bluuebunny/crossref_metadata_2025_split huggingface dataset:
Stochastic Kriging for Crashworthiness Optimization Accounting for Simulation Noise
Authors: Ahmadisoleymani Seyed Saeed , Missoum Samy 
Published: November 2020

Abstract
Vehicle crash simulations are notoriously costly and noisy. When performing crashworthiness optimization, it is therefore important to include available information to quantify the noise in the optimization. For this purpose, a stochastic kriging can be used to account for the uncertainty due to the simulation noise. It is done through the addition of a non-stationary stochastic process to the deterministic kriging formulation. This stochastic kriging, which can also be used to include the effect of random non-controllable parameters, can then be used for surrogate-based optimization. In this work, a stochastic kriging-based optimization algorithm is proposed with an infill criterion referred to as the Augmented Expected Improvement, which, unlike its deterministic counterpart the Expect Improvement, accounts for the presence of irreducible aleatory variance due to noise. One of the key novelty of the proposed algorithm stems from the approximation of the aleatory variance and its update during the optimization. The proposed approach is applied to the optimization of two problems including an analytical function and a crashwor-thiness problem where the components of an occupant restraint system of a vehicle are optimized.
Additive Manufacturing Adaptiveness Analysis Using Fuzzy Bayesian Network
Authors: Jing Liting , Ma Junfeng 
Published: November 2020

Abstract
Additive manufacturing (AM) is a revolutionary manufacturing technology that can produce products in a layer by layer manner. Because of its significant merits in complex geometry and fast fabrication, AM has received worldwide attentions from both industries and academia. Although extensive studies have been conducted on the aspects of process design, prototyping, quality control and reliability, the study of adopting AM in the application is still not fully investigated, which motives this study. In order to close this gap, this study proposes a fuzzy Bayesian Network based approach to discover the applicability of AM. Twelve features of AM applicability obtained from existing literature have been considered in the analysis; fuzzy linguistic description was used to capture the users’ perception; fuzzy Bayesian Network based causation model was developed to study the AM’s adaptiveness. The jet engine blade case study was applied to demonstrate the applicability of the proposed approach. The results showed that fuzzy Bayesian Network based causation approach is able to provide the robust and reliable results of applicability analysis and could also be extended to other risk assessment related design decision making process.
A Neighborhood-Based Neural Network for Melt Pool Prediction and Control
Authors: Zhang Yaqi , Shapiro Vadim , Witherell Paul 
Published: November 2020

Abstract
One of the most prevalent additive manufacturing processes, the powder bed fusion process, is driven by a moving heat source that melts metals to build a part. This moving heat source, and the subsequent formation and moving of a melt pool, plays an important role in determining both the geometric and mechanical properties of the printed components. The ability to control the melt pool during the build process is a sought after mechanism for improving quality control and optimizing manufacturing parameters. For this reason, efficient models that can predict melt pool size based on the process input (i.e., laser power, scan speed, spot size and scan path) offer a path to improved process control.
Towards improved process control, a data-driven melt pool prediction model is built with a neighborhood-based neural network and trained using experimental data from the National Institute of Standards and Technology (NIST). The model considers the influence of both manufacturing parameters and laser scan paths. The scan path information is encoded using two novel neighborhood features of the neural network through locality. The neural network is used to generate a surrogate model, and we demonstrate how the performance of the resulting surrogate model can be further improved by using several ensemble methods. We then demonstrate how the trained surrogate model can be used as a forward solver for developing novel laser power design algorithms. The resulting laser power plan is designed to keep melt pool size as constant as possible for any given scan pattern. The algorithm is implemented and validated with numerical experiments.
A Jupyter Notebook Environment for Multibody Dynamics
Authors: Gaut Aaron , Cameron Jonathan , Jain Abhinandan 
Published: November 2020

Abstract
DARTS is a rigid/flexible multibody dynamics toolkit for the modeling and simulation of aerospace and robotic vehicles for engineering applications. In this paper we describe an on-line, browser-based environment using Jupyter notebooks to support training needs for the DARTS software. The suite of curated tutorial notebooks is organized into different topic areas, and into multiple themes within each topic area. The notebooks within a theme use a progression of examples for users to expand their understanding of the software. The topic areas include one on the DARTS multibody dynamics software and another one on the theory underlying the multibody dynamics formulation. We also describe a number of Jupyter extensions that were used — and some developed in house — to enhance the notebook interface for use with the dynamics simulation software. One significant extension we implemented allows the embedding of live 3D visualizations within simulation notebooks.
Social Life Cycle Assessment of Computer-Aided Design Tools
Authors: Walters Justin , Mirkouei Amin 
Published: November 2020

