bluuebunny/crossref_metadata_2025_split huggingface dataset:
Deriving Metamodels to Relate Machine Learning Quality to Design Repository Characteristics in the Context of Additive Manufacturing
Authors: Williams Glen , Meisel Nicholas A. , Simpson Timothy W. , McComb Christopher 
Published: November 2020

Abstract
The widespread growth of additive manufacturing, a field with a complex informatic “digital thread”, has helped fuel the creation of design repositories, where multiple users can upload distribute, and download a variety of candidate designs for a variety of situations. Additionally, advancements in additive manufacturing process development, design frameworks, and simulation are increasing what is possible to fabricate with AM, further growing the richness of such repositories. Machine learning offers new opportunities to combine these design repository components’ rich geometric data with their associated process and performance data to train predictive models capable of automatically assessing build metrics related to AM part manufacturability. Although design repositories that can be used to train these machine learning constructs are expanding, our understanding of what makes a particular design repository useful as a machine learning training dataset is minimal. In this study we use a metamodel to predict the extent to which individual design repositories can train accurate convolutional neural networks. To facilitate the creation and refinement of this metamodel, we constructed a large artificial design repository, and subsequently split it into sub-repositories. We then analyzed metadata regarding the size, complexity, and diversity of the sub-repositories for use as independent variables predicting accuracy and the required training computational effort for training convolutional neural networks. The networks each predict one of three additive manufacturing build metrics: (1) part mass, (2) support material mass, and (3) build time. Our results suggest that metamodels predicting the convolutional neural network coefficient of determination, as opposed to computational effort, were most accurate. Moreover, the size of a design repository, the average complexity of its constituent designs, and the average and spread of design spatial diversity were the best predictors of convolutional neural network accuracy.
Numerical Investigation of Multi-Mode Guided Wave Focusing in Pipe–Like Structures Using Gradient Index Metamaterial Lens Design
Authors: Danawe Hrishikesh G. , Ozevin Didem , Tol Serife 
Published: November 2020

Abstract
This paper presents a numerical study on multi-mode focusing of guided elastic waves in pipe-like structures over a range of frequencies using a single metamaterial lens design. We explore focusing of the first two longitudinal (L(0,1) and L(0,2)) and the first torsional (T(0,1)) wave modes in steel pipe integrated with a metamaterial lens made of cylindrical steel stubs of varying heights attached to outer surface of the pipe. Proposed metamaterial lens design is based on gradient index (GRIN) theory with hyperbolic secant distribution of refractive index in circumferential direction. Amplification of multi-mode guided wave signals are achieved at focal points of the lens which is verified through numerical simulations. The focusing performance of proposed lens is studied at multiple frequencies for all the three modes and first two focal positions are verified with theoretical predictions of GRIN theory.
Towards Prospective Sustainability Life Cycle Assessment
Authors: Clarke-Sather Abigail R. , Mamun Saleh , Nolan Daniel , Schoff Patrick , Aro Matthew , Ulrich Bridget 
Published: November 2020

Abstract
Life cycle assessment (LCA) is a well-established tool for measuring environmental effects of existing technology. While the most recent LCA research has focused on environmental impacts, in particular on the effects of climate change, there is growing interest in how LCA can be used prospectively. A 2019 workshop in Duluth, Minnesota sought to define the needs and priorities of prospective life cycle assessment from a perspective that considers diverse viewpoints. In that workshop, participants outlined frameworks for how sustainability impacts might figure into a prospective LCA tool focused on assessing technologies currently under development. Those frameworks included social and economic impacts, which were characterized alongside environmental impacts, with the goal of predicting potential impacts and developing recommendations for improving technologies. Cultural perspective, in particular the roots of the German circular economy, was explored and held up as a reminder that different communities are influenced by different sustainability concerns, leading to diverse policy and cultural prerogatives. The purpose of this paper is to catalyze conversation about how to frame methodologies of existing LCA tools that could be used in a prospective sustainability context.
Statistical Analysis of Tensile Tests Performed on 316L Specimens Manufactured by Directed Energy Deposition
Authors: Iliopoulos A. P. , Thomas J. P. , Steuben J. C. , Saunders R. , Michopoulos J. G. , Bagchi A. , Birnbaum A. J. 
Published: November 2020

