bluuebunny/crossref_metadata_2025_split huggingface dataset:
Evolutionary Co-Mention Network Analysis via Social Media Mining
Authors: Gavino Phillip A. O. , Xiao Yinshuang , Cui Yaxin , Chen Wei , Sha Zhenghui 
Published: November 2023

Abstract
The immense volume of user-generated content on social media provides a rich data source for big data research. Comentioned entities in social media content offer valuable information that can support a broad range of studies, from product market competition to dynamic social network mining and modeling. This paper introduces a new approach that combines named entity recognition (NER) and network modeling to extract and analyze co-mention relationships among entities in the same domain from unstructured social media data. This approach contributes to design for market systems literature because little research has investigated product competition via co-mention networks using large-scale unstructured social media data. In particular, the proposed approach provides designers with a new way to gain insight into market trends and aggregated customer preferences when customer choice data is insufficient. Moreover, our approach can easily support the evolution analysis of co-mention relationships beyond cross-sectional analysis of co-mention networks in a single year due to the abundance of social media data in multiple years. To demonstrate the approach to supporting multi-year product competition analysis, we perform a case study on mining co-mention networks of car models with Twitter data. The result shows that our approach can successfully extract the co-mention relationships of car models in multiple years from 2016 to 2019 from massive Twitter content; and enables us to conduct evolutionary co-mention network analysis with temporal network modeling and descriptive network analysis. The analysis confirmed that the co-mention network is capable of identifying frequently discussed entities and topics, such as car model pairs that often involve in competition and emerging vehicle technologies such as electric vehicles (EV). Furthermore, conducting evolutionary co-mention network analysis provides designers with an efficient way to monitor shifts in customer preferences for car features and to track trends in public discussions such as environmental issues associated with EVs over time. Our approach can be generally applied to other studies on co-mention relationships between entities, such as emerging technologies, cellphones, and political figures.
Projected Barycentric Densities for Multi-Material Topology Optimization Using Shape Function With Penalization Approach
Authors: Singh Nikhil , Saxena Anupam 
Published: November 2023

Abstract
The paper proposes and implements modifications to the Shape Function with Penalization (SFP) approach to solve multi-material topology optimization (MMTO) problems. The SFP approach employs barycentric shape functions from the Finite Element literature, to evaluate material densities allowing one to solve MMTO requiring two or three variables per cell. Barycentricity, i.e., non-negativity and partition of unity amongst densities is the key for a cell to be assigned one of the specified materials and therefore no other. We show that applying filtering to SFP evaluated material densities leads to barycentric filtered density fields. Applying projection further to these filtered fields leads to loss of this property. We demonstrate next that applying filtering and projection to the design variables directly helps retain barycentricity in the resulting material densities. The corresponding MMTO problem formulation is developed followed by sensitivity analysis. The obtained formulation is applied on some well known topology optimization problems demonstrating the method’s ability to yield solutions with up to seven materials employing only 3 design variables per cell. Solutions elucidate that the modified projection operator successfully generates crisp transition regions between material (and void) phases.
Adaptation and Challenges in Human-AI Partnership for the Design of Complex Engineering Systems
Authors: Xu Zeda , Hong Chloe , Soria Zurita Nicolás F. , Gyory Joshua T. , Stump Gary , Nolte Hannah , Cagan Jonathan , McComb Christopher 
Published: November 2023

Abstract
Exploring the opportunities for incorporating Artificial Intelligence (AI) to support team problem solving has been the focus of intensive ongoing research. However, while the incorporation of such AI tools into human team problem solving can improve team performance, it is still unclear what modality of AI integration will lead to a genuine human-AI partnership capable of mimicking the dynamic adaptability of humans. This work unites human designers with AI Partners as fellow team members who can both reactively and proactively collaborate in real-time towards solving a complex and evolving engineering problem. Team performance and problem-solving behaviors are examined using the HyForm collaborative research platform. The problem constraints are unexpectedly changed midway through problem solving to simulate the nature of dynamically evolving engineering problems. This work shows that after the shock is introduced, human-AI hybrid teams perform similarly to human teams, demonstrating the capability of AI Partners to adapt to unexpected events. Nonetheless, hybrid teams do struggle more with coordination and communication after the shock is introduced. Overall, this work demonstrates that these AI design Partners can participate as active partners within human teams during a large, complex task, showing promise for future integration in practice.
Investigate Customer Preferences Using Online Video Reviews - Preliminary Results
Authors: Lin Kangcheng , Kim Harrison 
Published: November 2023

