendum of the worm gear.
An Incremental Harmonic Balance Method With Two Time Scales for Quasi-Periodic Motion of Nonlinear Systems With Cubic Nonlinearity
Authors: Huang Jianliang , Zhu Weidong , Chen Shuhui
Published: November 2019

A new incremental harmonic balance (IHB) method with two time scales procedure is used to analyze quasi-periodic motion of multiple degrees of freedom systems with cubic nonlinearity. An amplitude increment algorithm is adapted to deal with cases where the two frequencies are unknown a priori, in order to automatically trace frequency response of quasi-periodic motion of the system and accurately calculate all frequency components and their corresponding amplitudes. Results of application of the present IHB method to quasi-periodic free vibration of the nonlinear system are shown and compared with previously published results with Lau method and those from numerical integration. While differences are noted between results predicted by the present IHB method and Lau method, excellent agreement is achieved between results from the present IHB method and numerical integration even in cases of strongly nonlinear vibration.
Computer-Aided Design Ideation Using InnoGPS
Authors: Luo Jianxi , Sarica Serhad , Wood Kristin L.
Published: November 2019

Traditionally, the ideation of design opportunities and new concepts relies on human expertise or intuition and is faced with high uncertainty. Inexperienced or specialized designers often fail to explore ideas broadly and become fixed on specific ideas early in the design process. Recent data-driven design methods provide external design stimuli beyond one’s own knowledge, but their uses in rapid ideation are still limited. Intuitive and directed ideation techniques, such as brainstorming, mind mapping, Design-by-Analogy, SCAMPER, TRIZ and Design Heuristics may empower designers in rapid ideation but are limited in the designer’s own knowledge base. Herein, we harness data-driven design and rapid ideation techniques to introduce a data-driven computer-aided rapid ideation process using the cloud-based InnoGPS system. InnoGPS integrates an empirical network map of all technology domains based on the international patent classification which are connected according to knowledge distance based on patent data, with a few map-based functions to position technologies, explore neighborhoods, and retrieve knowledge, concepts and solutions in the near or far fields for design analogies and syntheses. The functions of InnoGPS fuse design science, network science, data science and interactive visualization and make the design ideation process data-driven, theoretically-grounded, visually-inspiring, and rapid. We demonstrate the procedures of using InnoGPS as a data-driven rapid ideation tool to generate new rolling toy design concepts.
Resource Allocation for System Reliability Analysis Using Accelerated Life Testing
Authors: Moustafa Kassem , Hu Zhen , Mourelatos Zissimos P. , Baseski Igor , Majcher Monica
Published: November 2019

Accelerated life test (ALT) has been widely used to accelerate the product reliability assessment process by testing product at higher than nominal stress conditions. For a system with multiple components, the tests can be performed at component-level or system-level. The data at these two levels require different amount of resources to collect and carry different values of information for system reliability assessment. Even though component-level tests are cheap to perform, they cannot account for the correlations between the failure time distributions of different components. While system-level tests can naturally account for the complicated dependence between component failure time distributions, the required testing efforts are much higher than that of component-level tests. This research proposes a novel resource allocation framework for ALT-based system reliability assessment. A physics-informed load model is first employed to bridge the gap between component-level tests and system-level tests. An optimization framework is then developed to effectively allocate testing resources to different types of tests. The information fusion of component-level and system-level tests allows us to accurately estimate the system reliability with a minimized requirement on the testing resources. Results of one numerical example demonstrate the effectiveness of the proposed framework.
Generative Design of Multi-Material Hierarchical Structures via Concurrent Topology Optimization and Conformal Geometry Method
Authors: Jiang Long , Chen Shikui , Gu Xianfeng David
Published: November 2019