Abstract
The present work reports on the results of tension tests conducted on 316L stainless steel specimens extracted out of thin-walled boxes produced by powder jet additive manufacturing. The specimens were minimally processed to study the potential effects of the manufacturing process on the apparent mechanical properties rather than identifying the properties of the resulting bulk material. Statistical analysis is performed and presented in an attempt to identify correlations between the manufacturing parameters and the apparent mechanical properties. The mechanical properties of interest are Young’s modulus, the yield stress (measured at the 0.2% level), the ultimate stress, and the failure strain.
A Blockchain-Based Traceability System for Waste Management in Smart Cities
Authors: Gopalakrishnan Praveen Kumare , Hall John , Behdad Sara 
Published: November 2020

Abstract
Waste tracking is becoming an important concern for developed countries as well as developing regions, where municipalities aim to assure proper waste management considering environmental and economic objectives. Waste tracking is important not only for a transparent reporting system compatible with environmental regulations but also for economically viable waste collection and recovery solutions. In this paper, a waste tracking system based on the blockchain technology is introduced where different entities involved in the system will be able to retrieve required data from the platform and decide on their level of contributions. The conventional technologies do not provide a sufficient level of transparency and coordination among different entities. With the introduction of blockchain as a tamper-proof technology, municipalities can enhance the efficiency of their waste management efforts. The proposed blockchain technology can connect proper stakeholders towards collaboration and sharing information. The concept of a smart contract for waste management is discussed and further, a decision-making framework is developed to guide users of the system select proper services available to them, depending on the level of data sharing, cost, reliability, and the security level that they expect from the system.
Analyzing the Characteristics of Cognitive-Assistant-Facilitated Ideation Groups
Authors: Maier Torsten , Soria Zurita Nicolas F. , Starkey Elizabeth , Spillane Daniel , Menold Jessica , McComb Christopher 
Published: November 2020

Abstract
The rapid digitalization of the world has affected engineering and design in a variety of ways, including the introduction of new computer-aided ideation tools. Cognitive assistants (CA), an increasingly common digital technology, use natural-language processing and artificial intelligence to provide computational support. Because cognitive assistants are capable of emulating humans in some tasks, they may be suited to support brainstorming activities when trained coaches or facilitators are not available. This study compared co-located brainstorming groups facilitated by human facilitators and a CA facilitator. Interaction Dynamics Notation was used to code the sessions, and Hidden Markov Models were used to define the group’s states. We found that human facilitation was associated with blocks/interruptions and responding to those while cognitive assistant facilitation was associated with deviations and silence. Human facilitation was also found to produce a more equal distribution of speaking time.
Free Vibration of Bistable Clamped-Clamped Beams: A Preliminary Study
Authors: Song Xiaolei , Liu Haijun 
Published: November 2020

Abstract
We present a preliminary study on bistable clamped-clamped beams both analytically and experimentally relating the linear post-buckling vibrations to the generated sound. In the analytical study, closed-form natural frequencies and mode shapes around the first buckled configuration are derived from an eigenvalue problem. It is found that as the static deflection of the buckled beam increases, the natural frequencies of the anti-symmetric vibrational modes stay constant, while those of the symmetric vibrational modes increase asymptotically. In the experimental study, a bistable clamped-clamped buckled beam made of steel is switched quasi-statically by hand between the two stable configurations. The generated sound is measured by a microphone and analyzed in both temporal and frequency domains, which agrees well with the analytical results. This work lays the foundation for using bistable beams in a variety of applications such as actuators, resonators, energy harvesters, and vibration reduction.
A Simple and Effective Methodology to Perform Multi-Objective Bayesian Optimization: An Application in the Design of Sandwich Composite Armors for Blast Mitigation
Authors: Valladares Homero , Tovar Andres 
Published: November 2020

Abstract
Bayesian optimization is a versatile numerical method to solve global optimization problems of high complexity at a reduced computational cost. The efficiency of Bayesian optimization relies on two key elements: a surrogate model and an acquisition function. The surrogate model is generated on a Gaussian process statistical framework and provides probabilistic information of the prediction. The acquisition function, which guides the optimization, uses the surrogate probabilistic information to balance the exploration and the exploitation of the design space. In the case of multi-objective problems, current implementations use acquisition functions such as the multi-objective expected improvement (MEI). The evaluation of MEI requires a surrogate model for each objective function. In order to expand the Pareto front, such implementations perform a multi-variate integral over an intricate hypervolume, which require high computational cost. The objective of this work is to introduce an efficient multi-objective Bayesian optimization method that avoids the need for multi-variate integration. The proposed approach employs the working principle of multi-objective traditional methods, e.g., weighted sum and min-max methods, which transform the multi-objective problem into a single-objective problem through a functional mapping of the objective functions. Since only one surrogate is trained, this approach has a low computational cost. The effectiveness of the proposed approach is demonstrated with the solution of four problems: (1) an unconstrained version of the Binh and Korn test problem (convex Pareto front), (2) the Fonseca and Fleming test problem (non-convex Pareto front), (3) a three-objective test problem and (4) the design optimization of a sandwich composite armor for blast mitigation. The optimization algorithm is implemented in MATLAB and the finite element simulations are performed in the explicit, nonlinear finite element analysis code LS-DYNA. The results are comparable (or superior) to the results of the MEI acquisition function.
Agent Based Resilient Transportation Infrastructure With Surrogate Adaptive Networks
Authors: Apostol Andre A. , Turner Cameron J. 
Published: November 2020

