bluuebunny/crossref_metadata_2025_split huggingface dataset:
Developing Requirements for a Manufacturing Training Platform: A Three-Pronged Approach
Authors: Renu Rahul Sharan , Righter James , Lytch Jada 
Published: November 2022

Abstract
The study reported in this paper is focused on training needs for manufacturing assembly line workers. Specifically, the objective is to investigate desired worker characteristics, upskilling opportunities, and need for a training platform targeted at assembly line workers in South Carolina. The study is performed in three parts: 1) a survey of South Carolina manufacturing professionals, 2) an analysis of manufacturing job ads, and 3) an analysis of publicly available data from O*NET®.
The survey consisted of 27 questions and primarily focused on learning objectives within the cognitive and psychomotor domains of Bloom’s taxonomy. The survey tool was also designed to determine industry perceptions of virtual training for manual assembly operations and perceptions of its potential benefits and limitations. Specific survey questions address the desired complexity of training objectives within the cognitive and psychomotor domains. The results from this survey will later be used provide a framework for customized, targeted training modules for assembly workers.
Job postings for assembly line workers were analyzed using Natural Language Processing techniques. Specifically, postings were analyzed to relate action verbs to the distinct domains of Bloom’s taxonomy of learning objectives. In addition, a frequency analysis of the verbs provided insight into skills that are in demand.
Preliminary survey results were compared to results from the job ads analysis and trends from O*NET® for alignment or discrepancies between announced characteristics and desired skills. This comparison provides insights into training requirements and upskilling opportunities for the manufacturing workforce. Further possibilities include targeted semi-structured interviews to explore knowledge gaps, pursue insights, and refine results.
Bistability Condition for Electrostatically Actuated Initially Curved Micro-Beams in the Presence of Curved Electrodes
Authors: Medina Lior 
Published: November 2022

Abstract
Following increasing interest in electrostatic actuation of curved beams via curved electrodes. A rigorous limit point analysis is carried out to view how the beam reacts as a function of its geometry, as well as that of the electrode. The culmination of the study is in a bistability condition that describes what geometry both beam and electrode must have in order for bistability to be present. The study is based on a single-degree-of-freedom (DOF) reduced order (RO) model of a curved beam, derived from Galerkin’s decomposition. The extraction of a condition is based on the existence of a vanishing discriminant of a cubic equation, which formed a boundary in the parameters space of both beam and electrode geometries. The boundary describes a shift in behaviour, from mono- to bistability. Such a model and subsequent analysis have been used before for the study of curved beams, especially when it is on the verge of bistability, with high degree of fidelity. The condition shows that while actuation voltages will increase or decrease as a function of electrode curvature, as well as operational range, the curvature of an electrode plays a key role in determining the behaviour of the beam. Such results can serve researchers and engineers alike in designing curved beam-electrode configurations for usage in future studies, thus promoting their usage in micro-electro-mechanical (MEMS) based applications.
Design of a Crawling Robot Based on Origami Structures
Authors: Yan Shuiqing , Wei Qi , Li Jiake , Ma Zhe , Zhou Xiang 
Published: November 2022

Abstract
A crawling robot with Kresling folding tubes as the motion joint is designed in this paper. The crawling robot is composed of a motion module and two load modules where the motion module is composed of four Kresling folding tubes and one corrugated tube. Based on the designed overall structure of the crawling robot, the corresponding linear and steering motion mechanisms are designed. The retraction and bending motions of motion module are analyzed and studied by the simulation in ABAQUS. Based on the designed overall structure of the crawling robot and the Kresling folding tube configuration, the mechanical structure of the crawling robot driven by the servo motor is designed. The robot prototype is made by 3D printing and metal machining. According to the test of the robot motion performance, the linear motion speed of the crawling robot can reach 33.7mm/s, and the steering speed can reach 5.2°/s. The designed crawling robot adopts modular design with a simple structure and driving method, and has fast movement speed. The design based on origami structure makes the robot smaller in size and flexible in deformation, and has potential applications ranging from pipeline detection to military reconnaissance, post-disaster rescue and ruin exploration.
Additive Manufacturability Analysis of Multiscale Aperiodic Structures: a Statistical Mechanics Approach
Authors: Trautschold Olivia , Dong Andy 
Published: November 2022

