bluuebunny/crossref_metadata_2025_split huggingface dataset:
Utilizing EEG to Explore the Mental States Involved in the Occurrence of Different Levels Design Fixation
Authors: Cao Juan , Zhao Wu , Guo Xin , Wu Tingting 
Published: November 2021

Abstract
Design fixation, which is a form of cognitive bias, is commonly reported to unconsciously occur when designers take the path of least resistance during the fulfillment of a design task. It’s thought to be easy and effortless. Nonetheless, the mental states such as mental effort and mental fatigue that accompany the occurrence of different levels of design fixation are still unknown. In the present study, an experiment using electroencephalography (EEG) was conducted to examine the mental effort and mental fatigue involved in the occurrence of different levels of design fixation during creative idea generation. Fluency, flexibility, the degree of copying, and the time spent generating ideas were used to evaluate the design performance and fixation level of each participant, and the task-related power changes of theta, alpha, and beta bands of participants with higher and lower levels of fixation during creative idea generation process were compared and analyzed separately. The comparison results revealed that participants with higher levels of design fixation made the less mental effort and showed higher levels of mental fatigue during the ideation process compared to those with lower levels of design fixation. These results provide additional evidence for the mental states involved in the occurrence of design fixation and could contribute to a deeper understanding of design fixation from the neuroscience perspective.
Fatigue Detection for Human Aware Adaptation in Human-Robot Collaboration
Authors: Kumar Rakesh Suresh , Jujjavarapu Sri Sadhan , Lee Lung Hao , Esfahani Ehsan T. 
Published: November 2021

Abstract
Knowledge about human cognitive and physical state is a key factor in physical Human-robot collaboration (pHRC). Such information benefits the robot in planning an adaptive control strategy to prevent or mitigate human fatigue. In this paper, we present a method to detect upper limb muscle fatigue during pHRC using a low-cost myoelectric sensor. We used Riemann geometry to extract robust features from the time-series data and designed a classifier to detect the binary state of human fatigue i.e. fatigued vs not fatigued. We evaluated the method using a fine-motor coordination task for the human to guide an industrial robot along a virtual path for sometime followed by a muscle curl exercise until it induces fatigue in the muscles, and then repeat the robot experiment. We recruited nine participants for the study and recorded muscle activity from their dominant upper limb using the myoelectric sensor and used the data to develop a classifier. We compared the accuracy and robustness of the classifier against conventional time-domain and wavelet-based features and showed that Riemann geometry-based features yield higher classification accuracy (∼ 91%) compared to conventional features and require less computational effort. Such classifier can be used in real-time to develop a human-aware adaptation strategy to prevent fatigue.
Understanding Resilience Optimization Architectures With an Optimization Problem Repository
Authors: Hulse Daniel , Zhang Hongyang , Hoyle Christopher 
Published: November 2021

Abstract
Optimizing a system’s resilience can be challenging, especially when it involves considering both the inherent resilience of a robust design and the active resilience of a health management system to a set of computationally-expensive hazard simulations. While prior work has developed specialized architectures to effectively and efficiently solve combined design and resilience optimization problems, the comparison of these architectures has been limited to a single case study. To further study resilience optimization formulations, this work develops a problem repository which includes previously-developed resilience optimization problems and additional problems presented in this work: a notional system resilience model, a pandemic response model, and a cooling tank hazard prevention model. This work then uses models in the repository at large to understand the characteristics of resilience optimization problems and study the applicability of optimization architectures and decomposition strategies. Based on the comparisons in the repository, applying an optimization architecture effectively requires understanding the alignment and coupling relationships between the design and resilience models, as well as the efficiency characteristics of the algorithms. While alignment determines the necessity of a surrogate of resilience cost in the upper-level design problem, coupling determines the overall applicability of a sequential, alternating, or bilevel structure. Additionally, the application of decomposition strategies is dependent on there being limited interactions between variable sets, which often does not hold when a resilience policy is parameterized in terms of actions to take in hazardous model states rather than specific given scenarios.
Coupled Electromagnetic and Thermoelastic Response of Conductive Materials Under Mechanical Loading and High Current Pulse Conditions
Authors: Michopoulos J. G. , Iliopoulos A. P. , Steuben J. C. , Apetre N. A. , Douglass S. , Lynn A. G. , Cairns R. L. 
Published: November 2021