Abstract
Social life cycle assessment (SLCA) is a newly developed concept that is used to assess the potential positive and negative social impacts of products and services. However, the existing approaches have not focused on improving social aspects in the execution of computer-aided design (CAD) software. The Idaho National Laboratory’s Materials and Fuels Complex is currently using Creo Parametric CAD software to design all experimental equipment. The purpose of this study is to conduct a socio-environmental life cycle assessment on the existing design procedures and present the findings and possible solutions to upper management. A comparison was performed to highlight the differences between the procedures. To determine the social effects, the Social Hotspots Database in OpenLCA was used in connection with a low, medium, high, and very high scale, which was used to quantify specific social categories. The social categories developed for this study include communication, rework time, time spent investigating non-normal methods of task completion, excessive working time, and social impacts of electricity usage. The environmental aspects were calculated by gathering data on carbon dioxide emissions per computer, utilizing the Creo software. The results produced through the calculations show that in all three areas of interest, the proposed approach decreased time and carbon dioxide emissions as well as an increase in employee satisfaction. Due to the virtually nonexistent SLCA studies in relation to the use of CAD software, it is anticipated that this study will provide a starting point for a more in-depth analysis of engineering departments.
Fostering Function-Sharing Using Bioinspired Product Architecture
Authors: Bhasin Devesh , McAdams Daniel A. 
Published: November 2020

Abstract
In this work, we deduce principles of bioinspired product architectures to leverage biological function-sharing in engineering design. Function-sharing allows multiple functions to be performed by a single structure and can lead to improvements in cost, weight and other performance characteristics. Billions of years of evolution has led to the emergence of function-sharing adaptations in biological systems. However, the current practice of bioinspired function-sharing is largely limited to the solution-driven mimicry of biological structures. In order to effectively leverage biological function-sharing in engineering design, we model and analyze the product architectures of five generalized case studies from the animal kingdom. Further, we create a categorization framework to explore patterns in the function-sharing scenarios associated with biological product architectures. Our results indicate the existence of four types of modules in the biological systems from the animal kingdom. We use the classification framework to deduce four guidelines for the bioinspiration of product architectures. The deduced guidelines can allow engineers to identify and implement novel function-sharing scenarios in early stages of product design. The application of the guidelines has been demonstrated by using a case study.
2:1 MUX and OR Logic Functions Using Triple Partial Electrodes: Toward Cascadable MEMS Logic Devices
Authors: Tella Sherif A. , Younis Mohammad I. 
Published: November 2020

Abstract
In the era of IoT and smarter sensors and actuators, MEMS resonators are actively being explored for ultra-low-power computing devices due to their simplicity and potential toward energy-efficient computing machines. However, the realization of complex logic functions through the cascadability of MEMS resonator logic devices has introduced new challenges that require both the logic input and logic output signals to be based on AC signals at the same frequency. Toward these challenges, this study demonstrates 2:1 MUX function and OR gate with improved energy efficiency based on activation and deactivation of the third vibrational mode of an arch microbeam resonator with a pair of three partial electrodes.
Best Fits and Dark Horses: Can Design Teams Tell the Difference?
Authors: Henderson Daniel , Booth Thomas , Jablokow Kathryn , Sonalkar Neeraj 
Published: November 2020

Abstract
Design teams are often asked to produce solutions of a certain type in response to design challenges. Depending on the circumstances, they may be tasked with generating a solution that clearly follows the given specifications and constraints of a problem (i.e., a Best Fit solution), or they may be encouraged to provide a higher risk solution that challenges those constraints, but offers other potential rewards (i.e., a Dark Horse solution). In the current research, we investigate: what happens when design teams are asked to generate solutions of both types at the same time? How does this request for dual and conflicting modes of thinking impact a team’s design solutions? In addition, as concept generation proceeds, are design teams able to discern which solution fits best in each category? Rarely, in design research, do we prompt design teams for “normal” designs or ask them to think about both types of solutions (boundary preserving and boundary challenging) at the same time. This leaves us with the additional question: can design teams tell the difference between Best Fit solutions and Dark Horse solutions?
In this paper, we present the results of an exploratory study with 17 design teams from five different organizations. Each team was asked to generate both a Best Fit solution and a Dark Horse solution in response to the same design prompt. We analyzed these solutions using rubrics based on familiar design metrics (feasibility, usefulness, and novelty) to investigate their characteristics. Our assumption was that teams’ Dark Horse solutions would be more novel, less feasible, but equally useful when compared with their Best Fit solutions. Our analysis revealed statistically significant results showing that teams generally produced Best Fit solutions that were more useful (met client needs) than Dark Horse solutions, and Dark Horse solutions that were more novel than Best Fit solutions. When looking at each team individually, however, we found that Dark Horse concepts were not always more novel than Best Fit concepts for every team, despite the general trend in that direction. Some teams created equally novel Best Fit and Dark Horse solutions, and a few teams generated Best Fit solutions that were more novel than their Dark Horse solutions. In terms of feasibility, Best Fit and Dark Horse solutions did not show significant differences. These findings have implications for both design educators and design practitioners as they frame design prompts and tasks for their teams of interest.
A Weighted Network Modeling Approach for Analyzing Product Competition
Authors: Cui Yaxin , Ahmed Faez , Sha Zhenghui , Wang Lijun , Fu Yan , Chen Wei 
Published: November 2020