Abstract
The wealth of online reviews has grown exponentially, attracting the attention of many researchers who recognize their potential as a valuable source of customer feedback. Leveraging online reviews enables product designers to gain a deeper understanding of customer preferences and make informed decisions to improve product design. Traditionally, the major source of online reviews comes from e-commerce websites, such as Amazon and eBay. However, as social media platforms gain popularity, video reviews from these platforms are becoming an increasingly important source of customer feedback. Video reviews have several advantages over traditional textual reviews. They are more comprehensive, less likely to be fake, have greater coverage of the customer base, and contain more interaction in the form of comments. The paper presents a four-stage methodology to analyze video reviews from social media platforms as an alternative source of customer feedback. This involves collecting and preprocessing video reviews, extracting product features using latent Dirichlet allocation (LDA) models, analyzing sentiment using the valence aware dictionary and sentiment reasoner (VADER) package, and computing feature importance using SHAP values. To the best knowledge of the authors, very few literatures have investigated the feasibility of using video reviews as a substitute for traditional online reviews. Therefore, this paper contributes to the growing literature by demonstrating the viability of video reviews as an alternative source of online reviews to understand customer preferences and their implications for engineering design.
Application of the Ellipse of Elasticity to an Hybrid-Chain Compliant Mechanism
Authors: Sorgonà Orazio , Giannini Oliviero , Verotti Matteo 
Published: November 2023

Abstract
In the last decades, the development of compliant mechanisms has been constantly increasing and these systems have found applications in numerous engineering fields. In line with this growth, many efforts have been made to define analytical procedures and design strategies for modeling their deflections. In this investigation, the theory of the ellipse of elasticity is applied to the linear kinetostatic analysis of a compliant mechanism. The proposed method takes advantage of the properties of antiprojective polarity transformations with respect to the conic associated to the flexible system. The main advantage of the approach lies on the fact that the deflection analysis problem is reduced to a geometric problem with straightforward solution. The compliant mechanism under study is designed considering flexures with different geometric features, such as initial or variable curvature, and non-uniform cross-section. From a kinematic point of view, the mechanism is obtained from a hybrid chain composed of two closed chains and of one open chain. The ellipse of elasticity associated to the compliant system is determined as a composition of sub-chains in series and parallel arrangements. The kinetostatic analysis of the compliant mechanism is therefore performed by exploiting the antiprojective properties related to the ellipse, leading to the definition of the field of displacements of the end-effector.
Design and Analysis of a Series-Parallel Hybrid 3-S

P

S-U Mechanism
Authors: Venkateswaran Swaminath , Chablat Damien , Creusot Denis 
Published: November 2023

Abstract
This article presents the design and analysis of a seriesparallel hybrid tensegrity mechanism. The mechanism has two stages which consist of a fixed base, an intermediate mobile platform, and a mobile end-effector. Each stage is connected by three tension springs and a universal joint in the center. By correlating to a 3-SPS-U architecture, the geometrical equations for the mechanism are generated in the Euler space and the Tilt & Torsion space. To simplify the computations, the Tilt & Torsion space is employed for the analysis of the mechanism. A stability analysis is carried out initially to identify the design parameters of the mechanism in the static mode through an optimization approach. By employing algebraic methods, the singularity analysis is then carried out on the hybrid mechanism. The results of this analysis helped in identifying the feasible workspace and the maximum tilt limits of the architecture. A mapping equation that demonstrates the relation between the Tilt & Torsion angles and the Euler angles is then presented using the results of singularity analysis. A numerical simulation is then demonstrated to validate the results of the analysis. The mechanism under study is then proposed to be integrated into a piping inspection robot for passing through elbows and T-sections.
Gathering and Communicating Empathic User Understanding in Product Development
Authors: Borgman Teresia , Hölttä-Otto Katja , Hyvärinen Jaana 
Published: November 2023

Abstract
Empathic design has been shown to be beneficial for finding users’ latent needs in the early stages of product development. It is useful in gaining a deep understanding of the users’ thoughts, feelings, and behavior. While there is significant research on empathy, less attention has been given to the communication of the findings onwards to the rest of the development team. Proper communication is essential, given that in most projects, the product development team is too large to be involved directly with the user. Further, for some engineering systems, like the one studied here, the end user may not always be the direct customer. This paper examines how user understanding is currently gathered and communicated in a global engineering and service company to identify the current methods and what challenges they face. A thematic analysis is conducted on ten interviews with employees involved either in gathering or receiving user information in product development. The results show that the main communication challenges were caused by lack of understanding toward colleagues due to e.g., their different disciplinary backgrounds, lack of common time and the number of handovers of empathic information. Another issue was the lack of a proper way of storing and finding the user information from past projects.
Modeling the Dynamics of Customer Demand to Determine the Optimal Time to Release Product Updates: A Cognitive Approach
Authors: Walter Ian , Paré Philip E. , Panchal Jitesh H. 
Published: November 2023

