bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Impact of Facilitation During Idea Generation in Engineering Design
Authors: Murphy Alexander R. , Moseley Emily , Weaver Morgan , Linsey Julie S. 
Published: November 2023

Abstract
During the engineering design process, group idea generation is often used to search the solution space before committing resources to the development of new products and services. The study presented investigates how trained facilitation using a brainstorming approach impacts the idea generation process. Three conditions are presented: a facilitated condition, unfacilitated condition, and nominal condition. Participants in these experimental conditions were tasked with generating ideas to a design problem leveraging a morphological matrix to record and organize their ideas. Specifically, the quantity of generated ideas, perceived task load, and psychological safety are measured when a trained facilitator is present or not present during the idea generation process. Results suggest that a trained facilitator has a positive impact on the quantity of generated ideas in an engineering context but is not as effective as nominal groups (individuals working independently), which is consistent with prior literature. However, results suggest that trained facilitation may have other benefits during idea generation such as reduced perceived task load and increased psychological safety of group members. This study shows the significant impact of trained facilitation on idea generation during engineering design and provides a foundation for future work that expands these results into how facilitation may impact other stages of the engineering design process.
Feeling the Distance: Exploring Novice Designers’ Perceptions of the Psychological Distance Towards and Empathy Induced by Problem Variations
Authors: Herzog Jenna , Fodale Rebekah , Alsager Alzayed Mohammad , Starkey Elizabeth M. , Prabhu Rohan 
Published: November 2023

Abstract
The accelerating depletion of natural resources has necessitated the design of environmentally sustainable engineering solutions. To meet this need for sustainable solutions, designers must actively incorporate considerations of environmental impact in their design decisions. Prior research suggests that the effects of climate change are often perceived to be psychologically distant, and this distance could inhibit individuals from actively engaging in environmentally sustainable behavior. Little research has investigated the impact of problem framing based on designers’ psychological distance on design performance. Furthermore, research suggests that empathy development could be an effective mechanism for bridging psychological distance. However, little research has assessed the utility of empathy-invoking problem framing in sustainable engineering design practice and education. Our aim in this study is to explore this research gap by comparing student designers’ perceptions of different problem formulations. Specifically, we tested the effects of variations in (1) the socio-spatial context and (2) the empathy focus of a similar design problem. The effects of these variations on the perceived psychological distance and empathy-invoking nature were tested through a 2 × 2 between-subjects experiment. From the results, we see that the variations in the problem formulation did not relate to either the perceived psychological distance or the perceived empathy-invoking nature. These findings suggest that issues related to environmental sustainability tend to be perceived similarly, despite differences in their socio-spatial context and empathy-invoking nature.
System Level Techno-Economic and Environmental Design Optimization for Ocean Wave Energy
Authors: McCabe Rebecca , Dietrich Madison , Liu Alan , Haji Maha 
Published: November 2023

Abstract
Ocean waves have the potential to provide clean energy to large swaths of society. Despite their promise, wave energy converters (WECs) are prohibitively expensive, partly due to the lack of design convergence in the WEC industry. A system-level optimization facilitates cost reduction, systematic evaluation of various architectures, and ultimately design convergence. This paper outlines an optimization framework for wave energy at the industry level, starting with the selection of a suite of metrics that capture the value proposition of wave energy more fully than traditional metrics. This includes metrics that encompass environmental sustainability and economic value in addition to economic cost. Two processes for metric weighting are proposed: a heuristic process for emerging off-grid markets and a capacity expansion model for the grid market. As a demonstration of the weighting process, results from three existing studies are synthesized into weights for the levelized cost of energy, capacity factor, and standard deviation of capacity factor. Finally, the optimization design variables, parameters, objectives, and constraints are formulated, and methods to handle the many categorical or integer design variables and parameters are discussed. The process articulated here can also be applied to other emerging energy technologies, ultimately advancing decarbonization in the energy sector.
Gender-Based Social Revolutions and Their Effect on Technology Evolution: A Case Study of the Sewing Machine
Authors: Kang Samantha , Dong Andy 
Published: November 2023

