bluuebunny/crossref_metadata_2025 huggingface dataset:
A Digital Self‐Administered Check‐Up Assessment and Evaluation of Risk Communication in Older People
Authors: Schumacher Connie , Pyatt Grace , Mowbray Fabrice , Jones Aaron , Prentice Dawn , Costa Andrew
Published: November 2024

Background

Digital health tools can significantly contribute to the continuum of care and support for persons with their self‐management role. Despite the increasing adoption of digital health tools, little is known about the uptake and comfort of use among older persons.

Methods

A mixed methods design was used to assess the feasibility and utility of the check‐up (CU), a self‐administered digital health assessment for older persons. For the older person, qualitative interviews and de‐identified CU assessment data were collected. A focus group was held to gain the health provider perspective.

Results

A total of 32 online CU assessments were completed, and 17 individuals participated in interviews. Three main themes were identified: digital literacy and accessibility, communication of risk and role within the circle of care.

Conclusion

Self‐administered digital assessments are feasible for older adults. Strategies for adoption should focus on accessibility and collaboration with health professionals to maximise utility and understanding of assessment results.
Survey‐based insights from choice‐based conjoint analyses on customer preferences for company characteristics of life insurers
Authors: Hanika Moritz , Gatzert Nadine
Published: November 2024

The aim of this paper is to investigate customer preferences for certain company characteristics when purchasing life insurance products. We focus on ratings provided by aggregators, namely financial strength, sustainability, service quality, and type of customer touchpoint (online vs. in person), while also studying potential product‐specific differences between annuities and term life policies. Toward this end, we conduct two choice‐based conjoint analyses with 529 and 543 German respondents to estimate individual preference structures. Consistent with economic expectations, we observe statistically significant higher preferences for better ratings of the life insurer, which also materialize in the respondents' marginal willingness to pay. One main finding is that customer preferences in our sample are asymmetric, with a strong focus on avoiding below‐average ratings. Furthermore, for both annuities and term life insurances, the relatively most important life insurer attribute for customers is financial strength, followed by service quality, sustainability, and type of customer touchpoint.
Measuring Beyond the Standard: Informal Measurement Systems as Cognitive Technologies
Authors: Kaaronen Roope O. , Manninen Mikael A. , Eronen Jussi T.
Published: November 2024

This paper explores the role of measurement as a cognitive technology across human history, emphasizing the coexistence of formal and informal measurement systems. While standardized systems dominate contemporary culture and are well documented across large‐scale societies of the past, this manuscript highlights the less explored domain of informal measurement practices that have been integral to daily life from the past to the present. Through the examination of body‐based measurement systems and proportional heuristics, we demonstrate how these informal strategies were not merely precursors to formal standards but essential adaptive tools for solving everyday problems. Often, these informal solutions come with practical advantages. This manuscript calls for a broader recognition of their significance in cultural and technological evolution.
Epistemic modality and evidentiality in virtual intercultural exchanges between Turkish and Texan users of English
Authors: Uzum Melike , Lindahl Kristen , Uzum Baburhan , Yazan Bedrettin , Akayoglu Sedat
Published: November 2024

This study reports on data from two 6‐week virtual intercultural exchanges (VIEs) between teachers of multilingual learners in K‐12 schools in Türkiye and the United States. Using the data from these asynchronous VIEs, we focus on Turkish world Englishes speakers’ use of epistemic markers and evidentials. We examine how participants from Türkiye use markers of epistemic modality and evidentiality in intercultural encounters and how that use influences their construction of cultural information about Türkiye. Our findings show participants selectively code epistemic modality and evidentiality to indicate (their proximity to) the source of cultural information, avoid potential misunderstanding, and claim responsibility for the factuality of the information shared (or not). Our study contributes to understanding how world Englishes and Intercultural Communication intersect in multilingual contact zones that transcend borders―both real and imagined―between cultures, languages, and nations.
Public Engagement and Participatory Design Preparedness: Adapting Participatory Technology Assessment Methods for Design Science
Authors: Owens Jared , Diem Stephanie , Farooque Mahmud , Verma Aditi
Published: November 2024

