bluuebunny/crossref_metadata_2025_split huggingface dataset:
On the Effectiveness and Optimal Performance of Gravity Compensation Design of an Inverted Slider-Crank Mechanism Using Realistic Springs
Authors: Zeng Xiangxian , Kuo Chin-Hsing 
Published: November 2022

Abstract
Designing of spring-balanced linkage mechanisms for gravity compensation is commonly done through an ideal assumption that the springs should have a zero free-length (ZFL). After the design results were generated, the ZFL springs could then be physically implemented on the designed linkages by versatile mechanical arrangement, which, however, will inevitably complicate the mechanism structure. This paper investigates the design performance of gravity compensation (i.e., static balancing) of a spring-balanced inverted slider-crank mechanism that uses realistic practical springs rather than ZFL springs. The design aims to seek optimal dimension parameters of the slider-crank such that either its peak driving torque or the overall energy consumption can be minimized under the balancing effect. The effectiveness and the balancing performance of the resulting designs are evaluated through statistics of a database of 8,000 case studies of the slider-crank with different load conditions. As a result, the peak driving torque of the balanced inverted slider-crank can be reduced by 60%–85% while the overall energy consumption can be decreased by 60%–89% in most cases. It demonstrates that, instead of using unrealistic ZFL springs to pursue the status of perfect balancing, using realistic springs in balancing a simple four-bar may still guarantee an acceptable compromise in balancing performance while avoiding complicating the mechanism structure for ZFL-spring implementation.
A New Framework for Efficient Sequential Sampling-Based RBDO Using Space Mapping
Authors: Park Jeong Woo , Lee Ikjin 
Published: November 2022

Abstract
In the engineering application of sampling-based reliability-based design optimization (RBDO), the Monte Carlo simulation (MCS) using a surrogate model of the performance function is mainly used for the probability of failure calculation and sensitivity analysis. However, if an inaccurate surrogate model is used, the calculation result using MCS will also be inaccurate, so it is essential to improve the accuracy of the surrogate model using sequential sampling. Hence, various sampling-based RBDO methods and sequential sampling methods have been proposed and used in various fields, and space mapping may also be a new framework for sequential sampling. In this paper, RBDO based on a sampling method with the Gaussian process regression (GPR) and space mapping is proposed. Space mapping is generally used to request high-fidelity samples to update the low-fidelity model in multi-fidelity model conditions. However, in the proposed method, it is used for sequential sampling to improve the accuracy of the existing surrogate model. The major advantage of the proposed space mapping-based RBDO is that the existing surrogate model and the finally updated surrogate model can be formulated with simple matrix and vector calculations. In particular, when there is only a surrogate model that has been built due to the loss of existing sample information since the space mapping updates the model, the accuracy of the surrogate model can be improved by sequential sampling. In addition, the proposed method can provide a convergence condition that can be used universally in several problems with the location of the sequential sampling point. The proposed method can be compared with sequential sampling-based RBDO using GPR, and the calculation accuracy and efficiency are demonstrated through 2-D highly nonlinear example and engineering problem.
The Impact of Gender on Individual Perceptions and Team Psychological Safety in Engineering Design Teams
Authors: Cole Courtney , Jablokow Kathryn , Mohammed Susan , Miller Scarlett 
Published: November 2022

Abstract
Improving team interactions in engineering to model gender inclusivity has been at the forefront of many initiatives in both academia and industry. However, there has been limited evidence on the impact of gender-diverse teams on psychological safety. This is important because psychological safety has been shown to be a key facet for the development of innovative ideas, and has also been shown to be a cornerstone of effective teamwork. But how does the gender diversity of a team impact the development of psychological safety? The current study was developed to explore just this through an empirical study with 38 engineering design student teams over the course of an 8-week design project. These teams were designed to be half heterogeneous (either half-male and half-female, or majority male) or other half homogeneous (all male). We captured psychological safety at five time points between the homogenous and heterogenous teams and also explored individual dichotomous (peer-review) ratings of psychological safety at the end of the project. Results indicated that there was no difference in psychological safety between gender homogenous and heterogenous teams. However, females perceived themselves as more psychologically safe with other female team members compared to their ratings of male team members. Females also perceived themselves to be less psychologically safe with male team members compared to male ratings of female team members, indicating a discrepancy in perceptions between genders. These results point to the need to further explore the role of minoritized groups in psychological safety research and to explore how this effect presents itself (or is covered up) at the team level.
Vibration Energy Harvester With Piecewise Linear Nonlinear Oscillator and Controllable Gap Size
Authors: Veney Jacob , D’Souza Kiran 
Published: November 2022