Abstract
Discrete, rule-based approaches to design for additive manufacturing can be inadequate or prohibitively complex for predicting the geometric fidelity of multiscale aperiodic structures that lack a representative volume element. In this paper, we introduce heuristics based upon statistical mechanics to assist in additive manufacturability analysis. The heuristics are derived from the following topological properties of complex network representations of the geometry of multiscale aperiodic structures: giant component size, node degree, average shortest path length, and robustness. We apply these heuristics to the design and additive manufacture of crumpled thin sheets, selected for their multiscale aperiodicity and reproducible statistical properties. Manufacturability is assessed by comparing these topo-logical network properties between a computationally designed crumpled structure and a microCT scan of an additively manufactured crumpled structure. Dimensionality reduction is performed on each network representation to balance spatial noise reduction and information loss. Consistent with expectations, the heuristics and topological properties show that crumpled structures at higher compaction levels are more readily manufactured. The results show the significance of statistical mechanics in providing insight into the additive manufacturability of multiscale aperiodic structures. We conclude by discussing the generality of this approach for alternative geometries and provide designers with a framework for interpreting manufacturability from a statistical mechanics perspective.
Multi-Input Shaping for a Multi-Mode Vibratory Structure Using Deadbeat Control Theory
Authors: Sinha Alok 
Published: November 2022

Abstract
A general approach of the multi-input shaping for an underdamped flexible structure is developed using the deadbeat control theory, which states that a discrete-time system can be made to reach the desired position in a minimum time by placing all the poles at the origin of z-plane. It is shown that this theory can also be used to minimize sensitivities to uncertainties in natural frequencies and modal damping ratios. Numerical results are presented for a spring mass model, and a finite element model of a tetrahedral truss structure. These examples deal with cases of number of inputs being equal to number of modes, and also non-integer ratio of numbers of states and inputs.
A Sequential Decision Making Approach to Learn Process Parameters by Conducting Experiments on Sacrificial Objects
Authors: Yoon Yeo Jung , Gupta Satyandra K. 
Published: November 2022

Abstract
Learning the right process parameters is essential to efficiently and safely execute tasks in automated manufacturing applications. When there is a risk of damaging the object, conducting experiments on sacrificial objects is a good option to explore the process parameters before performing the tasks on the object of interest. However, using too many sacrificial objects can incur unnecessary costs. We want to know the right amount of sacrificial objects to identify the right process parameters while keeping the expected task completion cost to a minimum. To do this, we use a sequential decision making approach to learn the process parameters. The sequential decision making approach we proposed is a combination of look ahead search, surrogate modeling, and a policy to select process parameters. In computation, the search tree simulates future decisions beyond the current decision and evaluates the costs by considering the effect of future experiments. The decision made in our approach only commits the user to the current stage and does not affect future decisions. When a decision is made to conduct experiments on the sacrificial object, the parameter policy to select process parameters should be simulated. In this policy, we consider the right trade-off between exploration and exploitation. In the beginning, the policy favors exploration since we do not have much data. In later stages, less exploration and more exploitation will be performed as we have sufficient number of experimental data. We validate our approach using the experimental setup of the robotic spray painting application.
Does Narrative Play a Role in Engineering Decision-Making and Design? A Preliminary Study
Authors: Ferguson Scott , Bryden Kenneth M. 
Published: November 2022

Abstract
This paper presents data from a preliminary study designed to test the hypothesis that narrative is the primary decisionmaking tool employed in engineering design. We also look for evidence that analysis and optimization provide anchor points that drive the narrative forward, even in the presence of unresolvable uncertainties. The data in this paper comes from student engineers who are engaged in an 8-month, NASA-sponsored design competition. We code 46 statements from our interviews, spanning 10 different narrative elements as defined by Fenton-O’Creevy and Tuckett [1]. We find that engineers use multiple narrative elements to develop conviction when reaching action readiness, and that of the 46 coded statements, only 14 corresponded to quantitative analysis in the form of models and calculations. We find that in engineering design the decision-making journey involves a combination of quantitative analysis and qualitative engineering judgment that is evaluated to develop conviction and reach a state of action.
A Stress-Deflection Model for Fixed-Clamped Flexures Using a Pseudo Rigid Body Approach
Authors: Huxman Connor , Butler Jared 
Published: November 2022

Abstract
Fixed-clamped flexures are one common component of compliant mechanisms which remain difficult to design due to their unique force- and stress-deflection response profiles. In this work, a stress-deflection model for fixed-clamped flexures is proposed that utilizes a modified pseudo rigid body model. Special analysis is carried out on the characteristic radius factor, a parameter to which model accuracy is particularly sensitive. Effects of both deflection magnitude and flexure geometry on this parameter are explored and considerations for optimal parameter selection are included. Finite element analysis demonstrates that the model is able to predict the vertical applied force, horizontal reaction force, and maximum von Mises stress with a maximum percent error less than 3.5% at yield for a range of steel flexure topologies. Using the model to predict behavior of flexible polypropylene flexures undergoing larger deflections, the model has a maximum error between 4–14% using static model parameters, compared to 74–96% using the small deflection equations. The distinct combination of axial and bending stresses experienced in fixed-clamped flexures has made mechanisms which use these members challenging to design. This work provides a model that designers, engineers, and researchers can draw from to understand stress profiles present in these flexible members.
Toward a Comprehensive Framework for Preliminary Design Evaluation in Additive Manufacturing
Authors: Cayley Alexander , Mathur Jayant , Meisel Nicholas 
Published: November 2022