Abstract
This paper applies a feminist critique of technology to develop a model for design trajectories, specifically the technology life cycle. The model aims to explain the origins of radical design changes even when scientific revolutions are absent (or distant) and radical performance improvements are inconsequential. The breast pump is introduced to illustrate how public health, social and cultural norms, federal policies, and identity influence a design trajectory. The breast pump’s delayed and limited evolution despite technology advances indicates the compounding consequences of these factors on a technology’s design trajectory. We then investigate the sewing machine (first patented in 1846) to explore this phenomenon more closely. Our research illustrates conditions under which a social norms lens might change the expected technological outcome predicted by purely economic or organizational models. By shifting the unit of analysis away from single designs to a trajectory of design cycles over time, this paper offers explanations for conditions under which designs will remain resistant to debiasing, with only minor incremental change, and the social dynamics associated with design discontinuities. Our model includes the social construct of gender norms as a socio-technological lens to examine the limitations of the traditional technology life cycle model. Finally, we discuss how our new model can update engineering design theory and pedagogy.
The Influence of Differential Pressure on Stability of Vertical Rotor-Seal System
Authors: Kimura Shogo , Inoue Tsuyoshi , Taura Hiroo , Heya Akira 
Published: November 2023

Abstract
The rotordynamic (RD) fluid force generated in fluid elements such as seals in turbomachinery affects the stability of turbomachinery and causes shaft vibrations. Various studies have been conducted to clarify the effects of seals on the stability of rotor systems. Many studies have been conducted on the rotor dynamics of horizontal shaft systems, considering the RD fluid force generated in the seals, and in these studies the stability of horizontal shaft systems has been assessed by eigenvalue analysis using RD coefficients. However, few studies have been conducted on vertical shaft systems. The dynamic behavior of vertical shafts differs significantly from that of horizontal shafts owing to their different structural arrangements. Vertical shaft systems are generally prone to instability because of the fluid film whirl, and the amplitude of shaft whirl tends to be large. When the amplitude is large, the RD fluid force cannot be linearized around the equilibrium point using RD coefficients. Therefore, destabilization and stabilization phenomena that appear in vertical shaft systems cannot be predicted by eigenvalue analysis. To predict such phenomena, Fluid-Structure Interaction (FSI) analysis is required, which considers the interaction between the shaft vibration and the RD fluid force generated in seals. This study used FSI analysis to investigate the effects of unbalance and differential pressure on the stability of a vertical shaft system subjected to RD fluid force generated in the seal.
High-Frequency Band Gap Design of Porous Phononic Crystals by Topology Optimization
Authors: Murai Naoki , Noguchi Yuki , Yamada Takayuki 
Published: November 2023

Abstract
Phononic crystals can control the propagation of elastic waves by their unique dispersion properties such as band gaps. In recent years, the application of phononic crystals for controlling high-frequency elastic waves, which is important for managing thermal conductivity in the nanoscale and for wireless communication, has been attracting attention. This research proposes a design method of phononic crystals which exhibit band gaps at high band order for the applications with high-frequency elastic waves. To consider manufacturability in nanoscale, phononic crystals made of a single material are assumed. A level set-based topology optimization is used for the design of phononic crystals. An objective function is proposed to achieve a band gap within an aimed range of frequency. As a numerical example, 2-dimensional silicon phononic crystal with hexagonal lattice is optimized. Our approach successfully generates a phononic crystal with a band gap at high band order.
Physics-Based Automated Data Preprocessing (ADP) for Machine Learning Applications
Authors: Valizadeh Sotubadi Saleh , Nguyen Vinh 
Published: November 2023

Abstract
This study aims to develop an Automated Data Processing (ADP) algorithm for real-world applications of machine learning (ML). Specially, this work seeks to contribute to the state-of-the-art Automated ML domain by providing a framework by which all the processing units of any ML application could be automatically determined based on a set of prior knowledge provided to the algorithm without external assistance by an ML developer. For this purpose, the ADP algorithm was designed consisting of a Reinforcement Learning (RL) agent as the actor-critic at the top level and a classification unit at the lower level for the data processing of the given raw data. The ADP algorithm is tested on a set of raw data extracted from several lathe turning experiments for the purpose of cutting tool-wear classification. The data are collected using acceleration, acoustics, and temperature sensors mounted on a Computer Numerical Controller (CNC) lathe machine. The ADP is implemented on the collected data and the RL agent is trained for several episodes. The performance accuracy of the classification unit on the test data is the criterion to define the reward function of the RL agent upon which the agent is trained. After the training is conducted the best action policy is extracted based on higher state values and the number by which each state is chosen as the optimal action policy at the end of each learning episode. The results show that the policy chosen by the ADP algorithm exhibits better classification performance on the given data and can outperform other policies different from the optimal policy selected by the algorithm. Therefore the proposed RL framework incorporates physics-based knowledge of a turning process to systematically automate data processing for ML applications.
Identification of Singularities and Real and Complex Solution Varieties of the Loop Constraints of Linkages Using the Kinematic Tangent Cone
Authors: Müller Andreas , Li Zijia 
Published: November 2023

