bluuebunny/crossref_metadata_2025 huggingface dataset:
On the Compatibility Equations in Geometrically Exact Beam Finite Element
Authors: Sonneville Valentin , Bauchau Olivier A. , Brüls Olivier
Published: December 2016

This paper discusses the compatibility equations which relate the velocity field and the strain field in geometrically exact beam theory. The analysis is carried out in the context of intrinsic equations, namely the dynamic equilibrium equations are formulated in terms of local velocities and strains only. In addition to the well established objectivity and path-independence requirements of the spatial discretization, these compatibility equations show that a consistent spatial interpolation of the velocity field should depend on the curvature of the beam, including initial curvature and curvature from the deformation, and it is shown that this consistency is connected to the ability of the finite element to represent exactly rigid body motion velocities. A two node interpolation scheme is studied and it appears that, as the element gets smaller under mesh refinement, the effect of this dependency reduces, leading eventually to the classical linear shape functions.
A Geometry Projection Method for the Optimal Design of Panel Reinforcements With Ribs Made of Plates
Authors: Zhang Shanglong , Norato Julián A.
Published: December 2016

The stiffness of plate structures can be significantly improved by adding reinforcing ribs. In this paper, we are in particular concerned with stiffening of panels using ribs made of constant-thickness plates that may be, for example, welded to the panel. These ribs are common in, for example, the reinforcement of ship hulls, aircraft wings, pressure vessels and storage tanks. Existing methods either produce rib designs that cannot be fabricated with plates, or employ heuristics that produce non-optimal designs. This paper presents a method for optimally designing the topology, locations and dimensions of rectangular ribs to reinforce a panel. To this end, we smoothly project an analytic, explicit geometry representation of a set of ribs onto a continuous density field over a design envelope. This density field is discretized in an element-wise manner on a uniform grid for analysis. The initial design consists of a prescribed set of ribs, constrained to remain perpendicular to the panel to facilitate manufacturing and joining of the ribs.

The advantages of our method are two-fold. On one hand, as in classical density-based topology optimization, we circumvent re-meshing by using a fixed finite element grid for the analysis, and the differentiability of the projection allows us to employ efficient and robust gradient-based optimization methods. On the other hand, the explicit geometry representation provides a direct translation into CAD, it produces reinforcement designs that conform to available plate cutting and joining processes, and it allows us to impose a constraint on the minimum separation between any two ribs to guarantee clear gaps for weld gun access. Also, bounds on the ribs dimensions can be naturally and directly accommodated. We present numerical examples of our panel reinforcement design under different types of loadings to demonstrate the applicability of the proposed method.
A New Smart Strategy for Web Searching of Commercial Products
Authors: Cavalieri Lorenzo , Capitanelli Andrea , Ceccacci Silvia , Gullà Francesca , Papetti Alessandra , Germani Michele
Published: December 2016

Search engine efficiency is an essential prerequisite to ensure a satisfactory on-line purchasing experience. Despite powerful tools available today, search engine is limited to a semantic elaboration of keywords and they do not allow users finding product categories that do not belong to their knowledge sphere. In this context, in order to make an effective search engine it is necessary to provide tools able to understand what the user is looking for and suggest the products that best satisfy their needs, regardless of users’ background.

To this aim, this paper proposes an innovative smart search strategy, based on artificial intelligence technologies. In order to highlight the system potential, the smart object market case study has been considered. The SOs market is grown so quickly to disorient the average user and it offer a wide variety of products apparently similar, but that are characterized by different features that the average user fails to perceive.
Integration of Customer-Product Interaction Into Quality Function Deployment
Authors: Martí Bigorra Anna , Isaksson Ove
Published: December 2016

Customer satisfaction is used by many companies as a key performance indicator and it is strategically important to be able to define design requirements that contribute to customer satisfaction when setting targets. For highly complex products such as vehicles, target setting is an evolving process based on continually changing internal and external requirements. Quality Function Deployment (QFD) is a method that provides a structured approach for incorporating customer needs into the product development process. However, in addition to product targets, product usage proficiency also contributes to customer satisfaction. Customers often do not read manuals; they learn by trying things out and sometimes the use of the product ends up outside the expected acceptable range of the designers, delivering to the customer low product performance. The intention of this article is therefore to gain a deeper understanding of the customer by analyzing customer-product interaction of customer products and integrating it into QFD to identify the most interesting design requirements to improve customer satisfaction when developing products that are comparable to the ones lunched in the market. The proposed method facilitates designer awareness of target population before re-designing an existing product and it helps designers to set a starting point to improve usage proficency for each customer by providing individualized feedback.
Countersteering to Recover Straight Ahead Running After a Disturbance
Authors: della Rossa Fabio , Gobbi Massimiliano , Mastinu Giampiero , Previati Giorgio
Published: December 2016

The paper deals with the analysis of a manoeuvre occurring frequently before crashes. Due to an external disturbance the straight ahead running of a vehicle is degradated into an oscillating motion. The driver is required to countersteer to recover the straight ahead motion. The bifurcation analysis of a simple model describing a vehicle+driver running straight ahead is performed. The mechanical model of the car has two degrees of freedom and the related equations of motion contain the non linear tyre characteristics. The driver is described by a non linear model defined by three parameters, namely the gain (steering wheel angle per lateral deviation from desired path), the prevision distance, the reaction time delay.