Abstract
The nature of modern products is changing, and design processes need to change accordingly. As products become increasingly software-enabled and with the rise of software-as-a-service models, customer-facing updates can be released more frequently than was previously possible. The development pipelines for software-enabled products are shifting towards agile processes, which emphasize regular product updates, choosing the timing of these updates is important, and requires an understanding of how demand will be influenced by each release. However, the existing discrete-choice models used to predict demand do not capture the dynamic aspects of consumer decision making. To address this gap, we propose a dynamic demand model and demonstrate how it can be used to determine the optimal time to release product updates. The demand model is based on decision field theory (DFT), which enables the modeling of the dynamic and time-varying behavior of human decision makers. The primary contribution in this paper is a product strategy for update release schedules which accounts for the dynamic and time-varying behavior of potential customers. We demonstrate the method using simulations of multiple scenarios.
Digital Twin Based Development of Mobile Robot Assistant in Wind Turbines Manufacturing
Authors: Malik Ali Ahmad 
Published: November 2023

Abstract
The thrust for increased rating capacity of wind turbines has resulted in larger generators, longer blades, and taller towers. Presently, up to 16MW wind turbines are available in the market which is nearly a 60% increase in the design capacity over the last five years. Manufacturing of these turbines involves assembling gigantic-sized components. Due to the frequent design changes and the variety of tasks involved, conventional automation is not feasible, making it a labor-intensive activity. However, the handling and assembling of large components are challenging human capabilities. Additionally, it results in longer lead times and increased overall cost of sustainable energy. The article proposes the use of mobile robotic assistants for the hybrid automation of wind turbine manufacturing. The robotic assistant can result in reduced production costs, improved product quality, and better work conditions. The article presents the development of a robot assistant for human operators to effectively perform the assembly of wind turbines. The technology of the digital twin is explored for the commissioning and reconfiguration of the robot assistant. The digital twin also helped in speeding up the design and validation of the robot device. The case of the world’s leading wind turbine manufacturer is studied. The developed system is also applicable to other cases of large component manufacturing involving intensive manual effort.
Prediction of Human Reaching Pose Sequences in Human-Robot Collaboration
Authors: Flowers Jared , Wiens Gloria 
Published: November 2023

Abstract
In Human-Robot Collaboration (HRC), robots and humans must work together in shared, overlapping, workspaces to accomplish tasks. If human and robot motion can be coordinated, then collisions between robot and human can seamlessly be avoided without requiring either of them to stop work. A key part of this coordination is anticipating humans’ future motion so robot motion can be adapted proactively. In this work, a generative neural network predicts a multi-step sequence of human poses for tabletop reaching motions. The multi-step sequence is mapped to a time-series based on a human speed versus motion distance model. The input to the network is the human’s reaching target relative to current pelvis location combined with current human pose. A dataset was generated of human motions to reach various positions on or above the table in front of the human starting from a wide variety of initial human poses. After training the network, experiments showed that the predicted sequences generated by this method matched the actual recordings of human motion within an L2 joint error of 7.6 cm and L2 link roll-pitch-yaw error of 0.301 radians on average. This method predicts motion for an entire reach motion without suffering from the exponential propagation of prediction error that limits the horizon of prior works.
Electronics Design and Verification for Robots With Actuation and Sensing Requirements
Authors: Chen Dongsheng , Huang Zonghao , Sung Cynthia 
Published: November 2023

Abstract
Robot design is a challenging problem involving a balance between the robot’s mechanical design, kinematic structure, and actuation and sensing capabilities. Recent work in computational robot design has focused on mechanical design while assuming that the given actuators are sufficient for the task. At the same time, existing electronics design tools ignore the physical requirements of the actuators and sensors in the circuit. In this paper, we present the first system that closes the loop between the two, incorporating a robot’s mechanical requirements into its circuit design process. We show that the problem can be solved using an iterative search consisting of two parts. First, a dynamic simulator converts the mechanical design and the given task into concrete actuation and sensing requirements. Second, a circuit generator executes a branch-and-bound search to convert the design requirements into a feasible electronic design. The system iterates through both of these steps, a process that is sometimes required since the electronics components add mass that may affect the robot’s design requirements. We demonstrate this approach on two examples — a manipulator and a quadruped — showing in both cases that the system is able to generate a valid electronics design.
A Comparison of Bayesian Acquisition Functions for Use In Surrogate Multi-Objective Feasibility Robust Optimization With Interval Uncertainty
Authors: Kania Randall J. , Azarm Shapour 
Published: November 2023