Abstract
Additive manufacturing (AM) is a rapidly growing technology within the industry and education sectors. Despite this, there lacks a comprehensive tool to guide AM-novices in evaluating the suitability of a given design for fabrication by the range of AM processes. Existing design for additive manufacturing (DfAM) evaluation tools tend to focus on only certain key process-dependent DfAM considerations. By contrast the purpose of this research is to propose a tool that guides a user to comprehensively evaluate their chosen design and educates the user on an appropriate DfAM strategy. The tool incorporates both opportunistic and restrictive elements, integrates the seven major AM processes and outputs an evaluative score and recommends processes and improvements for the input design. The paper presents a thorough framework for this evaluation tool and details the inclusion of features such as dual-DfAM consideration, process recommendations, and a weighting system for restrictive DfAM. The result is a detailed recommendation output that helps users to determine not only “can you print your design” but also “should you print your design” by combining several key research studies to build a comprehensive user design tool. This research demonstrates the potential of the framework through a series of case studies geometries. The preliminary framework presented in this paper establishes a foundation for future studies to refine the tool’s accuracy using more data and expert analysis.
Mean Squared Error May Lead You Astray When Optimizing Your Inverse Design Methods
Authors: Bernard Shai , Wang Jun , Fuge Mark 
Published: November 2022

Abstract
When performing time-intensive optimization tasks, such as those in Topology Optimization or Shape Optimization, researchers have turned to Machine Learned (ML) Inverse Design methods — i.e., algorithms that predict the optimized geometry from input conditions — to either replace or warm start traditional optimizers. Almost exclusively, such methods are trained and optimized to reduce the Mean Squared Error between a method’s output and a ground truth training dataset of optimized designs, this being the obvious choice for traditional supervised learning. While convenient, we show that this choice may be myopic. Specifically, we compare two methods of optimizing the hyper-parameters of both a random forest (RF) and k-nearest neighbors (KNN) model for predicting the optimal topology in a 2D heat sink example.
We show that under both direct Inverse Design as well as when warm starting further Topology Optimization (TO), using typical Mean Squared Error metrics produces less performant models than a proposed metric that directly evaluates the objective function, though both methods produce designs that are almost one order of magnitude better than a control condition that uses a uniform initialization common in TO. We also illustrate how warm starting TO with predicted solutions impacts both the convergence time, the type of solutions obtained during optimization, and the final designs. Sensitivity analyses on various model parameters demonstrate that the results are not dependent on other model hyperparameters. Overall, our initial results portend that researchers may need to revisit common choices for evaluating ID methods that subtly trade-off factors in how an ID method will actually be used. We hope our open-source dataset and evaluation environment will spur additional research in those directions.
Descriptor Aided Bayesian Optimization for Mixed Variable Materials Design With High Dimensional Qualitative Variables
Authors: Iyer Akshay , Yerramilli Suraj , Rondinelli James M. , Apley Daniel W. , Chen Wei 
Published: November 2022

Abstract
Engineering design often involves qualitative and quantitative design variables, which requires systematic methods for the exploration of these mixed-variable design spaces. Expensive simulation techniques, such as those encountered in materials design, underline the need for efficient search strategies — Bayesian optimization being one of the most widely adopted. Although recent developments in mixed-variable Bayesian optimization have shown promise, the effects of dimensionality of qualitative variables have not been well studied. High dimensional qualitative variables, i.e., with many levels, impose a large design cost as they typically require a larger dataset to quantify the effect of each level on the optimization objective. We address this challenge by leveraging domain knowledge about underlying physical descriptors to infer the effect of unobserved levels that have not been sampled yet. We show that domain knowledge about physical descriptors can be intuitively embedded into the latent variable Gaussian process approach — a mixed-variable GP modeling technique — and used to selectively explore levels of qualitative variables in the Bayesian optimization framework. Our method is robust to certain types of incomplete domain knowledge and significantly reduces the design cost for problems with high-dimensional qualitative variables.
“On the Edge” Obstacle Surmounting Method Using Hybrid Locomotion
Authors: Yeldan Alper , Arora Abhimanyu , Soh Gim Song 
Published: November 2022