Abstract
Statistical network models allow us to study the co-evolution between the products and the social aspects of a market system, by modeling these components and their interactions as graphs. In this paper, we study competition between different car models using network theory, with a focus on how product attributes (like fuel economy and price) affect which cars are considered together and which cars are finally bought by customers. Unlike past work, where most systems have been studied with the assumption that relationships between competitors are binary (i.e., whether a relationship exists or not), we allow relationships to take strengths (i.e., how strong a relationship is). Specifically, we use valued Exponential Random Graph Models and show that our approach provides a significant improvement over the baselines in predicting product co-considerations as well as in the validation of market share. This is also the first attempt to study aggregated purchase preference and car competition using valued directed networks.
Environmental and Economic Impacts of Nitrogen Trifluoride at an Idaho Semiconductor Facility
Authors: Thompson Matthew A. , Mohajeri Amir , Mirkouei Amin 
Published: November 2020

Abstract
Nitrogen Trifluoride (NF3) is an inorganic compound widely used in the electronics industry for manufacturing various products, such as semiconductors, solar panels, and touch screens. However, NF3 emissions that accumulate in the atmosphere have 17,200 times the global warming potentials of CO2 over a 100-year time horizon. The abatement efficiency for NF3 is high, although some amount of NOx is generated. This study aims to provide economic and environmental impact assessments on the use of NF3. Life cycle assessment method is applied for evaluating environmental aspects. Additionally, a real case study for the Idaho semiconductor facility is used to assess the method and demonstrate the application of this study. The results show that slight reductions in abatement efficiency have dramatic impacts on mitigating greenhouse gas (GHG) emissions. Even small unplanned releases of NF3, either through mechanical failure or human error, have significant environmental impacts, and every reasonable effort should be taken to avoid such incidents. The results also indicate that the cost of abatement is the primary driver of economic impact. It is found that the considerable distance from the chemical plant in Arizona to the point of use in Idaho contributed a minor portion of GHGs associated with the use of NF3. It is also found that contaminants (e.g., SF6 and CF4) within the cylinder potentially have more substantial GHG impacts than the actual NF3, owing in part to the relative ease with which NF3 is destroyed through abatement.
Organic Piezoresistive Pressure Sensitive Robotic Skin for Physical Human-Robot Interaction
Authors: Wei Danming , Zhang Ruoshi , Saadatzi Mohammad N. , Olowo Olalekan O. , Popa Dan O. 
Published: November 2020

Abstract
Pressure sensitive robotic skins have long been investigated for applications to physical human-robot interaction (pHRI). Numerous challenges related to fabrication, sensitivity, density, and reliability remain to be addressed under various environmental and use conditions. In our previous studies, we designed novel strain gauge sensor structures for robotic skin arrays. We coated these star-shaped designs with an organic polymer piezoresistive material, Poly (3, 4-ethylenedioxythiophene)-ploy(styrenesulfonate) or PEDOT: PSS and integrated sensor arrays into elastomer robotic skins. In this paper, we describe a dry etching photolithographic method to create a stable uniform sensor layer of PEDOT:PSS onto star-shaped sensors and a lamination process for creating double-sided robotic skins that can be used with temperature compensation. An integrated circuit and load testing apparatus was designed for testing the resulting robotic skin pressure performance. Experiments were conducted to measure the loading performance of the resulting sensor prototypes and results indicate that over 80% sensor yields are possible with this fabrication process.
Scalable Objective-Driven Batch Sampling in Simulation-Based Design for Models With Heteroscedastic Noise
Authors: van Beek Anton , Farooq Ghumman Umar , Munshi Joydeep , Tao Siyu , Chien TeYu , Balasubramanian Ganesh , Plumlee Matthew , Apley Daniel , Chen Wei 
Published: November 2020