Unreferenced bifurcations are discovered for the understeering vehicle. A supercritical Hopf bifurcation may occur as forward speed is increased. Also tangent (fold) bifurcations occur as the speed (or disturbance) are further increased.

The vehicle+driver model is validated by means of a number of tests performed in a track. The validation relies on the identification of driver’s parameters. The track is equipped with a plank sliding laterally when the vehicle rear axle passes on it. Such a lateral excitation applies a disturbance to the vehicle which initiates a spin to be counteracted by the driver. An analysis is performed on driver’s parameters identification. Such parameter identification seems a possible way to assess single driver’s ability to perform recovery manoeuvres.
Toward Risk-Informed Operation of Autonomous Vehicles to Increase Resilience in Unknown and Dangerous Environments
Authors: Mimlitz Zachary , Short Adam , Van Bossuyt Douglas L.
Published: December 2016

Operation of autonomous and semi-autonomous systems in hostile and expensive-to-access environments requires great care and a risk-informed operating mentality to protect critical system assets. Space exploration missions, such as the Mars Exploration Rover systems Opportunity and Curiosity, are very costly and difficult to replace. These systems are operated in a very risk-averse manner to preserve the functionality of the systems. By constraining system operations to risk-averse activities, scientific mission goals cannot be achieved if they are deemed too risky. We present a quantifiable method that increases the lifetime efficiency of obtaining scientific goals via the implementation of the Goal-Oriented, Risk Attitude-Driven Reward Optimization (GORADRO) method and a case study conducted with simulated testing of the method. GORADRO relies upon local area information obtained by the system during operations and internal Prognostics and Health Management (PHM) information to determine system health and potential localized risks such as areas where a system may become trapped (e.g.: sand pits, overhangs, overly steep slopes, etc.) while attempting to access scientific mission objectives through using an adaptable operating risk attitude. The results of our simulations and hardware validation using GORADRO show a large increase in the lifetime performance of autonomous rovers in a variety of environments, terrains, and situations given a sufficiently tuned set of risk attitude parameters. Through designing a GORADRO behavioral risk attitude set of parameters, it is possible to increase system resilience in unknown and dangerous environments encountered in space exploration and other similarly hazardous environments.
User Preferences in the Design of Multi-Purpose Products: A Case Study on the Redesign of a Utility Tool
Authors: Viswanathan Vimal , Sangelkar Shraddha , Alexander David , Moody Johnathan Brock
Published: December 2016

There is an abundance of multi-purpose products in the market. Multi-purpose products are defined as the artifacts that have more than one primary function. While some of the multipurpose products are well designed and successful in the market, many of them fail to make an impact. Many times, these products are designed without considering critical aspects of customer requirements into account. The current research in design theory and methodology primarily addresses the design of products with one main function and many supporting sub-functions. When more than one primary function is present in the design, the process becomes more complicated. Many times, the primary functions conflict with each other, making the design process further complicated. Motivated by this fact, we aim to develop a comprehensive design method that considers the user’s perspective for the design of multi-purpose products. As a first step, we developed a set of guidelines from a user point of view. This paper presents a case study where the authors redesigned a multi-purpose utility tool, which has a very high potential and a targeted audience. The original design process behind the product is studied with the help of the designer and the manufacturer. The newly developed guidelines are repeatedly applied on the product to derive new requirements for its redesign. Following a systematic redesign procedure, the product is redesigned. This paper details the case study along with the lessons learned from the same. It also describes the best scenarios for the use of the multi-purpose product design guidelines.
Human Action Modeling and Application to a Control System Using the Mahalanobis-Taguchi System
Authors: Asaka Yusuke , Watanuki Keiichi , Fukuda Shuichi , Muramatsu Keiichi , Kaede Kazunori
Published: December 2016

This paper discusses human action modeling and its application to a control system that uses the Mahalanobis – Taguchi System (MTS). In this study, we define embodied knowledge as being included in tacit knowledge. We also define a set of skills based on experiences and intuitive sense as seen in creating an art, sport, craft, or other skilled task. Embodied knowledge is difficult to express explicitly. As our goals, we analyze embodied knowledge acquisition for human action modeling and apply to a control system by using MTS.