Topology optimization has been proved to be an automatic, efficient and powerful tool for structural designs. In recent years, the focus of structural topology optimization has evolved from mono-scale, single material structural designs to hierarchical multimaterial structural designs. In this research, the multi-material structural design is carried out in a concurrent parametric level set framework so that the structural topologies in the macroscale and the corresponding material properties in mesoscale can be optimized simultaneously. The constructed cardinal basis function (CBF) is utilized to parameterize the level set function. With CBF, the upper and lower bounds of the design variables can be identified explicitly, compared with the trial and error approach when the radial basis function (RBF) is used. In the macroscale, the ‘color’ level set is employed to model the multiple material phases, where different materials are represented using combined level set functions like mixing colors from primary colors. At the end of this optimization, the optimal material properties for different constructing materials will be identified. By using those optimal values as targets, a second structural topology optimization is carried out to determine the exact mesoscale metamaterial structural layout. In both the macroscale and the mesoscale structural topology optimization, an energy functional is utilized to regularize the level set function to be a distance-regularized level set function, where the level set function is maintained as a signed distance function along the design boundary and kept flat elsewhere. The signed distance slopes can ensure a steady and accurate material property interpolation from the level set model to the physical model. The flat surfaces can make it easier for the level set function to penetrate its zero level to create new holes. After obtaining both the macroscale structural layouts and the mesoscale metamaterial layouts, the hierarchical multimaterial structure is finalized via a local-shape-preserving conformal mapping to preserve the designed material properties. Unlike the conventional conformal mapping using the Ricci flow method where only four control points are utilized, in this research, a multi-control-point conformal mapping is utilized to be more flexible and adaptive in handling complex geometries. The conformally mapped multi-material hierarchical structure models can be directly used for additive manufacturing, concluding the entire process of designing, mapping, and manufacturing.
Build Your Firm With Strangers?: Longitudinal Studies on Open Source Hardware Firm Growth
Authors: Li Zhuoxuan , Seering Warren
Published: November 2019

The success of many open source software projects revealed the power of voluntary collaborative production of large/complex software systems. The research community is therefore curious about the viability of open source projects in other areas. In around 2000, open source practices started to take place in the commercial hardware realm, and so far, the phenomenon has not been fully explored. Using grounded theory, the authors studied 31 firms for an average of 2.3 years, discovering a 4-phase growth pattern of open source hardware firms, including starting the firm, identifying core competencies, business model improvement and business maturation. The firms behaviors in each stage are reported, as well as the evolution of community demography, behaviors and impact in different growth phases.
A Differential Game Approach to Dynamic Competitive Decisions Toward Human-Computer Collaboration
Authors: Bayrak Alparslan Emrah , McComb Christopher , Cagan Jonathan , Kotovsky Kenneth
Published: November 2019

Partnership between humans and computers has a significant potential to extend the ability of humans to address complex design problems. This paper presents a decision-making process for computers to effectively collaborate with humans in the solution of complex problems under dynamic competition. In the proposed process, the computers learn strategies and objectives from prior experimental data and provide strategy suggestions to human collaborators. The study integrates clustering and sequential learning methods from machine learning with a differential game formulation based on model predictive control to find dynamic Nash equilibrium solutions to zero-sum games. The application of the proposed approach is demonstrated on the real-time strategy game Starcraft II that offers a dynamic competitive problem comparable in complexity to real-world applications. The results show that the proposed approach can successfully identify a variety of opening strategies in the experimental data for the initial phase of the process. The game-theoretic strategies in the later phases provide useful suggestions for low-performing players but are unnecessarily conservative for high-performing players where there is little opportunity for improvement. These results suggest a need for an assessment of the opponent expertise and a human intuition to judge the appropriateness of the game-theoretic suggestions for further improvement.
Thermomechanical Topology Optimization of Lattice Heat Transfer Structure Including Natural Convection and Design Dependent Heat Source
Authors: Wu Tong , Najmon Joel C. , Tovar Andres
Published: November 2019

Lattice Heat Transfer (LHT) structures provide superior structural support while improving the heat transfer coefficient through their high surface-to-volume ratios. By using current Additive Manufacturing (AM) technologies, LHT with highly complex structures is possible. In this study, the design concept of LHT is further improved by implementing a thermomechanical topology optimization method. With utilization of design-dependent heat source, the method can be applied to generate stiffer LHT structures under mechanical and thermomechanical loads, without decreasing their thermal performance; relative to a design made of a uniform LHT having the same mass fraction. Two numerical examples are presented to illustrate how to use the proposed approach to design LHT sections. The results show that the mechanical performance can be improved more than 50% compared to a uniform LHT with the same mass fraction, without decreasing the thermal performance. The method does not require a fluid mechanics model, thus it is computational effective and particularly suitable for the conceptual design stage. The resulting optimized lattice is made possible by utilizing additive manufacturing technologies.
Modified Model-Free Adaptive Control Method Applied to Vibration Control of an Elastic Crane
Authors: Pham Hoang Anh , Söffker Dirk
Published: November 2019