Abstract
Understanding, modeling and simulating the behavior of thermally and electrically conductive materials under simultaneous high electric current pulse and mechanical preload conditions has long been a topic of interest for various applications involving electromechanical systems. To this end, the present work describes a computational framework that enables the fully coupled electromagnetic and thermoelastic analysis of such systems. The partial differential equations (PDEs) representing the electrodynamic and thermodynamic conservation laws are utilized and encapsulated in a computational environment enabling their numerical solution. A specific contribution of the framework is that it is capable of solving the non-linear forms of the relevant PDEs that are formed due to the dependence of the material properties on state variables such as temperature. The proposed framework is applied for a specific high-current testing apparatus under construction in our laboratory. A high current pulse is conducted through a mechanically pretensioned specimen and generates Joule heating activating thermo-elastic strains in conjunction with Lorentz body forces influencing the associated dynamic thermo-structural response of specimens of interest. Application of the developed framework enables the generation of field predictions for the quantities of interest. Selective simulation results are presented to demonstrate the capabilities of the proposed framework followed by discussion and conclusions.
Co-Evolution of Communication and System Performance in Engineering Systems Design: A Stochastic Network-Behavior Dynamics Model
Authors: Chaudhari Ashish M. , Gralla Erica L. , Szajnfarber Zoe , Panchal Jitesh H. 
Published: November 2021

Abstract
The socio-technical perspective on engineering system design emphasizes the mutual dynamics between interdisciplinary interactions and system design outcomes. How different disciplines interact with each other depends on technical factors such as design interdependence and system performance. On the other hand, the design outcomes are influenced by social factors such as the frequency of interactions and their distribution. Understanding this co-evolution can lead to not only better behavioral insights, but also efficient communication pathways. In this context, we investigate how to quantify the temporal influences of social and technical factors on interdisciplinary interactions and their influence on system performance. We present a stochastic network-behavior dynamics model that quantifies the design interdependence, discipline-specific interaction decisions, the evolution of system performance, as well as their mutual dynamics. We employ two datasets, one of student subjects designing an automotive engine and the other of NASA engineers designing a spacecraft. Then, we apply statistical Bayesian inference to estimate model parameters and compare insights across the two datasets. The results indicate that design interdependence and social network statistics both have strong positive effects on interdisciplinary interactions for the expert and student subjects alike. For the student subjects, an additional modulating effect of system performance on interactions is observed. Inversely, the total number of interactions, irrespective of their discipline-wise distribution, has a weak but statistically significant positive effect on system performance in both cases. However, excessive interactions mirrored with design interdependence and inflexible design space exploration reduce system performance. These insights support the case for open organizational boundaries as a way for increasing interactions and improving system performance.
Co-Design Optimization of a Combined Heat and Power Hybrid Energy System
Authors: Li Dongze , Wu Jiaxin , Zhang Jie , Wang Pingfeng 
Published: November 2021

Abstract
As an energy efficient technology that generates electricity and captures the heat that would otherwise be wasted to provide useful thermal energy, combined heat and power (CHP) hybrid energy systems have been widely used in the U.S. In the presented study, a two-stage co-design optimization model for CHP-based hybrid energy systems is developed. By applying a mixed integer programming (MIP) method, the optimization is performed from the operational and design perspectives. Six components: CHP, boiler, heat recover unit (HRU), thermal storage system (TS), power storage system (ES), and photovoltaic (PV) are considered in the CHP-based microgrids. During the optimization process, the cost-based optimal component design solutions are firstly obtained by minimizing the total installation costs of the components. The optimal operational strategy is further attained based on the component design by minimizing the costs from production, operation and maintenance, startup, and unsatisfied load. In the end, non-disruptive and disruptive scenarios are considered in the case study to testify to the model’s effectiveness in co-design and reliability improvement.
Spatial Mechanism-Environment Contact Geometric Models
Authors: Robson Nina , Lee Aaron 
Published: November 2021

Abstract
This work proposes a theoretical foundation for a general spatial geometric mechanism-environment contact model. In the proposed model the curvature of the environment in the vicinity of the contact is approximated by a number of spherical surfaces with known radii of curvature that constrain/define the movement of the body. We show how the modeled body-environment contact and curvature constraints can be transformed into conditions on spatial velocity and acceleration (i.e. first and second order effects) of certain points of the moving body that can be incorporated in the kinematic task for designing spatial mechanisms. Further, we explore the exact synthesis of a spatial six degrees-of-freedom TPS kinematic chain which end-effector maintains contact with objects in the environment and varies orientation in the vicinity of a contact location. It is discussed how the higher order motion constraints allow for the introduction of kinematic task variations in the vicinity of a contact, resulting in different behaviors of the designed spatial mechanism. The theoretical foundation presented in this paper is crucial in gaining understanding of the constraints in describing mechanism-environment interactions in the vicinity of a contact and is a new contribution.
Design Experiences as Pathways for Embracing Failure
Authors: Das Madhurima , Yang Maria C. 
Published: November 2021