An analysis of embodied knowledge using devices and pattern-recognition techniques to recognize un-explicit knowledge are being developed owing to recent improvements in technology. Embodied knowledge acquisition element of recognition can be represented as a pattern recognition technique. In this paper, we confirm that MTS is an adaptive method for recognizing pattern in human action modeling.

We set up a control model including a controller using the MTS, which is modeled after an internal model of the cerebellum. We apply the controller based on the Recognition Taguchi (RT) method to invert the control of the pendulum. The result indicate that the controller is capable of detecting disturbance.
Architecting Fail-Safe Supply Networks
Authors: Rezapour Shabnam , Srinivasan Ramakrishnan S. , Tew Jeffrey , Allen Janet K. , Mistree Farrokh
Published: December 2016

A fail-safe network is one that mitigates the impact of different uncertainty sources and provides the most profitable level of service. This is achieved by having 1) a structurally fail-safe topology against rare but high magnitude stochastic events called disruptions and 2) an operationally fail-safe flow dynamic against frequent but low magnitude stochastic events called variations. A structurally fail-safe network should be robust and resilient against disruptions. Robustness and resilience respectively determine how well and how quickly disruptions are handled by the SN. Flow planning must be reliable in an operationally fail-safe supply network against variations to provide the most profitable service level to customers. We formulate the problem of designing/redesigning fail-safe supply networks as a compromise Decision Support Problem. We analyze the correlations among robustness, resilience, and profit for supply networks and propose a method for supply network managers to use when they need to find a compromise among robustness, resilience, and profit.
Comparative Study of Mechanical Error in P3R and 4R Manipulators
Authors: Jaiswal Ankur , Jawale H. P.
Published: December 2016

This article incorporates formulation and application for the varying position of output link of P3R and 4R manipulator configuration and estimation of mechanical error. The behavior of the output link is analyzed for linear and rotary inputs for both the type of configurations. The variation in link tolerance is introduced as input for estimating the mechanical error for P3R and 4R manipulator with the derived formula. Comparison of the errors provides the basis of selecting the best manipulator with the minimum mechanical error. Sample case is illustrated for the identical operational features in these P3R and 4R manipulators and the performance of these manipulators is quantified in this paper. The methodology is successful in considering the effect of source of mechanical errors in general; the proposed methodology is going to be helpful for the analysis of spatial and parallel manipulator.
Stabilization of a Quadrotor With Uncertain Suspended Load Using Sliding Mode Control
Authors: Zhou Xu , Liu Rui , Zhang Jiucai , Zhang Xiaoli
Published: December 2016

The stability and trajectory control of a quadrotor carrying a suspended load with a fixed known mass has been extensively studied in recent years. However, the load mass is not always known beforehand in practical applications. This mass uncertainty brings uncertain disturbances to the quadrotor system, causing existing controllers to have a worse performance or to be collapsed. To improve the quadrotor’s stability in this situation, we investigate the impacts of the uncertain load mass on the quadrotor. By comparing the simulation results of two controllers — the proportional-derivative (PD) controller and the sliding mode controller (SMC) driven by a sliding mode disturbance of observer (SMDO), the quadrotor’s performance is verified to be worse as the uncertainty increases. The simulation results also show a controller with stronger robustness against disturbances is better for practical applications.
Robust Framework for the Computed Torque Control of Nondeterministic Multibody Dynamic Systems
Authors: Sabet Sahand , Poursina Mohammad
Published: December 2016

Considering uncertainty is inarguably a crucial aspect of dynamic analysis, design, and control of a mechanical system. When it comes to multibody problems, the effect of uncertainty in the system’s parameters and excitations becomes even more significant due to the accumulation of inaccuracies. For this reason, this paper presents a detailed research on the use of the Polynomial Chaos Expansion (PCE) method for the control of nondeterministic multibody systems. PCE is essentially a way to compactly represent random variables. In this scheme, each stochastic response output and random input is projected onto the space of appropriate independent orthogonal polynomial basis functions. In the field of robotics, a required task is to force robotic arms to follow designated paths. Controlling such systems usually leads to difficulties since the dynamic equations of multibody problems are highly nonlinear. Computed Torque Control Law (CTCL) is able to overcome these difficulties by using feedback linearization to evaluate the required torque/force at any time to make the system follow a trajectory. In this paper, a mathematical framework is introduced to apply the Computed Torque Control Law to a multibody system with uncertainty. Surprisingly, it is shown that using this control scheme, uncertainty in geometry does not affect the closed-loop equations of controlled systems. Both the intrusive PCE method and the Monte Carlo approach are used to control a fully actuated two-link planar elbow arm where each link is required to follow a specified path. Lastly, a comparison of the time efficiency and accuracy between the traditionally used Monte Carlo method and the intrusive PCE is presented. The results indicate that the intrusive PCE approach can provide better accuracy with much less computation time than the Monte Carlo method.
iFEED: Interactive Feature Extraction for Engineering Design
Authors: Bang Hyunseung , Selva Daniel
Published: December 2016