Abstract
Connected autonomous intelligent agents (AIA) can improve intersection performance and resilience for the transportation infrastructure. An agent is an autonomous decision maker whose decision making is determined internally but may be altered by interactions with the environment or with other agents. Implementing agent-based modeling techniques to advance communication for more appropriate decision making can benefit autonomous vehicle technology.
This research examines vehicle to vehicle (V2V), vehicle to infrastructure (V2I), and infrastructure to infrastructure (I2I) communication strategies that use gathered data to ensure these agents make appropriate decisions under operational circumstances. These vehicles and signals are modeled to adapt to the common traffic flow of the intersection to ultimately find an traffic flow that will minimizes average vehicle transit time to improve intersection efficiency. By considering each light and vehicle as an agent and providing for communication between agents, additional decision-making data can be transmitted. Improving agent based I2I communication and decision making will provide performance benefits to traffic flow capacities.
Design of an Ankle Rehab Robot With a Compliant Parallel Kinematic Mechanism
Authors: Jalgaonkar Nishant , Kim Adam , Awtar Shorya 
Published: November 2020

Abstract
In this paper, we present the design of a novel ankle rehabilitation robot (ARR), called the Flex-ARR, that employs a compliant parallel kinematic mechanism (PKM) with decoupled degrees of freedom. The Flex-ARR is designed to collocate the biological center of rotation of the ankle with that of the robot’s center of rotation to allow natural ankle motion. While multiple ARR designs have been developed in research labs and some are commercially available, their clinical adoption has been limited because they do not emulate the natural motion of the ankle. The Flex-ARR leverages a unique PKM design that uses compliance to absorb minor misalignments between the center of rotation of the ankle and the robot, thereby allowing natural ankle motion. Also, because of its unique design, the PKM inherently accommodates variations in user foot sizes with minimal adjustments. The Flex-ARR is designed to provide multiple training modes that allow for both rehabilitation and assessment modalities. This paper provides a review of the literature to identify the key factors that have limited the clinical adoption of existing ARRs. Based on this, functional requirements and design specifications for an optimal ARR are defined. This is then used to develop a design strategy, followed by conceptual and detailed design.
Structure, Process, and Material Influences for 3D Printed Lattices Designed With Mixed Unit Cells
Authors: Briguiet Gabriel , Egan Paul F. 
Published: November 2020

Abstract
Emerging 3D printing technologies are enabling the design and fabrication of novel architected structures with advantageous mechanical responses. Designing complex structures, such as lattices, with a targeted response is challenging because build materials, fabrication process, and topological design have unique influences on the structure’s mechanical response. Changing any factor may have unanticipated consequences, even for simpler lattice structures. Here, we conduct mechanical compression experiments to investigate varied lattice design, fabrication, and material combinations using stereolithography printing with a biocompatible polymer. Mechanical testing demonstrates that a higher ultraviolet curing time increases elastic modulus. Material testing demonstrated that anisotropy does not strongly influence lattice mechanics. Designs were altered by comparing homogenous lattices of single unit cell types and heterogeneous lattices that combine two types of unit cells. Unit cells for heterogeneous structures include a Cube design for a high elastic modulus and Cross design for improved shear response. Mechanical testing of three heterogeneous layouts demonstrated how unit cell organization influences mechanical outcomes, therefore enabling the tuning of an elastic modulus that surpasses the law of averages designed for application-dependent mechanical needs. These findings provide a foundation for linking design, process, and material for engineering 3D printed structures with preferred properties, while also facilitating new directions in design automation and optimization.
Is Verifying Frequently an Optimal Strategy? A Belief-Based Model of Verification
Authors: Kulkarni Aditya U. , Salado Alejandro , Wernz Christian , Xu Peng 
Published: November 2020