Abstract
Recently, vibration energy harvesting has been seen as a viable energy source to provide for our energy dependent society. Researchers have studied systems ranging from civil structures like bridges to biomechanical systems including human motion as potential sources of vibration energy. In this work, a bench-top system of a piecewise-linear (PWL) nonlinear vibration harvester is studied. A similar idealized model of the harvester was previously looked at numerically, and in this work the method is adjusted to handle physical systems to construct a realistic harvester design. With the physically realizable harvester design, the resonant frequency of the system is able to be tuned by changing the gap size between the oscillator and mechanical stopper, ensuring optimal performance over a broad frequency range. Current nonlinear harvester designs show decreased performance at certain excitation conditions, but this design overcomes these issues while also still maintaining the performance of a linear harvester at resonance. In this investigation, the system is tested at various excitation conditions and gap sizes. The computational response of the resonance behavior of the PWL system is validated against the experiments. Additionally, the electromechanical response is also validated with the experiments by comparing the output power generated from the experiments with the computational prediction.
Trade-Off Characterization Between Social and Environmental Impacts Using Agent-Based Models and Life-Cycle Assessment
Authors: Liechty Joseph C. , Mabey Christopher S. , Mattson Christopher A. , Salmon John L. , Weaver Jason M. 
Published: November 2022

Abstract
Meeting the UN’s sustainable development goals requires designers and engineers to solve multi-objective optimization problems involving trade-offs between social, environmental, and economic impacts. This paper presents an approach for designers and engineers to quantify the social and environmental impacts of a product at a population-level and then perform a trade-off analysis between those impacts. In the approach, designers and engineers define the attributes of the product as well as the materials and processes used in the product’s life cycle. Agent-Based Modeling (ABM) tools that have been developed to model the social impacts of products are combined with Life-Cycle Assessment (LCA) tools that have been developed to evaluate the pressures that different processes create on the environment. Designers and engineers then evaluate the trade-offs between impacts using Pareto frontiers to find non-dominated solutions that minimize environmental impacts while maximizing positive and/or minimizing negative social impacts. Product adoption models generated by ABM allow designers and engineers to approximate population-level environmental impacts and avoid Simpson’s paradox, where a reversal in choices is preferred when looking at the population-level impacts versus the product-level impacts. This analysis of impacts has the potential to help designers and engineers create more impactful products that contribute towards the UN sustainable development goals.
The Effects of Representation Modality on Subsystem Clustering
Authors: Murphy Alexander R. , Patel Apurva , Sen Chiradeep , Summers Joshua D. 
Published: November 2022

Abstract
The representation of an engineering system can come in different modalities. These modalities may impact how designers’ reason about a given system, which could have implications for how solutions are derived to a given problem. In this paper, function structures, function graphs, and component graphs are compared to understand how each representation modality impacts subsystem clustering behavior. This was achieved through a human-subjects experiment that presented three common products (a hair dryer, a food mixer, and a toilet) represented in one of the three described modalities and a troubleshooting scenario. Results suggest that the effects of the representation modality are consistent between participants at two different universities; however, these effects are not consistent between products. Similarly, the effects of representation modality on the occurrence of element pairs also vary between products, suggesting that the specifics of the system that is represented have a non-negligible effect on the clustering patterns. This analysis can be expanded in future work to include clusters of larger size to take a deeper look at subsystem reasoning. These preliminary results show that representation modality can affect systems thinking. This work takes a step towards understanding how function modeling impacts design cognition at the subsystem level, and how functional decomposition compares to other modalities of system representation.
Progress Towards Data-Driven High-Rate Structural State Estimation on Edge Computing Devices
Authors: Satme Joud , Coble Daniel , Priddy Braden , Downey Austin R. J. , Bakos Jason D. , Comert Gurcan 
Published: November 2022

Abstract
Structures operating in high-rate dynamic environments, such as hypersonic vehicles, orbital space infrastructure, and blast mitigation systems, require microsecond (μs) decision-making. Advances in real-time sensing, edge-computing, and high-bandwidth computer memory are enabling emerging technologies such as High-rate structural health monitoring (HR-SHM) to become more feasible. Due to the time restrictions such systems operate under, a target of 1 millisecond (ms) from event detection to decision-making is set at the goal to enable HR-SHM. With minimizing latency in mind, a data-driven method that relies on time-series measurements processed in real-time to infer the state of the structure is investigated in this preliminary work. A methodology for deploying LSTM-based state estimators for structures using subsampled time-series vibration data is presented. The proposed estimator is deployed to an embedded real-time device and the achieved accuracy along with system timing are discussed. The proposed approach has shown potential for high-rate state estimation as it provides sufficient accuracy for the considered structure while a time-step of 2.5 ms is achieved. The Contributions of this work are twofold: 1) a framework for deploying LSTM models in real-time for high-rate state estimation, 2) an experimental validation of LSTMs running on a real-time computing system.
A Decision-Centric Perspective on Evolving Cyber-Physical-Social Systems: Effectiveness, Group Value, and Opportunities
Authors: van Beek Anton 
Published: November 2022