One of the major challenges faced by the decision maker in the design of complex engineering systems is information overload. When the size and dimensionality of the data exceeds a certain level, a designer may become overwhelmed and no longer be able to perceive and analyze the underlying dynamics of the design problem at hand, which can result in premature or poor design selection. There exist various knowledge discovery and visual analytic tools designed to relieve the information overload, such as BrickViz, Cloud Visualization, ATSV, and LIVE, to name a few. However, most of them do not explicitly support the discovery of key knowledge about the mapping between the design space and the objective space, such as the set of high-level design features that drive most of the trade-offs between objectives. In this paper, we introduce a new interactive method, called iFEED, that supports the designer in the process of high-level knowledge discovery in a large, multiobjective design space. The primary goal of the method is to iteratively mine the design space dataset for driving features, i.e., combinations of design variables that appear to consistently drive designs towards specific target regions in the design space set by the user. This is implemented using a data mining algorithm that mines interesting patterns in the form of association rules. The extracted patterns are then used to build a surrogate classification model based on a decision tree that predicts whether a design is likely to be located in the target region of the tradespace or not. Higher level features will generate more compact classification trees while improving classification accuracy. If the mined features are not satisfactory, the user can go back to the first step and extract higher level features. Such iterative process helps the user to gain insights and build a mental model of how design variables are mapped into objective values. A controlled experiment with human subjects is designed to test the effectiveness of the proposed method. A preliminary result from the pilot experiment is presented.
Designing a Low Activation Pressure Drip Irrigation Emitter With Constraints for Mass Manufacturing
Authors: Shamshery Pulkit , Winter Amos G.
Published: December 2016

This work discusses the modeling and optimization of a drip irrigation emitter for reducing activation pressure. Our model formulation focuses on analytically characterizing fluid-structure interactions in an existing 8 liters per hour (lph) pressure-compensating online emitter. A preliminary experimental validation of the resulting model was performed for three different emitter architectures. This model was used as a basis for a genetic algorithm-based optimization algorithm that focused on minimizing activation pressure. The design variables considered in our formulation include, geometric features of the emitter architecture, and practical constraints from manufacturing. We applied our optimization approach to four emitters (with flow rates of 4, 6, 7 and 8.2 lph) and were able to lower activation pressure by more than half in each case. The optimization results for all four emitters were experimentally validated in lab-studies. We performed a more exhaustive validation study for the 8.2 lph emitter with an emitter manufacturer. Results from these experiments (which followed ISO standards) showed that the optimized 8.2 lph emitter had a 75% lower activation pressure when compared to the original emitter design.
Impressionist: A 3D Peekaboo Game for Crowdsourcing Shape Saliency
Authors: Yao Houpu (Hope) , Ren Max Yi
Published: December 2016

Designers often express their intents (e.g., on product functionalities and semantics) through shape features. Therefore, collecting such “salient” features from existing shapes and learning their associations with design intents will enable efficient design of new shapes. However, the acquisition of saliency knowledge from a large shape collection has not been accomplished. This paper investigates a gamification approach to this end. In addition, we propose to validate a derived saliency map by its corresponding shape recognition accuracy through crowd-sourcing. This allows a comparison across existing and the proposed saliency acquistion and computation methods. Currentl results show that the proposed method achieves statistically similar recognition accuracy to existing saliency data on a standard shape database, indicating that various saliency maps are equally valid according to the proposed saliency definition. Nonetheless, the saliency data obtained through the proposed game consistently produces reasonable viewpoints across shapes, outperforming existing curvature-based and crowdsourcing approaches. The findings from this study could lead to developments of game mechanisms that are more scalable and cost effective at saliency elicitation than existing paid crowdsourcing approaches.
A Data-Driven Approach to Predict Hand Positions for Two-Hand Grasps of Industrial Objects
Authors: Arisoy Erhan Batuhan , Ren Guannan , Ulu Erva , Ulu Nurcan Gecer , Musuvathy Suraj
Published: December 2016