Abstract
Verification activities increase an engineering team’s confidence in its system design meeting system requirements, which in turn are derived from stakeholder needs. Conventional wisdom suggests that the system design should be verified frequently to minimize the cost of rework as the system design matures. However, this strategy is based more on experience of engineers than on a theoretical foundation. In this paper, we develop a belief-based model of verification of system design, using a single system requirement as an abstraction, to determine the conditions under which it is cost effective for an organization to verify frequently. We study the model for a broad set of growth rates in verification setup and rework costs. Our results show that verifying a system design frequently is not always an optimal verification strategy. Instead, it is only an optimal strategy when the costs of reworking a faulty design increase at a certain rate as the design matures.
How Engineering Design Students’ Psychological Safety Impacts Team Concept Generation and Screening Practices
Authors: Cole Courtney , Marhefka Jacqueline , Jablokow Kathryn , Mohammed Susan , Ritter Sarah , Miller Scarlett 
Published: November 2020

Abstract
Psychological safety has been shown to be a consistent, generalizable, and multilevel predictor of outcomes in performance and learning across fields. While work in this field has suggested that psychological safety can impact the creative process, particularly in the generation of ideas and in the discussions surrounding idea development, there has been limited investigations of psychological safety in the engineering domain. Without this knowledge we do not know when fostering psychological safety in a team environment is most important. This study provides the first attempt at answering this question through an empirical study with 53 engineering design student teams over the course of a 4- and 8-week design project. Specifically, we sought to identify the role of psychological safety on the number and quality (judged by goodness) of ideas generated. In addition, we explored the role of psychological safety on ownership bias and goodness in the concept screening process. The results of the study identified that while psychological safety was not related to the number of ideas a team developed, it was positively related to the quality (goodness) of the ideas developed. In addition, while no relationship was found between psychological safety and ownership bias during concept screening, the results showed that teams with high psychological safety selected a higher percentage of their team members ideas.
Kinematic Analysis of a 5-DOF Positioner for Precision Additive Manufacturing
Authors: Saadatzi Mohammad Hossein , Popa Dan O. 
Published: November 2020

Abstract
Additive manufacturing, as a viable industrial-production technology, requires multi-DOF positioning with high precision and repeatability for either the printer head, or the part being printed. In this paper we present a novel methodology to analyze the error propagation informing the design of a high-precision robotic 5-DOF positioner for applications in additive manufacturing. We designed our positioner through serial attachment of linear and rotational stages by comparing the precision of three different kinematic arrangements of stages. Within order to minimize positioning errors in Cartesian space, the kinematic sensitivity of the mechanisms end-effector relative to the maximum expected error of each joint was computed, and the kinematic configuration with smallest 6D positioning error at the end-effector was selected. The methodology employed in this paper for the error propagation analysis of serial kinematic chains has a great level of generality and can facilitate the design and optimization of a wide-class of multi-DOF positioners.
“You Cannot Test It Before It Is Verified. When It Is Verified You Cannot Change It”: Prototyping Challenges in Industry
Authors: Arndt Hansen Camilla , Özkil Ali Gürcan 
Published: November 2020

Abstract
In a new product development process, multiple decisions must be made regarding the selection of concepts to pursue and how a product should be constructed. In many companies, these decisions are informed by the results from prototype tests.
In this research, we investigate which challenges practitioners experience when they prototype, the opportunities they see for overcoming such challenges, and the current trends and developments that affect prototyping in industry.
Data were collected from eight practitioners from the Danish industry through a mixed methods research approach consisting of a small survey and focus group discussions. A qualitative content analysis resulted in the identification of 23 prototyping challenges, 15 opportunities for future improvement, and 5 prototyping trends.
Practitioners feel challenged when they have to make decisions from a few prototypes with limited properties and they seek guidance on the proper use of prototyping strategies during the development process. Additionally, challenges arise due to gaps between virtual and physical prototyping, regulations in the medical industry, and unexpected misinterpretations of prototypes.
These results contribute to our understanding of prototyping in industry and helps us identify areas where the research community can focus their future efforts for the benefit of practitioners.
Analysis of the Rigid Motion of a Developable Conical Mechanism
Authors: Woodland McKell , Hsiung Michelle , Matheson Erin L. , Safsten C. Alex , Greenwood Jacob , Halverson Denise M. , Howell Larry L. 
Published: November 2020

Abstract
We demonstrate analytically that it is possible to construct a developable mechanism on a cone that has rigid motion. We solve for the paths of rigid motion and analyze the properties of this motion. In particular, we provide an analytical method for predicting the behavior of the mechanism with respect to the conical surface. Moreover, we observe that the conical developable mechanisms specified in this paper have motion paths that necessarily contain bifurcation points which lead to an unbounded array of motion paths in the parameterization plane.