Model-free adaptive control (MFAC) is a data-driven control approach receiving increased attention in the last years. Different model-free-based control strategies are proposed to design adaptive controllers when mathematical models of the controlled systems should not be used or are not available. Using only measurements (I/O data) from the system, a feedback controller is generated without the need of any structural information about the controlled plant. In this contribution an improved MFAC is discussed for control of unknown multivariable flexible systems. The main improvement in control input calculation is based on the consideration of output tracking errors and its variations. A new updated control input algorithm is developed. The novel idea is firstly applied for controlling vibrations of a MIMO ship-mounted crane. The control efficiency is verified via numerical simulations. The simulation results demonstrate that vibrations of the elastic boom and the payload of the crane can be reduced significantly and better control performance is obtained when using the proposed controller compared to standard model-free adaptive and PI controllers.
A Modular Approach to Level Curve Tracking With Two Nonholonomic Mobile Robots
Authors: Chatterjee Sarthak , Wu Wencen
Published: November 2019

In this paper, we consider the problem of tracking noisy two-dimensional level curves using only the instantaneous measurements of the field, taken by two mobile agents, without the need of estimating the field gradient. To do this, we propose a dual-control-module structure consisting of the formation control and curve tracking modules. The former uses the linear velocity of the agents to generate the angular velocities, which are then used to maintain a constant distance between the two agents. The latter uses the instantaneous field measurements to generate the linear velocities of the two agents to successfully track level curves. The modular approach decouples the problems of formation control and curve tracking, thus allowing the seamless design of the two modules. We show that the proposed dual-module control structure allows fast and accurate tracking of planar level curves.
A Framework of Multi-Fidelity Topology Design and its Application to Optimum Design of Flow Fields in Battery Systems
Authors: Yaji Kentaro , Yamasaki Shintaro , Tsushima Shohji , Fujita Kikuo
Published: November 2019

We propose a novel framework based on multi-fidelity design optimization for indirectly solving computationally hard topology optimization problems. The primary concept of the proposed framework is to divide an original topology optimization problem into two subproblems, i.e., low- and high-fidelity design optimization problems. Hence, artificial design parameters, referred to as seeding parameters, are incorporated into the low-fidelity design optimization problem that is formulated on the basis of a pseudo-topology optimization problem. Meanwhile, the role of high-fidelity design optimization is to obtain a promising initial guess from a dataset comprising topology-optimized design candidates, and subsequently solve a surrogate optimization problem under a restricted design solution space. We apply the proposed framework to a topology optimization problem for the design of flow fields in battery systems, and confirm the efficacy through numerical investigations.
Improving the Method for Kinematic Analysis of Mechanisms That Was Based on Parametric Polynomial System With Gröbner Cover
Authors: Arikawa Keisuke
Published: November 2019

A polynomial system that contains parameters is termed a parametric polynomial system (PPS). We had previously proposed a method of kinematic analysis of mechanisms based on PPS with Gröbner cover, where the parameters are used to express link lengths, displacements of active joints, and so on. Calculating Gröbner cover of PPS that expresses kinematic constraints, and interpreting the segments of the parameter space that are generated by Gröbner cover, it is possible to gain an insight for comprehensively understanding kinematic properties of mechanisms characterized by the parameters. In this study, certain improvements to the method were made to enhance its practical application. The validity check of the segments in the real domain using quantifier elimination provides an automatic reliable check even for a large number of segments. The evaluation of the solution spaces in the segments using primary decomposition facilitates the kinematic interpretation of the complex solution spaces. The active joint selection based on the variable order in Gröbner cover enables the analyses without explicitly specifying active joints. Moreover, the alternative algebraic formulation of kinematic problems based on a homogeneous transformation matrix provides further insight regarding the mechanisms containing zero-length links. The effectiveness of these improvements was verified by the analyses of the configurations of 3RPR mechanism and five-bar linkage.
Model-Based Combustion Duration and Ignition Timing Prediction for Combustion Phasing Control of a Spark-Ignition Engine Using In-Cylinder Pressure Sensors
Authors: Wang Xin , Khameneian Amir , Dice Paul , Chen Bo , Shahbakhti Mahdi , Naber Jeffrey D. , Archer Chad , Qu Qiuping , Glugla Chris , Huberts Garlan
Published: November 2019