Abstract
Objective-driven adaptive sampling is a widely used tool for the optimization of deterministic black-box functions. However, the optimization of stochastic simulation models as found in the engineering, biological, and social sciences is still an elusive task. In this work, we propose a scalable adaptive batch sampling scheme for the optimization of stochastic simulation models with input-dependent noise. The developed algorithm has two primary advantages: (i) by recommending sampling batches, the designer can benefit from parallel computing capabilities, and (ii) by replicating of previously observed sampling locations the method can be scaled to higher-dimensional and more noisy functions. Replication improves numerical tractability as the computational cost of Bayesian optimization methods is known to grow cubicly with the number of unique sampling locations. Deciding when to replicate and when to explore depends on what alternative minimizes the posterior prediction accuracy at and around the spatial locations expected to contain the global optimum. The algorithm explores a new sampling location to reduce the interpolation uncertainty and replicates to improve the accuracy of the mean prediction at a single sampling location. Through the application of the proposed sampling scheme to two numerical test functions and one real engineering problem, we show that we can reliably and efficiently find the global optimum of stochastic simulation models with input-dependent noise.
A Formal Representation of Conjugate Verbs in Function Modeling
Authors: Chowdhury Ahmed , Venkatanarasimhan Lakshmi N. A. , Sen Chiradeep 
Published: November 2020

Abstract
Many modern and innovative design problems require multi-modal, reconfigurable solutions. Function modeling is a common tool used to explore solutions in early stages of mechanical engineering design. Currently, function structure representations do not support the modeling of formally-defined reconfigurable function models. There is a well-established need in function modeling to dynamically capture the effects of state change of a flow property on the operating mode of the system. This paper presents a formal representation to capture the duality of specific functions, and illustrates it through three verbs that shift from one mode of operation to its logical and topological opposite, based on the existence of, or the value of a signal from, an input flow. Additionally, an approach to extend these functions to function features, in order to support physics-based reasoning on the interactions between flows is also presented. Through the example of a system-level model of a geothermal heat pump operating in its heating mode, the representation demonstrates the ability to support causal reasoning on functional modes of systems, provides quantitative reasoning on the efficiency of those modes, and illustrates the modeling efficacy of the extended representation.
Investigating the Coupled Effects Between Rotor-Blade Aeroelasticity and Tip Vortex Stability
Authors: Rodriguez Steven N. , Iliopoulos Athanasios P. , Michopoulos John G. , Jaworski Justin W. 
Published: November 2020

Abstract
The relationship between rotor-blade aeroelasticity and tip-vortex stability is investigated numerically. An aeroelastic framework based on the free-vortex wake and finite element methods is employed to model a subscaled helicopter rotor in hover and forward-tilted conditions. A linear eigenvalue stability analysis is performed on tip vortices to associate the coupled impact of aeroelastic effects and vortex evolution. Prior numerical investigations have shown that highly flexible wind turbine rotor-blades have the potential to decrease levels of the instability of tip vortices. The present work focuses on testing these findings against a subscaled rotor within the range of helicopter operational rotation frequencies. The presented work aims to develop further insight into rotor-wake interactions and blade-vortex interaction to explore the mitigation of adverse rotorcraft operational conditions, such as their effect on aerodynamic-induced airframe vibrations and the associated life-cycle fatigue performance.
Incorporating Contextual Factors Into a Design Process: An Analysis of Engineering for Global Development Literature
Authors: Burleson Grace , Sienko Kathleen H. , Toyama Kentaro 
Published: November 2020

Abstract
It is frequently suggested that designing artifacts requires a deep understanding of context. We analyzed 186 papers from three specific literature searches involving a design process for global development applications. Each paper was reviewed and coded for (1) whether or not the study teams performed some level of contextual investigation, (2) the methods and/or approaches for understanding the artifact’s context, and (3) use of open-ended analysis or framework-based analysis for qualitative contextual investigation. In total, 159 papers (85%) described some level of contextual investigation with 46 of these describing performance-focused field tests in the artifact’s context of use and 113 papers using qualitative methods for contextual investigation. 18 papers used a framework, such as a cited questionnaire, for analyzing their contextual data, overwhelmingly during needs assessment and validation stages of design. We find that study teams of every paper motivated their design with contextual information, but there was great diversity in how context was investigated, analyzed, and described. Few linked their design decisions to specific contextual factors. We recommend that researchers include detailed description of methods and contextual factors considered during their design process to improve the transferability and repeatability of design approaches.