The relationship over time between knowledge of a design’s performance and the impediments to its revision poses an important, fundamental design challenge. Early stages of design processes are characterized by maximal decision-making flexibility, but choices must be made with minimal knowledge of the impact they will have on operational performance. While knowledge of operational performance characteristics accumulates as a design matures, inherent forces of path dependency and lock-in tied to upstream design decisions increase the difficulty of implementing design changes downstream. To mitigate this challenge, some design processes utilize participatory methods involving early stakeholder engagement to yield a better understanding of the landscape of design expectations, hopes, and concerns. This information can be used to anticipate otherwise unforeseen downstream problems, the occurrence of which are not uncommon in sociotechnical system contexts distinguished by the inherent complexity of interacting social and technical elements. Recognizing the value of early engagement in the design process, this project explores the utility of methods derived from the Participatory Technology Assessment (pTA) methodology in a sociotechnical systems design context; specifically, that of fusion energy systems. We hypothesize that pTA as structured by the Expert and Citizen Assessment of Science and Technology (ECAST) network, originally designed over a decade ago and continually refined in the years since as a multi-phase public engagement methodology for post-normal science and technology policy decision-making support, may be a valuable pre-design tool for increasing participatory design preparedness. As an initial small-scale test of pTA, the project team, consisting of fusion physics, engineering, and pTA experts, conducted two pTA-style open-framing dialogues on energy sources, energy transitions, and fusion energy systems with members of the public recruited from the Madison, Wisconsin area. While preliminary, our findings suggest that pTA methods can be useful for investigating public framings of the greater context around a sociotechnical design challenge; public awareness and understandings of design-relevant technologies; public capacities to comprehend and discuss design-related educational materials; and public expectations, hopes, and concerns with a design’s technologies, all of which we conjecture can be used to refine the structure and content of participatory design engagements. Future experimentation embedding pTA methods in real or simulated design processes may provide highly valuable information for planning and executing participatory design engagements that themselves provide crucial information in the early stages of sociotechnical systems design.
DesignQA: Benchmarking Multimodal Large Language Models on Questions Grounded in Engineering Documentation
Authors: Doris Anna C. , Grandi Daniele , Tomich Ryan , Alam Md Ferdous , Cheong Hyunmin , Ahmed Faez
Published: November 2024

This research introduces DesignQA, a novel benchmark aimed at evaluating the proficiency of multimodal large language models (MLLMs) in comprehending and applying engineering requirements in technical documentation. Developed with a focus on real-world engineering challenges, DesignQA uniquely combines multimodal data — including textual design requirements, CAD images, and engineering drawings — derived from the Formula SAE student competition. Different from many existing MLLM benchmarks, DesignQA contains document-grounded visual questions where the input image and input document come from different sources. The benchmark features automatic evaluation metrics and is divided into segments — Rule Comprehension, Rule Compliance, and Rule Extraction — based on tasks that engineers perform when designing according to requirements. We evaluate state-of-the-art models like GPT4 and LLaVA against the benchmark, and our study uncovers the existing gaps in MLLMs’ abilities to interpret complex engineering documentation. Key findings suggest that while MLLMs demonstrate potential in navigating technical documents, substantial limitations exist, particularly in accurately extracting and applying detailed requirements to engineering designs. This benchmark sets a foundation for future advancements in AI-supported engineering design processes. DesignQA is publicly available at: https://github.com/anniedoris/design_qa/.
Using Design Insights From Customer Feedback to Advance Design for Repairability
Authors: Franz Claire , Saidani Michael , Song Hyeonik
Published: November 2024