Abstract
Engineering design optimization problems are commonly multi-objective, involve computationally expensive simulations, and have uncertainty. In these problems, consideration of uncertainty can produce feasibly robust optimized design solutions, i.e., optimized and feasible under uncertainty. In general, methods for solving multi-objective robust optimization problems can be computationally expensive. To ease the computational burden and make these methods more accessible, a surrogate-based bi-objective Bayesian feasibility robust optimization method from the literature has been extended in this paper to higher numbers of objectives. A key component of the proposed method is a new acquisition function. The acquisition function proposed in this paper uses a Monte Carlo approach to approximate the expected centroidal improvement of the solution set. The proposed acquisition function is compared against exact (closed form) acquisition function-based approaches and other methods from the literature. The experimental results from this study suggest the multi-objective robust optimization framework is insensitive to the selection of acquisition function.
Self-Supervised Learning of Temporally Varying Process Parameter Models for Direct Ink Writing
Authors: Yoon Yeo Jung , Yang Yang , Gupta Satyandra K. 
Published: November 2023

Abstract
Direct ink writing (DIW) is an additive manufacturing (AM) process known for its flexibility in printing a variety of materials. However, ink drying is one of the common issues in DIW process. Time-dependent changes in ink properties require us to use a temporally varying process parameter models to efficiently utilize ink. To address this problem, we propose a learning approach for constructing reliable process parameter models that compensate for the temporal changes in ink properties. In the study, we use a mixture of silicones as the ink and a robotic arm with a fluid dispenser system as the experimental setup. We begin by printing test artifacts and collecting initial data with various combinations of process parameters. This data is then used to construct surrogate models for the DIW process and estimate the proper ranges of process parameters to use over time. With self-supervised learning approach and the process parameter adjustment, our method maximizes ink utilization. Additionally, we apply image processing techniques to analyze the printed artifacts. Our results demonstrate the accuracy of the models, compare the DIW process outcomes for actual artifacts with and without temporal adjustment, and estimate the feasible duration for successful printing.
A Conceptual Design of Natural Gas Downhole Drilling and Production Equipment on RFKD Model
Authors: Jiang Yiwei , Zhao Wu , Guo Xin , Zhang Kai , Yu Miao , Hu Huicong 
Published: November 2023

Abstract
The stable operation of natural gas downhole drilling and production equipment is an important guarantee for the gradual transition to sustainable energy in the future. Since the equipment is in the environment of high-pressure natural gas impact, continuous change of gas pressure, and sand-gas mixing, it has higher requirements on reliability. Therefore, in the conceptual design stage, how to complete the solution process from requirement constraints to scheme configuration, and design high-reliability downhole equipment is an important part. New challenges are presented in the designer’s understanding of equipment functions and constraints and completing functional knowledge configuration when designing such stable-performance equipment. To solve this problem, a requirement function knowledge deployment (RFKD) model for equipment conceptual design is extended. Through this model, the conflicts faced by the main function of the equipment are obtained and solved, and the knowledge constraint rules that more accurately match the main function are proposed. The other functions of the equipment are standardized and decomposed into sub-functions, and the structured design knowledge is matched with the sub-functions through function-structure. The feature-knowledge mapping method is extended on the RFKD model. In this paper, the extended model is validated through a conceptual design of a multi-stage downhole throttle that can accommodate large pressure differences.
Reducing the Barriers to Designing 3D-Printable Prosthetics In Resource-Constrained Environments
Authors: Lee Junghun , Chesang Andrew , Gichane Michael , Busogi Moise , Byiringiro Jean , Tucker Conrad 
Published: November 2023

Abstract
Prosthetic devices remain unaffordable to many patients in resource-constrained environments. The design process of personalized prosthetic devices is sophisticated and requires specialized equipment and trained professionals who may have limited availability. The situation is further exacerbated by the fact that the resources are frequently situated away from underserved communities, compounding the issue of accessibility. Lowering the barriers to accessing required equipment and minimizing the involvement of trained professionals in designing and manufacturing prosthetic devices can potentially minimize the required visits to the prosthetists and lower the associated costs. Traditional prosthetic and orthotic device creation methods can take up to several months. There are attempts to leverage advanced technologies to enhance the design process, but they require special equipment such as 3D scanners and MRI equipment in addition to the active involvement of specialists.
This work proposes a computer vision method for creating a virtual model of the affected human limb. We also propose an automated approach for generating the socket from the virtual model and scaling other parts of the prosthetic device. The prosthetic device designed through the proposed process is realized via a 3D printer. We present a case study demonstrating the feasibility of our approach, using a mobile phone and open-source software to reconstruct a virtual representation of an upper limb and customize a prosthetic hand with minimal input from a specialist.