Abstract
In this paper, we view evolving cyber-physical-social systems (CPSSs) from a group decision-making perspective, introduce the group value concept as a potential approach to improve their effectiveness, and conclude by identifying a set of research directions for further scientific inquiry. An evolving CPSS is a system in which the digital and physical spaces adapt to changing interests in the social space. In this paper, we introduce the group value concept as an approach to balancing the interests of individuals in the social space and deciding how a CPSS should evolve. The advantages afforded by the group value concept are twofold: (i) it enables CPSSs to evolve along with the interests of the social space, and (ii) it provides transparency in the decision-making process that will improve public support. The group value is a stochastic function that is constructed by modeling the distributions of individual value functions and shares a similarity with utility-theory and normative models for group decision-making. Through analysis of the introduced framework, we show: (i) how the group value concept can be used to bring about evolving CPSSs, (ii) introduce the difference between utility theory and normative models for group decision-making, (iii) define the conditions under which the introduced evolving CPSSs framework is valid, and (iv) delineate a set of four research areas for further scientific inquiry. The motivation for delineating a set of additional research challenges comes from the observation that group decisions violate the conditions of logical decision-making that can only be satisfied for an individual’s decisions. Consequently, establishing an agent that controls the evolution of a CPSS needs to consider the consequences of violating these conditions on the effectiveness of the decision. Through continued research in the identified decision-centric research areas, evolving CPPSs can be established to address many societal challenges and will be more effective as they enjoy broader public support.
Hysteresis Suppression in Coupled Resonators Under Simultaneous Primary and Superharmonic Excitations
Authors: Lyu Ming , Zhao Jian , Kacem Najib , Song Jiahao , Sun Rongjian , Liu Pengbo 
Published: November 2022

Abstract
A general model of two resonators subjected to electrostatic coupling and simultaneous primary and superharmonic excitations is developped. A reduced-order model including quadratic and cubic nonlinear terms is generated and the multi-scale method is used to solve the dynamic characteristics and analyze the contribution of the different frequency components. In addition, an overall nonlinear coefficient is defined, which can be adjusted to make the system exhibit different dynamic characteristics including softening, hardening, and linear behaviors. Finally, the conditions for restoring the linear behavior at the highest possible amplitude and suppressing its hysteresis are given.
Aircraft Maintenance Schedule Design Optimization During a Pandemic
Authors: Jordan Elizabeth , Azarm Shapour 
Published: November 2022

Abstract
The aim of this paper is to formulate and solve the aircraft maintenance scheduling design optimization problem while considering the effects of a pandemic, such as the COVID-19 pandemic. Aircraft maintenance is dependent on how much the aircraft is in service, among other factors. It is no surprise that the pandemic has significantly impacted airline operations, due to travel restrictions and people’s hesitancy to travel. Thus, it is important for airliners to consider the progression of the pandemic when designing their flight and maintenance schedules. The approach proposed in this paper addresses this issue by integrating several models. The first one is a time series forecasting model to predict future COVID-19 cases — in this paper we use a Long Short-Term Memory (LSTM) network. The second model is a simple neural network for predicting flight frequencies based on historical flight data and the results of the first model. The predicted flight frequencies are used to generate a flight schedule, which serves as input to the third model — the maintenance schedule design optimization model, which is formulated as a mixed binary-integer non-linear optimization problem. The final output from the integrated model is the optimized maintenance schedule and associated costs. To demonstrate the proposed approach, we present an illustrative example with 3 aircraft and perform a sensitivity analysis to gain further insight into the results.
Investigating How Engineers and Designers Communicate Design Rationale
Authors: Mirabito Yakira , Goucher-Lambert Kosa 
Published: November 2022