Design for repairability is an important design practice to increase the useful life of consumer products and decrease environmental impact. Current design for repairability guidelines include general practices that can be applied to a range of products across industries. However, these guidelines lack device-specific insights. This research proposes a methodology for extracting repairability design insights from customer feedback. This would help repair-conscious designers identify device components that may need redesign and/or prioritize components to offer as replacement parts. In this research, standalone computer keyboards are examined as a case study. This approach includes a repairability keyword list to extract repairability-related phrases from Amazon product reviews. Topic modeling is performed with Non-Negative Matrix Factorization and BERTopic to assess device failure modes for computer keyboards. The repairability-related phrases are used to manually compare the repairability of three different keyboards. The proposed method identifies several failure modes for computer keyboards, such as sticky keys, Bluetooth disconnection, keyboard leg breakage, and instability in the keyboard base. This research indicates that topic modeling is a promising approach for obtaining repairability-related design leads from customer feedback.
Assessing Pedestrian Trust in Autonomous Vehicles: a Comparative Study of Immersive Virtual Reality and Non-Immersive Video Media
Authors: Islam Marufa , She Jinjuan , Chagdes James
Published: November 2024

Trust is crucial for integrating autonomous vehicles (AVs) into society, with various studies examining pedestrian trust through methods like images, texts, videos, virtual reality (VR), and field studies. The impact of these representational methods on trust assessment remains uncertain. Our study conducted a 2 (media types: VR vs. video) by 7 (vehicle behavior trials) within-subject experiment with 50 adults to compare pedestrian trust, risk perception, and comprehensibility in immersive VR and non-immersive video settings. The results showed VR enhanced subjective judgments like trust and risk perception in general, even though participants’ understanding of vehicle behaviors is statistically the same. In addition, representing scenarios in an immersive VR or non-immersive video environment does not influence the relative effectiveness of vehicle behaviors on the trust assessment. The findings indicate the necessity of selecting appropriate media in trust studies depending on the research purpose (e.g., general assessment vs. comparative effectiveness) and validate the use of non-immersive video media to compare trust differences due to vehicle behavior variations. This research provides guidance for future AV-pedestrian trust studies in media type selection, particularly for meta-analyses aiming to consolidate findings from varied research by highlighting the critical role of media choice in trust assessments.
Dynamic Simulation and Collision Detection for Flexible Mechanical Systems With Contact Using the Floating Frame of Reference Formulation
Authors: Dai Xu , Kövecses József , Teichmann Marek
Published: November 2024

Contact simulation is essential in modelling mechanical systems. The contact models require accurate geometric information, which is determined through collision detection methods. When the mechanical system includes flexible bodies such as structural components, the dynamic formulation and collision detection can be more challenging, as the geometric boundaries of such components keep changing during the simulation. The floating frame of reference (FFR) formulation is suitable for flexible systems with small deformation. In this work, a stable and efficient dynamic simulation method is introduced for flexible systems with contact based on the FFR formulation. In addition, a curve-based collision detection method is proposed, which is more consistent with the dynamic formulation and more efficient than common existing collision detection methods. Case studies of flexible beams and multibody systems are employed to demonstrate the performance of the proposed dynamic simulation and collision detection methods.
Effects of Image Uncertainty on Blood Flow Simulation
Authors: Li Huiru , Yu Huidan , Wei Jie , Du Xiaoping
Published: November 2024

Image-based Computational Fluid Dynamics (ICFD) is widely applied for simulating and predicting patient-specific hemodynamics in human blood vessels. This study explores the impact of image input uncertainty from segmentation on ICFD simulation outputs for the iliac stenosis application for individual patients, using patients’ Computed Tomography Angiography imaging data. This investigation includes key components: first, we assess uncertainty in the patient’s three-dimensional (3D) vessel geometry extracted by three image segmentation platforms and multiple different operators, highlighting segmentation-induced variability. Secondly, ICFD simulations are performed to predict flow pressure fields based on random 3D shapes generated from segmentation, illustrating the impact of image uncertainty. To enable statistical image analysis, we develop an alignment method that computes the average 3D shape from a set of sample shapes from segmentation. By transforming complex 3D vessel shapes into coordinate vectors with automatic detection of correspondence points, this method enables the alignment and averaging of multiple 3D shapes. The resulting average 3D shape serves as input for ICFD simulation, generating a flow pressure field that can approximate the average prediction. The findings of this study demonstrate the effects of image uncertainty and the need for new methodologies to quantify these effects. The alignment method proves valuable for statistically analyzing random image data and 3D geometries, providing a useful tool for image statistical analysis.
Construction of 3D-Engineered Glioma Tissues Using the Acoustic Patterning Device
Authors: Du Yingshan , Li Jiali , Shen Liang , Pei Zhe , Cai Bowen , Li Teng , Qiu Chongpeng , Tian Zhenhua
Published: November 2024