The wide spread use of 3D acquisition devices with high-performance processing tools has facilitated rapid generation of digital twin models for large production plants and factories for optimizing work cell layouts and improving human operator effectiveness, safety and ergonomics. Although recent advances in digital simulation tools have enabled users to analyze the workspace using virtual human and environment models, these tools are still highly dependent on user input to configure the simulation environment such as how humans are picking and moving different objects during manufacturing. As a step towards, alleviating user involvement in such analysis, we introduce a data-driven approach for estimating natural grasp point locations on objects that human interact with in industrial applications. Proposed system takes a CAD model as input and outputs a list of candidate natural grasping point locations. We start with generation of a crowdsourced grasping database that consists of CAD models and corresponding grasping point locations that are labeled as natural or not. Next, we employ a Bayesian network classifier to learn a mapping between object geometry and natural grasping locations using a set of geometrical features. Then, for a novel object, we create a list of candidate grasping positions and select a subset of these possible locations as natural grasping contacts using our machine learning model. We evaluate the advantages and limitations of our method by investigating the ergonomics of resulting grasp postures.
Evolution of Flexible Multibody Dynamics for Simulation Applications Supporting Human Spaceflight
Authors: Huynh An , Brain Thomas A. , MacLean John R. , Quiocho Leslie J.
Published: December 2016

During the course of transition from the Space Shuttle and International Space Station programs to the Orion and Journey to Mars exploration programs, a generic flexible multibody dynamics formulation and associated software implementation has evolved to meet an ever changing set of requirements at the NASA Johnson Space Center (JSC). Challenging problems related to large transitional topologies and robotic free-flyer vehicle capture/release, contact dynamics, and exploration missions concept evaluation through simulation (e.g., asteroid surface operations) have driven this continued development. Coupled with this need is the requirement to oftentimes support human spaceflight operations in real-time. Moreover, it has been desirable to allow even more rapid prototyping of on-orbit manipulator and spacecraft systems, to support less complex infrastructure software for massively integrated simulations, to yield further computational efficiencies, and to take advantage of recent advances and availability of multi-core computing platforms. Since engineering analysis, procedures development, and crew familiarity/training for human spaceflight are fundamental to JSC’s charter, there is also a strong desire to share and reuse models in both the non-real-time and real-time domains, with the goal of retaining as much multibody dynamics fidelity as possible. Three specific enhancements are reviewed here: (1) linked list organization to address large transitional topologies, (2) body level model order reduction, and (3) parallel formulation/implementation. This paper provides a detailed overview of these primary updates to JSC’s flexible multibody dynamics algorithms as well as a comparison of numerical results to previous formulations and associated software.
Mass Customization: Reuse of Digital Slicing for Additive Manufacturing
Authors: Kwok Tsz-Ho , Ye Hang , Chen Yong , Zhou Chi , Xu Wenyao
Published: December 2016

Additive manufacturing, also known as 3D printing, enables production of complex customized shapes without requiring specialized tooling and fixture, and mass customization can then be realized with larger adoption. The slicing procedure is one of the fundamental tasks for 3D printing, and the slicing resolution has to be very high for fine fabrication, especially in the recent developed Continuous Liquid Interface Production (CLIP) process. The slicing procedure is then becoming the bottleneck in the pre-fabrication process, which could take hours for one model. This becomes even more significant in mass customization, where hundreds or thousands of models have to be fabricated. We observe that the customized products are generally in a same homogeneous class of shape with small variation. Our study finds that the slicing information of one model can be reused for other models in the same homogeneous group under a properly defined parameterization. Experimental results show that the reuse of slicing information have a maximum of 50 times speedup, and its utilization is dropped from more than 90% to less than 50% in the pre-fabrication process.
Reliability-Based Design Optimization Using Confidence-Based Model Validation for Insufficient Experimental Data
Authors: Moon Min-Yeong , Choi K. K. , Cho Hyunkyoo , Gaul Nicholas , Lamb David , Gorsich David
Published: December 2016

The conventional reliability-based design optimization (RBDO) methods assume that a simulation model is able to represent the real physics accurately. However, the simulation model could be biased. Accordingly, when the conventional RBDO design is manufactured, the product may not satisfy the target reliability. Therefore, model validation, which corrects model bias, should be integrated in the RBDO process by incorporating experimental data. The challenge is that only a limited number of experimental data is usually available due to the cost of actual product testing. Consequently, model validation for RBDO needs to account for the uncertainty induced by insufficient experimental data as well as variability inherently existing in the products. In this paper, a confidence-based model validation process that captures the uncertainty and corrects model bias at user-specified target conservativeness level is developed. Thus, RBDO can be performed using confidence-based model validation to obtain conservative RBDO design. It is found that RBDO with model bias correction becomes a moving-target problem because the feasible domain changes as the design moves. Consequently, the RBDO optimum may not be easily found due to the convergence problem. To resolve the issue, an efficient process is proposed by carrying out deterministic design optimization (DDO) and RBDO without validation, followed by RBDO with confidence-based model validation. Finally, we demonstrate that the proposed RBDO approach can achieve a conservative and practical optimum design given a limited number of experimental data.
Simulating the Human Shoulder Through Active Tensegrity Structures
Authors: Baltaxe-Admony Leya Breanna , Robbins Ash S. , Jung Erik A. , Lessard Steven , Teodorescu Mircea , SunSpiral Vytas , Agogino Adrian
Published: December 2016