Abstract
The configuration space (c-space) of a mechanism is the real solution variety of a set of loop closure constraints. Singularities of this variety (referred to as c-space singularities) are singular configurations of the mechanism. In addition, a mechanism may exhibit other kinematic singularities that are not visible from the differential geometry of the c-space (referred to as hidden singularities). The latter is related to a drop in the rank of the constraint Jacobian while the c-space is locally a smooth manifold. Another kinematic feature that is only due to the corank of the constraint Jacobian is the shakiness of a mechanism. Such situations were analyzed by investigating the local geometry of the c-space and its corank stratification. It has been shown recently that hidden singularities and shakiness can be attributed to the fact that complex solution branches intersect with the c-space, i.e., with real solution branches. This paper employs the kinematic tangent cone to identify local solution branches. While the kinematic tangent cone is an established generally applicable concept, which gives rise to a computational (numeric and symbolic) algorithm, it has yet only been applied for analyzing the real solution set. The method is shown for several examples. Further, the algebraic aspects are briefly elaborated.
Who Is This Design For? Promoting Consideration of People During Concept Generation
Authors: Makhlouf Thanina E. , Murphy Laura R. , Daly Shanna R. , Seifert Colleen E. 
Published: November 2023

Abstract
The success of a design is not determined solely by its technical aspects. A design must work for the people who will be using it and in the context in which it will be used. Human-centered design approaches suggest strategies to remind engineering designers of the people impacted by their design decisions. While many of these strategies can be used within and across multiple design phases, during the act of sketching concepts during concept generation, there are few explicit strategies for centering people. We investigate possible impact from a simple intervention during a concept generation task through a between-subjects experiment. Working alone is a single design session, half of a group of mechanical engineering students were asked to explicitly “represent people” within their conceptual sketches. Afterwards, all students reviewed each of their concepts to answer, “Who is this idea for? Who do you imagine would use it?” Those who received the intervention requiring representation of people within concept sketches produced significantly longer reflections with greater depth compared to the control group. Adding drawings of people to sketches resulted in more consideration of the social and physical context of use and of the user’s personal preferences and values. Depicting people in generated concepts substantially reduced claims a design is “for everyone,” suggesting explicit representation of potential users produced more thoughtful consideration of diversity among potential users.
Predicting the Quantity of Recycled End-of-Life Products Using a Hybrid SVR-Based Model
Authors: Xia Hanbing , Han Ji , Milisavljevic-Syed Jelena 
Published: November 2023

Abstract
End-of-life product recycling is crucial for achieving sustainability in circular supply chains and improving resource utilization. Forecasting the quantity of recycled end-of-life products is essential for planning and managing reverse supply chain operations. Decision-makers and practitioners can benefit from this information when designing reverse logistics networks, managing tactical disposal, planning capacity, and operational production. To address the challenge of small sample data with multiple factors influencing the recycling number, and to deal with the randomness and nonlinearity of the recycling quantity, a hybrid predictive model has been developed in this research. The model is based on k-nearest neighbor mega-trend diffusion (KNNMTD), particle swarm optimization (PSO), and support vector regression (SVR) using the data from the field of end-of-life vehicles as a case study. Unlike existing literature, this research incorporates the data augmentation method to build an SVR-based model for end-of-life product recycling. The study shows that developing the predictive model using artificial virtual samples supported by the KNNMTD method is feasible, the PSO algorithm effectively brings strong approximation ability to the SVR-based model, and the KNNMTD-PSO-SVR model perform well in predicting the recycled end-of-life products quantity. These research findings could be considered a fundamental component of the smart system for circular supply chains, which will enable the smart platform to achieve supply chain sustainability through resource allocation and regional industry deployment.
A Heuristic Approach to Classify Geometrically Defective Bead Segments Based on Range of Curvature, Range of Sound Power and Maximum Height
Authors: Surovi Nowrin Akter , Soh Gim Song 
Published: November 2023

Abstract
The paper presents a classification methodology for determining geometrically good and defective bead segments for the Wire Arc Additive Manufacturing (WAAM) process based on optimally determined thresholds of three parameter metrics: namely, the range of curvature, range of sound power, and maximum height. We show how a heuristic search can be performed to systematically identify the suitable thresholds for the various parameter metrics to classify bead segments. The heuristic search for each parameter metric is performed by finding a threshold value that minimizes overlapping area between the Kernel Density Estimation (KDE)s formed by the distribution of the good and defective bead segments. We combine these thresholds to yield a three-dimensional threshold window for identifying geometrically defective bead segments. This approach is helpful for dataset labeling that overcomes the visual inspection labeling used in the literature. Experiments are conducted on the Inconel 718 material, with 660 bead segment data. We verify the results of our threshold selection using real bead scans and evaluate its mean squared error (MSE) with the parabolic bead model commonly used in literature to model bead profiles. We find that our proposed approach can separate the starting and ending segments of the beads well and identify geometrically defective bead segments between them. We also observe that the MSE of geometrically defective bead segments’ is much higher than the good segments, demonstrating the validity of our approach to classify geometrically defective bead segments.
Development of a Pillow Placement Process for Robotic Bed-Making
Authors: Cheung Chi-Hong , Tan Aaron Hao , Goldenberg Andrew 
Published: November 2023