Abstract
There is a growing movement in engineering and industry for students and practitioners to learn to embrace failure and develop resilience. The design process is naturally full of iteration and failures that can inherently be leveraged as learning opportunities for students. This study establishes a set of failure-related interventions implemented in an introductory design course, and then examines potential links to students’ experiences and attitudes towards failure. These interventions included a failure-themed “speaker seminar” series, a virtual gallery of design mistakes (“mistake museum”), and the introduction of a prototype logger for students to intentionally reflect on each iteration of their own design projects, including what went wrong and what was learned from the iteration. Students found these interventions to be effective in gaining perspective on failure and learning to embrace it. Students’ perceptions of the openness to failure of the class, perceptions of the field of design’s openness to failure, and perceptions of their major’s openness to failure all changed significantly, while their perceptions of their own openness to failure and their academic institution’s openness to failure were unchanged over the duration of the design course. Students also self-reported that the reflective processes of logging prototypes made them feel more comfortable with failure.
Safe Collaboration Between Human and Robot in a Context of Intermittent Haptique Interface
Authors: Mugisha Stanley , Zoppi Matteo , Molfino Rezia , Guda Vamsi , Chevallereau Christine , Chablat Damien 
Published: November 2021

Abstract
In the list of interfaces used to make virtual reality, haptic interfaces allow users to touch a virtual world with their hands. Traditionally, the user’s hand touches the end effector of a robotic arm. When there is no contact, the robotic arm is passive; when there is contact, the arm suppresses mobility to the user’s hand in certain directions. Unfortunately, the passive mode is never completely seamless to the user. Haptic interfaces with intermittent contacts are interfaces using industrial robots that move towards the user when contact needs to be made. As the user is immersed via a virtual reality Head Mounted Display (HMD), he cannot perceive the danger of a collision when he changes his area of interest in the virtual environment. The objective of this article is to describe movement strategies for the robot to be as fast as possible on the contact zone while guaranteeing safety. This work uses the concept of predicting the position of the user through his gaze direction and the position of his dominant hand (the one touching the object). A motion generation algorithm is proposed and then applied to a UR5 robot with an HTC vive tracker system for an industrial application involving the analysis of materials in the interior of a car.
Mastering Manufacturing: Exploring the Influence of Engineering Designers’ Prior Experience When Using Design for Additive Manufacturing
Authors: Prabhu Rohan , Simpson Timothy W. , Miller Scarlett R. , Meisel Nicholas A. 
Published: November 2021

Abstract
Additive manufacturing (AM) processes present designers with unique capabilities while imposing several process limitations. Designers must leverage the capabilities of AM — through opportunistic design for AM (DfAM) — and accommodate AM limitations — through restrictive DfAM — to successfully employ AM in engineering design. These opportunistic and restrictive DfAM techniques starkly contrast the traditional, limitation-based design for manufacturing techniques — the current standard for design for manufacturing (DfM). Therefore, designers must transition from a restrictive DfM mindset towards a ‘dual’ design mindset — using opportunistic and restrictive DfAM concepts. Designers’ prior experience, especially with a partial set of DfM and DfAM techniques could inhibit their ability to transition towards a dual DfAM approach. On the other hand, experienced designers’ auxiliary skills (e.g., with computer-aided design) could help them successfully use DfAM in their solutions. Researchers have investigated the influence of prior experience on designers’ use of DfAM tools in design; however, a majority of this work focuses on early-stage ideation. Little research has studied the influence of prior experience on designers’ DfAM use in the later design stages, especially in formal DfAM educational interventions, and we aim to explore this research gap. From our results, we see that experienced designers report higher baseline self-efficacy with restrictive DfAM but not with opportunistic DfAM. We also see that experienced designers demonstrate a greater use of certain DfAM concepts (e.g., part and assembly complexity) in their designs. These findings suggest that introducing designers to opportunistic DfAM early could help develop a dual design mindset; however, having more engineering experience might be necessary for them to implement this knowledge into their designs.
Getting Beyond the Hairy House: Using Structure-Function-Mechanism to Advance Biologically Inspired Design Pedagogy
Authors: Helms Michael E. , Ehsan Hoda , Kim Euisun , Moore Roxanne , Alemdar Meltem , Cappelli Christopher J. , Rosen Jeff , Weissburg Marc 
Published: November 2021