The flexibility and structural compliance of the biological shoulder joint allows humans to perform a wide range of motions with their arms. The current paper is a preliminary study in which we propose a structurally compliant robotic manipulator joint inspired by the human shoulder joint, which elastically deforms when actuated. The tensile actuation is similar to the contraction and extension of biological muscles. We present four separate models for the shoulder: a simple saddle, a complex saddle, a suspended tubercle, and interlocked tetrahedrons. The analysis explores the dynamics in each design to compare the inherent advantages and disadvantages, which gives insight into the design and development of better interfaces for biologically inspired human-oriented robotics.
Foundation Ontology for Distributed Manufacturing Process Planning
Authors: Sarkar Arkopaul , Sormaz Dusan
Published: December 2016

Distributed computer integrated manufacturing is increasingly adopting cloud computing, software-as-a-service (SaaS) and multi-agent systems as steps towards “design anywhere, build anywhere” strategy. In this scenario, foundation ontologies not only serve as common message exchange structure among distributed agents but also provide reasoning service to extract implicit knowledge from explicit information already stored in the knowledge base. Foundation ontologies, comprised of most general concepts of a domain, provide a common semantic structure to the domain-level ontologies, which capture details of multi-disciplinary manufacturing knowledge. In this paper, foundation ontology for manufacturing process planning is proposed and manufacturing process selection information of a sample prismatic design feature is modeled using the proposed foundation ontology, as a case study.
A Digital Material Design Framework for 3D-Printed Heterogeneous Objects
Authors: Huang Pu , Li Yongqiang , Chen Yong , Zeng Jun
Published: December 2016

In the paper a digital material design framework is presented to compute multi-material distributions in three-dimensional (3D) model based on given user requirements for additive manufacturing (AM) processes. It is challenging to directly optimize digital material composition due to extremely large design space. The presented material design framework consists of three stages. In the first stage, continuous material property distribution in the geometric model is computed to achieve the desired user requirements. In the second stage, a material dithering method is developed to convert the continuous material property distribution into 3D printable digital material distribution. A tile-based material patterning method and accordingly constructed material library are presented to efficiently perform material dithering in the given 3D model. Finite element analysis (FEA) is used to evaluate the performance of the computed digital material distributions. To mimic the layer-based AM process, cubic meshes are chosen to define the geometric shape in the digital material design stage, and its resolution is set based on the capability of the selected AM process. In the third stage, slicing data is generated from the cubic mesh model and can be used in 3D printing processes. Three test cases are presented to demonstrate the capability of the digital material design framework. Both FEA-based simulation and physical experiments are performed; in addition, their results are compared to verify the tile-based material pattern library and the related material dithering method.
An Approach to Compare Sustainability Performance of Additive and Subtractive Manufacturing During Process Planning
Authors: Doran Michael P. , Smullin Matteo M. , Haapala Karl R.
Published: December 2016

Additive manufacturing is a family of processes that has been gaining attention recently by industry, researchers, and policy makers. Many claims have been made about improved sustainability performance over traditional subtractive machining processes. However, these claims have not been substantiated through sustainability characterization. This paper presents a method to compare several sustainability metrics for parts of varying size produced by additive and subtractive manufacturing processes. The production of a part is modeled using direct energy deposition (DED) as the representative additive process and milling as the representative subtractive process. The results indicate that milling has superior performance than DED when relatively small volumes of material are removed from an initial workpiece. As more material is removed by milling, the difference between the performance of DED and milling decreases. With increasing material removal volumes, DED becomes the superior process from a sustainable manufacturing perspective. This research gives decision makers a demonstrated approach and selection windows for the superior process type for a given workpiece design and preselected sustainability assessment metrics.
Enumeration of Architectures With Perfect Matchings
Authors: Herber Daniel R. , Guo Tinghao , Allison James T.
Published: December 2016

In this article a class of architecture design problems is explored with perfect matchings. A perfect matching in a graph is a set of edges such that every vertex is present in exactly one edge. The perfect matching approach has many desirable properties such as complete design space coverage. Improving on the pure perfect matching approach, a tree search algorithm is developed that more efficiently covers the same design space. The effect of specific network structure constraints and colored graph isomorphisms on the desired design space is demonstrated. This is accomplished by determining all unique feasible graphs for a select number of architecture problems, explicitly demonstrating the specific challenges of architecture design. Additional applications of this work to the larger architecture design process is also discussed.
Characterization of Antifragility in Cyber Systems Using a Susceptibility Metric
Authors: Devendorf Erich , Zeliff Kayla , Jabbour Kamal
Published: December 2016