Cell patterning techniques play a pivotal role in the development of three-dimensional (3D) engineered tissues, holding significant promise in regenerative medicine, drug screening, and disease research. Current techniques encompass various mechanisms, such as nanoscale topographic patterning, mechanical loading, chemical coating, 3D inkjet printing, electromagnetic fields, and acoustic waves. In this study, we introduce a unique standing bulk waves-based acoustic cell patterning device designed for constructing anisotropic-engineered glioma tissues containing acoustically patterned human glioblastoma cell U251. Our device features two orthogonal pairs of piezoelectric transducers securely mounted on a customized holder. The energy of standing bulk waves generated from these transducers can be transmitted into the medium in a Petri dish through its bottom wall. Cells in the medium can be directed to the local minima of Gor’kov potential fields and trapped by the resultant acoustic radiation force. Through proof-of-concept experiments, we validate the functionality of our acoustic patterning device and assess the morphology and differentiation of U251 cells within the engineered glioma tissues. Our findings reveal that cells can be arranged in different distributions, such as parallel-line-like and lattice-like patterns. Moreover, the aligned cells exhibit more obvious elongation along the cell alignment orientation compared to the result of a control group. We anticipate that this study will catalyze the advancement in contactless cell patterning technologies within tissue engineering, facilitating the development of engineered tissues for applications in regenerative medicine and disease research.
Hydraulic-Mechanical Coupled Simulation of 2D Micromotion in Variable Displacement Vane Pumps for Engine Lubrication
Authors: Rundo Massimo , Squarcini Raffaele
Published: November 2024

Variable displacement vane pumps are widely used for lubrication in internal combustion engines. A key aspect concerns the contact between the vane tip and the stator, both for wear issues and for potential leakages in case of detachment. The purpose of this study is to introduce a simulation methodology based on coupling the lumped parameter hydraulic model and the multibody mechanical model. The principle underlying the method consists of calculating the forces on the moving elements in the hydraulic model and letting the multibody model calculate the displacements of the bodies. The advantage of this approach lies in the assessment of 2D micromovements resulting from clearances of the vanes relative to other mechanical components of the pump core. It allows simulating the detachment of vanes from the stator track, their tilt in the rotor slots, as well as the mutual interaction between vanes through the underneath floating rings. Simulations revealed two distinct behaviors depending on the operating conditions. At low speeds, a strong interaction between the vanes and the rings was observed. In particular, the rings are maintained in an almost constant position by the thrust of the vanes. Conversely, at high speeds, all vanes remain in constant contact with the stator track, and the rings are completely floating. The model is well-suited for enhancing pump design, given its ability to calculate the forces exchanged by the mechanical parts more realistically.
Inspired by AI? A Novel Generative AI System to Assist Conceptual Automotive Design
Authors: Wang Ye , Damen Nicole B. , Gale Thomas , Seo Voho , Shayani Hooman
Published: November 2024