Abstract
In this case study we report on the use of a Next Generation Science Standards (NGSS)-aligned form of Structure-Behavior-Function, called Structure-Function-Mechanism (SFM), to teach four high school engineering teachers an approach for Biologically Inspired Design (BID). Functional theories of design describe a natural way in which designers solve design problems. They provide support for case-based and analogical-based reasoning systems and have been used successfully to teach BID to undergraduate students. We found that teachers instructed on BID practice and pedagogy using our modified theory were able to grasp the structural concepts and looked for clear markers separating mechanism (behavior) and function. Because of the systems-of-systems nature of most biological entities, these boundaries were often subjective, presenting unique challenge to teachers. As high school engineering teachers look for methods to enhance their pedagogy and to understand multidisciplinary content, these findings will inform future curriculum development and professional learning approaches for engineering education.
Design of a Planar Cable-Driven Parallel Crane Without Parasitic Tilt
Authors: Etienne Lionel , Cardou Philippe , Métillon Marceau , Caro Stéphane 
Published: November 2021

Abstract
Cable-Driven Parallel Robots (CDPRs) offer high payload capacities, large translational workspace and high dynamics performances. Their rotational workspace is generally far more limited, however, which can be resolved by using cable loops, as was shown in previous research. In the case of fully-constrained CDPRs, cable loops can induce unwanted torques on the moving-platform, causing it to tilt and move away from its intended position, which we call parasitic tilt. Hence, the orientation accuracy of such robots is usually limited. This paper deals with the design, modelling and prototyping of a planar CDPR with infinite rotations, without parasitic tilt and without an additional motor. This robot, which we call a Cable-Driven Parallel Crane (CDPC), is composed of a mobile platform (MP) with an embedded mechanism and a transmission module. The MP is linked with the frame by a parallelogram of three cables to constrain its orientation, including a cable loop, as well as a fourth cable. The two-degree-of-freedom (dof) motions of the moving-platform of the CDPC and the internal dof of its embedded mechanism are actuated by a total of three actuators, which are fixed to the frame. As a consequence, the overall system is fully-actuated, its total mass and inertia in motion is reduced and it is free of parasitic tilts.
Exploring the Effects of Individual Differences in Function Structure Modeling Behaviors
Authors: Patel Apurva , Summers Joshua D. 
Published: November 2021

Abstract
This paper presents an exploratory study conducted to understand the role of individual differences between designers in the function modeling process and with respect to final models. An input-process-output framework of function modeling is proposed to systematically approach this theory building and discovery research study. Four measures of individual differences are identified of interest. These include the systemizing quotient, goal orientation, risk propensity, and concept design thinking style. Each metric is composed of multiple items that can be assessed through survey instruments. A previously developed protocol study is used to capture function modeling behaviors and a final function structure model. Data collected from the survey instruments and protocol study is processed to generate input, process, and output measures. A regression-based analysis is used to identify correlations in three groups: input-process, input-output, and process-output. Potential correlations of interest are identified within each group. Implications of these correlations are discussed from a function structure modeling perspective and hypotheses for future research are identified based on the patterns observed in this study. Three testable hypotheses are proposed for future investigation: (1) Goal orientation has no effect on activity distribution in the function modeling process, (2) Thinking style has no effect on the function modeling process, and (3) Risk propensity has no effect on element distribution in the function modeling process. Finally, an anticipated experiment is outlined to investigate one of the potential relationships discovered in this study.
Connecting Design Actions, Reasoning, and Outcomes in Concept-Selection
Authors: Mirabito Yakira , Goucher-Lambert Kosa 
Published: November 2021

Abstract
Final concepts are often not the most creative or innovative design within the solution space. The purpose of this research is to gain insight into the decisions made in concept selection. In particular, we studied how designers link multiple decision-making elements together, including: actions (what people do), reasoning (why they do it), and design outcomes (an objective measure of engineering performance). Fifty-seven participants were tasked with solving a design challenge relating to a robotic gripper by selecting a design within a predefined design space. Each design had a corresponding measure (termed “success rate”) which enabled each designer’s performance to be quantified and compared against other designers. The task was hosted on an interactive interface in which design actions were collected. A post-task survey probed for the reasoning behind design actions. Characterization of decision-making behavior and reasoning was rooted in prior design literature. Design actions were quantified concerning the degree of design space explored and the decision-making strategies employed. Key results include design strategies such as manipulation techniques, the impact of maximum observed success rates, and a willingness to submit an alternative solution which influenced design outcomes. Although designer preferences validated the design strategies identified, there was no correlation between the decision factors considered and improved outcomes. The methods and findings from this work assessed the underlying dynamics when engineers selected less innovative or creative solutions and recommended decision-making strategies that should be considered to improve design outcomes.