Traditional engineering design practice seeks to create reliable systems that maintain a desired minimum performance when subjected to a defined set of impulses. To manage impulses, designers implement techniques to specify systems that are resilient or robust to impulses. Resilient systems perform with degraded capacity when subjected to impulses while robust systems remain unaffected by impulses. In this paper we examine antifragility, a complement to resilience and robustness, to manage the impulse response of complex cyber systems. Where fragile systems fracture when subjected to impulses, antifragile systems become stronger. We discuss why this strengthening characteristic makes antifragility attractive for managing impulse response in complex cyber systems and develop a measure for antifragility that differentiates it from fragility, resiliency and robustness. We then discuss an antifragile cyber system to demonstrate the benefits of antifragility in an impulse-rich environment.
Ontology-Based Laser and Thermal Metamodels for Metal-Based Additive Manufacturing
Authors: Roh Byeong-Min , Kumara Soundar R. T. , Simpson Timothy W. , Michaleris Panagiotis , Witherell Paul , Assouroko Ibrahim
Published: December 2016

Additive manufacturing (AM) is a promising technology that is expected to revolutionize industry by allowing the production of almost any shape directly from a 3D model. In metal-based AM, numerous process parameters are highly interconnected, and their interconnections are not yet understood. Understanding this interconnectivity is the first step in building process control models that help make the process more repeatable and reliable. Metamodels can be used to conceptualize models of complex AM processes and capture diverse parameters to provide a graphical view using common terminology and modeling protocols. In this paper we consider different process models (laser and thermal) for metal-based AM and develop an AM Process Ontology from first-principles. We discuss and demonstrate its implementation in Protégé.
Problem Discovery With Many-Objective Visual Analytics
Authors: Woodruff Matthew , Simpson Timothy W.
Published: December 2016

Problem discovery is messy. It involves many mistakes, which may be regarded as a failure to address a design problem correctly. Mistakes, however, are inevitable, and misunderstanding the problems we are working on is the natural, default state of affairs. Only through engaging in a series of mistakes can we learn important things about our design problems.

This study provides a case study in Many-Objective Visual Analytics (MOVA), as applied to the problem of problem discovery. It demonstrates the process of continually correcting and improving a problem formulation while visualizing its optimization results. This process produces a new, clearer understanding of the problem and puts the designer in a position to proceed with more-detailed design decisions.
Patterned Carbon Nanotube Growth on Convex Cylindrical Stainless Steel Substrates for the Production of Coronary Stents
Authors: Robison Warren , Jensen Brian , Bowden Anton
Published: December 2016

This paper reports research in fabrication of cylindrical stents using carbon-infiltrated carbon nanotubes (CI-CNT), a material with good hemocompatibility. We demonstrate growth of CI-CNT forests in patterned lines on a 3 mm diameter stainless steel (SS) rod. Lines were patterned parallel, at 7°, at 45°, and perpendicular relative to the axis of the rod. Minimal cracking was seen in the parallel and angled lines. Significant cracking was seen in the perpendicular lines and we attempted to characterize the cracking in order to correlate it to width of the lines and height of the forest. No correlation was found but the average uncracked length was determined to be 414 μm with a standard deviation of 67 μm. We also demonstrate successful growth with minimal cracking of CI-CNT forests in a zig-zag type pattern in an effort to further the possibility of creating a coronary stent utilizing CI-CNT. Some of the patterned samples were also removed from the cylindrical substrate, resulting in free-standing, patterned, cylindrical patterns made from CI-CNT.
Image Based Slicing and Tool Path Planning for Hybrid Stereolithography Additive Manufacturing
Authors: Ye Hang , Zhou Chi , Xu Wenyao
Published: December 2016

Hybrid stereolithograpgy (SLA) process synthesizes the laser scanning based SLA system and mask projection based SLA system. It adopts laser as the energy source for scanning the border of a 2D pattern, whereas a mask image is used to solidify the interior area. By integrating the merits of the two subsystems, the hybrid SLA process can achieve relatively high surface quality without sacrificing the productivity. For the hybrid system, closed polygon contours are required to direct laser scanning, and a binary image is also needed for mask projection. We proposed a novel image based slicing method. This method can convert the 3D model into a series of binary images directly, and each image is corresponding to the cross-session of the model at a specific height. Based on the resultant binary image, we use image processing method to gradually shrink the image. The contours of shrunk image are traced and then restored as polygons to direct the laser spot movement. The final shrunk image will serve as the input for mask projection. The experimental result of several test cases demonstrate that proposed method is substantially more time-efficient than traditional approaches.
Designing Integrated STEM Education: Linking STEM Teachers and Learners in a K-20 Continuum
Authors: Shahbazi Zahra , Jacobs MaryAnn , Lehnes Alexandra , Mancuso Kathleen
Published: December 2016

To address the grand challenge of the severe shortage of qualified engineering workforce and equally important educators, engineering and education departments at Manhattan College created a holistic program called Engineering Scholars Training and Retention (STAR) program. Engineering STAR program created a collaboration among undergraduate education and engineering majors, the professors who teach them and current STEM teachers and their students in local urban middle and high schools.