Design inspiration is crucial for establishing the direction of a design as well as evoking feelings and conveying meanings during the conceptual design process. Many practice designers use text-based searches on platforms like Pinterest to gather image ideas, followed by sketching on paper or using digital tools to develop concepts. Emerging generative AI techniques, such as diffusion models, offer a promising avenue to streamline these processes by swiftly generating design concepts based on text and image inspiration inputs, subsequently using the AI generated design concepts as fresh sources of inspiration for further concept development. However, applying these generative AI techniques directly within a design context has challenges. Firstly, generative AI tools may exhibit a bias towards particular styles, resulting in a lack of diversity of design outputs. Secondly, these tools may struggle to grasp the nuanced meanings of texts or images in a design context. Lastly, the lack of integration with established design processes within design teams can result in fragmented use scenarios. Focusing on these challenges, we conducted workshops, surveys, and data augmentation involving teams of experienced automotive designers to investigate their current practices in generating concepts inspired by texts and images, as well as their preferred interaction modes for generative AI systems to support the concept generation workflow. Finally, we developed a novel generative AI system based on diffusion models to assist conceptual automotive design.
Design, Development, and Testing of a Smart Hand Tool: Achieving Work Task Recognition Using Synthetic Data and Edge Intelligence
Authors: New Rachel , Salazar Carlos D. , Bendaña Jose , Sripada V. S. Sundar , Chinchali Sandeep , Fleischmann Kenneth R. , Longoria Raul G.
Published: November 2024

This paper describes research toward developing smart hand tools that leverage artificial intelligence (AI) and sensors for use by human workers. Smart hand tools can provide useful feedback that can benefit human workers, contribute to worker training, and broaden participation in the skilled trade workforce. Specifically, the paper focuses on task recognition. Given the challenges of producing enough training data for machine learning (ML) using data purely from human-based testing, this paper shows how data synthetically-generated by a robot can be leveraged in the ML training process. The paper also demonstrates how fine-tuning ML models for individual physical tasks and workers can significantly scale up the benefits of using ML to provide this feedback. Experimental results show the effectiveness and scalability of this approach, including comparing test data size versus accuracy. In order for smart hand tools of the type introduced here to operate in real-time task recognition, as well as providing analytics on efficient and safe tool usage and operation, ML models need to be deployed ‘on tool’. This paper demonstrates how this can be accomplished by using a tinyML implementation. This paper provides a proof-of-concept for using automated platforms to help train smart tools, which will be essential given the wide range of uses for smart hand tools.
3D Infill Path Generation on Quadric Surfaces for Additive Manufacturing
Authors: Wegner Isaac , Campbell Matthew I.
Published: November 2024

Conventional 3-axis 3D printing has limitations due to the need for supports for certain geometries and in weaknesses along layer lines. Layers for these printers are entirely planar, leading to difficulty in printing overhanging material. The planar structure also allows the weaker inter-layer adhesion to align with stresses, making components prone to failure under certain loading conditions. The recent advent of additional rotational axes on these printers has allowed for a departure from using strictly planar layers, but the software needed to generate these non-planar toolpaths is not nearly as robust as current planar slicers. This paper proposes a method to generate the infill paths for printing on arbitrary quadric surfaces as opposed to only planar surfaces. The method uses coordinate transforms of the layer surface to ensure that toolpaths conform to the interior of a layer perimeter without leaving empty regions within the perimeter interior. This includes proper exclusion of paths within any interior cavities within the outermost perimeters. Two common 3D infill patterns, cubic and gyroid, are implemented and tested. Examples of sliced single layers and complete models are showcased and their structures are compared with those comprised of only planar paths. The viability of extending the method to other patterns in the future is discussed.
Harvesting Energy From Low-Frequency Excitations Using a Rolling Ball With Frequency Up-Conversion
Authors: Kortam Zahwa , Arafa Mustafa
Published: November 2024