Abstract
This paper presents on the edge obstacle surmounting method for QuadRunner, a hybrid quadruped robot, to overcome obstacles using hybrid locomotion where both legged and wheel configurations are utilized. When obstacle heights exceed the workspace of its leg, QuadRunner becomes quasi-statically mismatched and fails to achieve the climbing task quasi-statically. By incorporating its body as contact support, the center of gravity (COG) of QuadRunner can be successfully shifted on top of the obstacle to perform surmounting task. The unique design of the QuadRunner leg allows it to behave as a four-bar or slider-crank mechanism depending on the leg’s configuration. Here we detail the sub-state strategy for its surmount task, where QuadRunner goes through the sub-states {L}EAN, {H}OOK, {F}OUR-BAR, {S}LIDE, {G}ET-UP to climb obstacles. In addition, limitations of the operation are analyzed and the requirements for climbing are identified. With our proposed method, QuadRunner can surmount obstacles of heights between 10 cm and 22 cm (higher than its kinematic max height of 16 cm) within 25 seconds. Lastly, a reliability test shows that the robot can climb the obstacle with a 70% success rate.
Multidisciplinary Optimization to Reduce Cost and Power Variation of a Wave Energy Converter
Authors: McCabe Rebecca , Murphy Olivia , Haji Maha 
Published: November 2022

Abstract
Wave energy converters (WECs) can advance the global energy transition by producing clean power for utility grids and offshore technologies. This paper provides a multidisciplinary, dual objective optimization of the Reference Model 3 (RM3), a two-body point absorber WEC design benchmark. The simulation model employs linear hydrodynamics with force saturation and probabilistic waves. The RM3 geometry and controller parameters are optimized using sequential quadratic programming to minimize the levelized cost of energy (LCOE) and the coefficient of variation of power. The minimum-LCOE design produces a power variation of 205% and an LCOE of $0.08/kWh, a seven-fold cost reduction and 23% lower variation from the RM3 baseline of $0.75/kWh and 255% variation. Parameter sensitivities show that LCOE depends more strongly on site and economic parameters than geometric or material parameters, while power variation is largely insensitive to all parameters. A Pareto trade-offbetween cost and power variation reveals different optimal designs depending on which objective is prioritized, suggesting application-specific design heuristics. Three representative optimal designs are investigated: a minimum-LCOE design for cost-sensitive operations like utility power, a minimum-variation design for cost-insensitive installations like small offshore systems, and a balanced design for intermediate applications. Power probability distributions are shown for each.
Exploring Partitioning in Subsystem Prototyping
Authors: Connor Zachariah , Camburn Bradley 
Published: November 2022

Abstract
The development of a novel complex system is often cost-prohibitive. One reason for this, is that system-level performance can typically only be evaluated once all subsystems have been designed; and therefore, it is difficult to iterate on full system prototypes. Subsystem isolation is a well-established prototyping method to enable efficient design at both the system and subsystem levels. In this process, a complex system is partitioned into manageable subsystems that are subsequently designed in detail. Subsystem isolation permits rapid, low-cost iteration which is known as a critical driver of prototype performance. This paper provides an exploration of the role of partitioning in subsystem isolation prototyping through a review of methods and an applied case study. The paper reviews two topics related to subsystem isolation prototyping: 1) partitioning approaches and 2) strategies for iteration at the system versus subsystem level. Methods from design theory, systems engineering, and multi-domain optimization are reported. The case study explored subsystem prototyping in the design and construction of an electric in space propulsion test facility utilized in DoD research. The case study is used to demonstrate the processes involved in system partitioning methods and subsystem level design. These approaches allow designers to efficiently explore options for architecture-level design, without expending unnecessary resources on iterations of the integrated system.
Development of a Novel Three-Dimensional Parallel-Plate Biochemical Sensor for Electrochemical Impedance Spectroscopy (EIS)
Authors: Rafiee Negar , Huffman Brian , Shavezipur Mohammad 
Published: November 2022

Abstract
Miniaturized electrochemical impedance-based biochemical sensors have been extensively used for detection of chemical and biological agents at extremely low concentrations. The majority of the sensors presented in the literature have a two-dimensional (planar) geometry and use two interdigitated electrodes that are exposed to an aqueous solution for deteciotn. In this work, a novel three-dimensional sensor is presented that uses a parallel-plate geometry for sensor structure. The sensor has a digitated electrode patterned on the substrate and a planar and porous electrode suspended above the fixed electrode. The sensor is fabricated using PolyMUMPs and diethylhexyl phthalate (DEHP) is used to demonstrate the ability of the sensor for biochemical detection. The measurement results show that the sensor has distinct Nyquist responses for 0.02 ppm, 0.2 ppm, 2 ppm and 10 ppm concentrations of the DEHP. Different electrochemical phenomena such as double-layer capacitance formation and charge transport at high frequencies, and diffusion at low frequencies are detected using Nyquist plots. The experimental results show that, for 3D parallel-plate sensors, in contrast to interdigitated geometry, the electrostatic effect is noticeable, and the parallel-plate capacitance due to the dielectric effect of the solution notably affects the Nyquist response. This is an important effect, specially for biological detection.