We developed three new academic programs (engineering education minor and certificate programs for both undergraduate and graduate engineering and education majors) to support engineering and education students who are passionate about promoting engineering for 6–12 grade students and become qualified and competent engineering educators.

In addition, through partnership with local middle/high schools we developed an engineering ambassadors’ program where students from engineering and education majors develop hands-on design projects and present them to middle/high school students to encourage and inspire more students to study engineering.

Next, we engaged in a professional development program to support current STEM teachers to develop skills in engineering and increase the number of teachers who possess the pedagogical content knowledge to prepare students to be successful in engineering fields.

All three aspects of the STAR program employees engineering design projects to introduce engineering to students and teachers. This integrative model could serve initially as a template to design such programs.
DNA-Structured Linear Actuators
Authors: Zampaglione Kyle , Sabelhaus Andrew P. , Chen Lee-Huang , Agogino Alice M. , Agogino Adrian K.
Published: December 2016

This work presents a series of DNA-structured linear actuators that have high displacements and compact profiles. These actuators operate by twisting and untwisting a double helix that resembles a DNA molecule. Unlike most similarly-motivated twisted string actuators (TSAs), these DNA-structured actuators can have the ability to exert both push and pull forces on a load. Thus, although originally designed for cable-driven robotics, these actuators have the ability to work as part of many different mechatronic systems. Two inherently different actuator designs were investigated, one with straight-line edges (rails) and one with helical rails. Two mathematical models of angular rotation versus linear displacement were developed and simulated, one for each design, and three prototypes were constructed to validate the models. The final prototype was tested for displacement, restorative torque, and pull force characteristics. This last prototype showed a 30.5 cm stroke for a 40.5 cm actuator, or a displacement of 75.3% of its total length.
How Do I Choose? The Influence of Concept Selection Methods on Student Team Decision-Making
Authors: Zheng Xuan , Miller Scarlett R.
Published: December 2016

While research has been conducted to study the use of concept selection methods in design education, few studies have focused on the influence of these methods on individual students’ and teams’ thought processes in grade-dependent class projects. In order to fill this research gap, the current study was designed to compare the influence of two concept selection methods, the Concept Selection Matrix (CSM) and a new adjective assessment method called the Tool for Assessing Semantic Creativity (TASC), through an experimental study in two sections of a first year engineering design class. The results of the study show that while students were equally confident in the concept ratings from the CSM and TASC methods, they reported that they were more likely to select ideas ranked highly in the CSM method. However, subsequent analysis revealed no difference between the common elements in the ideas rated highly by the two methods and the final design ideas produced.
A Combined Plant/Controller Optimization Framework for Hybrid Vehicles With MPG, Emissions and Drivability Considerations
Authors: Febbo Huckleberry , Ersal Tulga , Stein Jeffrey L.
Published: December 2016

The design and control of hybrid-electric vehicle (HEV) powertrains presents an optimization problem to balance the trade-off between multiple objectives, such as fuel economy, driv-ability, and emissions. However, current design methodologies do not simultaneously incorporate all of these three considerations into both the sizing and control layers of the optimization problem. This paper first demonstrates that the trade-offs between these objectives can be non-trivial in the HEV control problem. This motivates the need for a systematic design procedure that can take all three objectives into account. To address this need, the paper describes the development of a new and efficient design framework called the Hybrid-Vehicle Design Tool (HVDT), which adopts a bi-level optimization strategy. Efficiency is achieved by introducing a neural-network-based meta-model to predict the performance of the optimal control strategy obtained using Dynamic Programming (DP). To demonstrate the HVDT, a small HEV is designed for the UDDS and HWFET driving cycles separately. Results show that the optimized design can reduce fuel consumption, improve emissions and improve driv-ability when compared to the nominal design obtained using first principle design methodologies. Additionally, compared to using DP directly in the bi-level optimization, using the meta-model reduces the simulation from 238 to 16 days (93%) and from 132 to 16 days (88%) for the UDDS and HWFET cycles, respectively, with an acceptable compromise in the accuracy of predicting the performance of DP.