Abstract
Bed-making is a common chore completed in various living environments to promote user comfort, hygiene, and well-being. Unfortunately, the physical and tedious nature of the act makes it challenging for segments of the elderly community to complete the chore, and thus the opportunity arises to develop robots to automate the task. However, despite the opportunity’s importance and positive impact, there is limited research on developing robotic bed-making systems. The aim of this research is to start addressing this gap by proposing methods for accomplishing pillow placement, a major part of the bed-making task. This paper introduces a pillow placement process to be used by a static 6-DOF (degree of freedom) one-armed robotic manipulator equipped with a 2-finger gripper. The process uses YOLOv4-tiny, image transformations, and principal component analysis (PCA) to infer pillow poses in a transformed RGB image, as well as a set of manipulator macro-actions to move pillows to their goal pose. We evaluated the proposed methodology in a real-world setting, where it enabled the robot to place pillows at desired poses on a miniature bed successfully in 89% of the experimental runs.
Resilience and Sustainability in Certified Green Buildings: Applying Ecosystem Concepts to Aid in More Dynamic Green Communities
Authors: Payne Emily , Wagner Hannah , Layton Astrid 
Published: November 2023

Abstract
Sustainable and resilient buildings ensure safety and lifespan while also benefiting the environment. Leadership in Energy and Environmental Design (LEED) is one respectable certification that many buildings can receive to ensure that they are meeting future climate and energy goals. However, LEED buildings have credits that do not necessarily agree with creating a sustainable environment. When comparing the orientation of LEED points and their relationship to the building and community to ecological structures, we found that a rearrangement of categories can provide visualization for organized recycling and higher cyclicity through ecological network applications. This relationship was applied to a new scorecard which has results indicating that if designers choose to meet criteria where one credit in each grouping is implemented in construction, then a sustainable building can still be efficient as well as recognized as a green building.
Impact of Shaft Misalignments in Planetary Gearboxes
Authors: Fuerst Marius , Otto Michael , Stahl Karsten 
Published: November 2023

Abstract
Planetary gear sets have a more complex structure than layshaft gearboxes which leads to challenges in design, analysis and operation. In this paper, design critical phenomena in planetary gear sets are addressed and latest test bench findings are presented. These focus on the impact of carrier shaft and planet pin misalignments on the load distribution in the gear mesh. Based on the deformation behavior of the loaded gearbox, which is determined by a coordinate measuring machine, a method is proposed to analyze the tooth alignment deviation with respect to the carrier rotational position. Finally, results from different test setups are analyzed and compared.
State-Space Characterization of Human Balance Through a Reinforcement Learning Based Muscle Controller
Authors: Akbaş Kübra , Zhou Xianlian 
Published: November 2023

Abstract
For proper balance assessment during a physical rehabilitation program, clinicians typically rely on rubric-oriented battery tests to score a patient’s physical capabilities, which can lead to subjectivity. While approaches have been taken to increase the objectivity of physical assessments and provide more detailed quantifications, many of them tend to be limited to tracking the center of pressure, which can lead to missing information that is critical to the whole-body movement. To this end, the center of mass (COM) state space presents a promising avenue for balance characterization. Previous work has shown various approaches to analyze balance in the COM state space through analytical, computational, and experimental methods. Here, we investigate balance recovery by developing a balance controller for a musculoskeletal model through reinforcement learning (RL). The RL framework is built on top of two neural networks describing the joint trajectory mimicking or targeting and the muscle activation coordinate, which in combination control the musculoskeletal model. The neural network models, i.e., the controller, are learned through the Proximal Policy Algorithm (PPO). The controller is then iteratively tested by imposing random initial states (positions and velocities) to the model and notating the trials with successful balance recovery trajectories. This allows for the construction of a balance region (BR) which describes the potential initial COM states that will yield balanced trajectories. The resulting BR was then compared to the analytical margin of stability determined through a linear inverted pendulum approach, which showed a similar trend in the successful COM states. Overall, the presented balance controller shows a promising new approach to assessing balance in bipedal systems, particularly in humans.