Combustion phasing, which can be defined as the crank angle of fifty percent mass fraction burned (CA50), is one of the most important parameters affecting engine efficiency, torque output, and emissions. In homogeneous spark-ignition (SI) engines, ignition timing control algorithms are typically map-based with several multipliers, which requires significant calibration efforts. This work presents a framework of model-based ignition timing prediction using a computationally efficient control-oriented combustion model for the purpose of real-time combustion phasing control. Burn duration from ignition timing to CA50 (ΔθIGN-CA50) on an individual cylinder cycle-by-cycle basis is predicted by the combustion model developed in this work. The model is based on the physics of turbulent flame propagation in SI engines and contains the most important control parameters, including ignition timing, variable valve timing, air-fuel ratio, and engine load mostly affected by combination of the throttle opening position and the previous three parameters. With 64 test points used for model calibration, the developed combustion model is shown to cover wide engine operating conditions, thereby significantly reducing the calibration effort. A Root Mean Square Error (RMSE) of 1.7 Crank Angle Degrees (CAD) and correlation coefficient (R2) of 0.95 illustrates the accuracy of the calibrated model. On-road vehicle testing data is used to evaluate the performance of the developed model-based burn duration and ignition timing algorithm. When comparing the model predicted burn duration and ignition timing with experimental data, 83% of the prediction error falls within ±3 CAD.
Can Process Metrics Predict Product Success?: A Pilot Study of Student Design Teams
Authors: Bracken Jennifer , Glavin Francis Xavier , Henderson Daniel , Jablokow Kathryn , Sonalkar Neeraj , Erdman Andrew Michael
Published: November 2019

Engineering projects typically revolve around producing a deliverable. That deliverable goes to a customer, who either deems it acceptable or in need of further work. The engineering analysis and components of whatever system is to be produced are the subject of much scrutiny. However, the human process composed of team interactions that lead up to creating that final product is frequently treated as a “black box” that simply produces an output. In order to identify what factors in that process are key to a successful product, this work seeks to identify what successful engineering design teams do differently than less successful teams. As part of our larger research project, metrics for measuring team performance during the process of design have also been created. In this paper, we use three of those metrics in a case study of 5 senior-level student design teams. These data are employed in conjunction with feedback from the instructor, acting as their customer, to identify which behaviors had strong links with more successful team results. We also investigate whether any of the behaviors exhibited by the teams correspond to worse results, in order to identify behaviors with the potential to be used to predict poorer performance in advance. This analysis is completed using data collected via a mid-term survey and an end-of-project survey (both completed by the team members), in addition to video and audio meeting data, and data collected from both midterm and final presentations. We present these results as an avenue to move us towards enabling engineers to choose to engage knowingly in behaviors that correlate with better project results.
Design and Optimization of an Origami-Inspired Jumping Mechanism With Nonlinear Stiffness Properties
Authors: Sadeghi Sahand , Betsill Blake D. , Li Suyi
Published: November 2019

This research investigates the feasibility of utilizing origami folding techniques to create an optimized jumping mechanism. As a theoretical example, we study the dynamic characteristics of a jumping mechanism consisting of two masses connected by a Tachi-Miura Polyhedron (TMP) origami structure with nonlinear stiffness characteristics. We show how the desired “strain-softening” effects of the TMP structure can lead to design of jumping mechanisms with optimized performance. The kinematics of TMP origami structure is reviewed and a modified model of its reaction-force displacement curve is presented. We derive the equations of motion of the jumping process and use their numerical solutions extensively for design optimization. Through this process we are able to obtain optimum geometrical configurations for two different objectives: The maximum time spent in the air and the maximum clearance off the ground. Results of this study can lead to emergence of a new generation of more efficient jumping mechanisms with optimized performance in the future.
MPC-Based Control of Autonomous Vehicles With Localized Path Planning for Obstacle Avoidance Under Uncertainties
Authors: Devaragudi Sai Rajeev , Chen Bo
Published: November 2019

This paper presents a Model Predictive Control (MPC) approach for longitudinal and lateral control of autonomous vehicles with a real-time local path planning algorithm. A heuristic graph search method (A* algorithm) combined with piecewise Bezier curve generation is implemented for obstacle avoidance in autonomous driving applications. Constant time headway control is implemented for a longitudinal motion to track lead vehicles and maintain a constant time gap. MPC is used to control the steering angle and the tractive force of the autonomous vehicle. Furthermore, a new method of developing Advanced Driver Assistance Systems (ADAS) algorithms and vehicle controllers using Model-In-the-Loop (MIL) testing is explored with the use of PreScan®. With PreScan®, various traffic scenarios are modeled and the sensor data are simulated by using physics-based sensor models, which are fed to the controller for data processing and motion planning. Obstacle detection and collision avoidance are demonstrated using the presented MPC controller.