Abstract
Design rationale is the explicit documentation of the reasons behind decisions made in designing a product or system. Typically, design rationale is captured using a combination of written reports and oral presentations. Research shows that the structure and information used to communicate rationale significantly influence human behavior. To better understand the influence that communication of design rationale has on the design process, a detailed understanding of the information and techniques used to communicate design rationale needs to be studied. This research aims to identify how engineers and designers communicate this information in written form and the implications that their communication patterns have in engineering design. Eight hundred and forty-six pages of technical engineering design reports from 28 teams representing 116 individuals were analyzed using a mixed-methods approach and then compared across project types. The data were coded into categories using a schema we developed. The findings highlight the range of clarity that designers use in their rationales to support their design actions. Instead of clear, logical reasoning trends, designers often use techniques to fill gaps in design rationale through making assumptions, inserting oneself, or redirecting focus. The results suggest a need for improving design communication in engineering education and practice, perhaps through existing design reasoning frameworks or design rationale capturing tools. By capturing design rationale clearly between human designers, human-AI systems can leverage these findings to increase human confidence in and acceptance of a design agent’s recommendations.
Assessing the Manufacturability of Students’ Early-Stage Designs Based on Previous Experience With Traditional Manufacturing and Additive Manufacturing
Authors: Pearl Seth , Meisel Nicholas 
Published: November 2022

Abstract
As additive manufacturing (AM) becomes more mainstream in industry, the newer design for additive manufacturing (DfAM) considerations must be distinguished from the older design for traditional manufacturing (DfTM) considerations. Designers who wish to maximize additive manufacturing’s potential must reconsider the traditional manufacturing axioms they may be more familiar with. While research has previously investigated the potential influences that can affect the designs produced in concept generation, little research has been done explicitly targeting the manufacturability of early-stage concepts and how previous experience in manufacturing affects this. The research in this paper addresses this gap in knowledge, specifically targeting differences in concept generation due to designer experience with additive manufacturing and traditional manufacturing. In this study, participants were given priming content on DfTM and DfAM considerations and then asked to complete a design challenge centered on concept generation. The participants’ final designs were evaluated for manufacturability as suited for traditional and additive manufacturing. Results show that students with low manufacturing experience levels create designs that are more naturally suited for traditional manufacturing. Additionally, as designers’ manufacturing experience levels increase, there is an increase in the number of designs suited for additive manufacturing. This correlates with a higher self-reported use of DfAM axioms in the evaluation of these designs. These results suggests that students with high manufacturing experience levels make a subconscious decision for which manufacturing process to design for.
Scalar Field Prediction on Topologically-Varying Graphs Using Spectral Shape Encoding
Authors: Ferguson Kevin , Hardin James , Gillman Andrew , Kara Levent Burak 
Published: November 2022

Abstract
Scalar fields, such as stress or temperature fields, are often calculated in shape optimization and design problems in engineering. For complex problems where shapes have varying topology and cannot be parametrized, data-driven scalar field prediction can be faster than traditional finite-element methods. However, current data-driven techniques to predict scalar fields are limited to a fixed grid domain, instead of arbitrary graph/mesh structures. In this work, we propose a method to predict scalar fields on meshes of arbitrary refinement and topology. It uses features that capture shape geometry on a local and global scale as input to a multilayer perceptron to predict solutions to partial differential equations on graph data structures. The proposed set of global features is a vector that concisely represents the entire mesh as a spectral shape encoding. The model is trained on finite-element von Mises stress fields, and once trained it can estimate stress values at each node on any input mesh. Two shape datasets are investigated, and the model demonstrates decent performance on both, with a median R-squared value of 0.75. We also demonstrate the model’s performance on a temperature field in a conduction problem, where its predictions have a median R-squared value of 0.98. By predicting from a simple, yet rich, set of mesh features, our method provides a potential flexible alternative to finite-element simulation in engineering design contexts. Code and datasets are available at: https://github.com/kevinferg/spectral-shape-encoding.
Generative Design of Cyber-Physical-Human System Families: Concepts and Research Issues
Authors: Rosen David W. , Choi Young Mi 
Published: November 2022

Abstract
Cyber-physical-social systems (CPSS) are typically considered as an evolution of cyber-physical systems to include human interactions; further, that interactions among humans through CPSS can lead to emergence of social or community structures and behaviors. Cyber-physical-human systems (CPHS) can be considered as subsets of CPSS that are smart products that offer services to its customer, supported by back-end systems (e.g., information, finance) and other infrastructure. In this paper, initial concepts and research issues are presented regarding the design of CPHS families and generations of these families. Significant research gaps are identified that should drive future research directions. Potential impacts of filling those gaps provide motivation for the development of a design methodology for CPHS. The approach proposed here is a novel combination of generative and configuration design methods with product family design methodology. With this approach, a wide variety of CPHS, including customized CPHS, can be developed quickly by sharing technologies and modules across CPHS family members. Further, the foundation is provided for the evolution of CPHS families; for example, deciding when to add a new family member, when to design a new CPHS family platform, or when the current CPHS family can no longer be supported and a new family developed. The domain of assistive technology is used in this paper to provide an example field of practice that could benefit from a systematic design methodology and opportunities to leverage technology solutions.