This paper presents the design and testing of an electromagnetic device that harvests energy from low-frequency, low-amplitude excitations. The concept is based on a steel ball that acts as an inertial mass that rolls along two parallel guides. Tilting the guides cyclically causes the ball to roll back and forth, magnetically plucking a cantilever beam that is fixed underneath it and carrying a tip magnet. Frequency up-conversion is achieved as the beam vibrates at its natural frequency, which is higher than the excitation frequency, and this is employed to harvest energy by electromagnetic induction as the tip magnet moves relative to a stationary coil. Experimental testing at frequencies ranging from 0.075 Hz – 1.125 Hz with tilt angles ranging from 1.8° – 5.7° demonstrate the effectiveness of the device in harvesting energy from low-frequency, low-amplitude tilting excitations. The experiments are used to gain insight into the frequency up-conversion mechanism and its characteristics. The direction of the ball’s rolling was observed to affect the output voltage magnitude, and the experiments were used to determine the most favorable frequencies, drive conditions as well as limitations of the proposed concept, as plucking the cantilever beam essentially draws from the kinetic energy of the ball. The device was also used to charge a capacitor through a rectifying circuit to validate the concept. Potential applications include utilizing the concept in low-frequency environments, such as human motion or the vibration of tree branches.
Analysis of Contact Points on Guidewires in Neurointerventional Surgery Using U-Net-Based Shape Reconstruction
Authors: Xu Yang , Rana Mahad , Zhou Maggie , Mangla Sundeep , Shi Yong
Published: November 2024

A study is currently underway to enhance a robotic system tailored for assisting surgeons during neuroendovascular interventions. This system relies on a guidewire to navigate through the vascular artery network. However, due to the guidewire’s high flexibility, locating the contact points on it poses a challenge, which will lead to safety concerns and imprecise navigation outcomes for the robotic system. To address this issue, this research proposes a shape-based force estimation method to precisely locate the contact points on the guidewire. This method uses the guidewire’s contour in the image as the shape information and the guidewire’s mechanical property to establish the relationship between shape change and applied loads. To extract the guidewire’s contour from the background, a convolutional neural network, specifically a U-Net model, was employed. Ninety-six images were used for the U-Net model training. The U-Net model achieved a Dice score of 82.8%, indicating its effectiveness in contour extraction. Based on the extracted contour, a thinning method was used to extract the mid-line of the guidewire, which can be used to calculate the curvature of the guidewire. By analyzing the curvature change, the localized maximum value of the curvature indicated the location of the applied load. An experimental validation of the proposed method was conducted. The relative error of the load location estimation was approximately 6.5%, demonstrating the accuracy of the proposed approach.
Mechanical Design of a Novel Robotic Gripper for Fixed Automation Applications
Authors: Jones Hunter , Slaboch Brian J.
Published: November 2024

This paper presents an industrial robotic gripper for high-volume, fixed automation applications. The gripper is unique in that it can both grasp an object and retract the object using a single electric motor. Once the object is grasped and retracted, it can also be extended and released by reversing the direction of the motor. The new gripper is driven by a single cam mechanism with two cam profiles that control both the extension and retraction of the mechanism as well as the opening and closing of the gripper fingers. The cam driven approach includes an extension spring that allows the gripper fingers to automatically grasp and release different sized objects. This paper presents the new robotic gripper, including a discussion of its design principles. The advantage of this mechanism is that it only needs one actuator to complete a high-volume fixed automation task that usually requires two actuators. Thus, the proposed gripper design has the potential to have lower power consumption and improved energy efficiency when compared to conventional approaches that use some combination of pneumatic and electric actuators.
Human-Artificial Intelligence Teaming for Scientific Information Extraction From Data-Driven Additive Manufacturing Research Using Large Language Models
Authors: Safdar Mutahar , Xie Jiarui , Mircea Andrei , Zhao Yaoyao Fiona
Published: November 2024

Data-driven research in Additive Manufacturing (AM) has gained significant success in recent years. This has led to a plethora of scientific literature to emerge. The knowledge in these works consists of AM and Artificial Intelligence (AI) contexts that haven’t been mined and formalized in an integrated way. It requires substantial effort and time to extract scientific information from these works. AM domain experts have contributed over two dozen review papers to summarize these works. However, information specific to AM and AI contexts still requires manual effort to extract. The recent success of foundation models such as BERT (Bidirectional Encoder Representations for Transformers) or GPT (Generative Pre-trained Transformers) on textual data has opened the possibility of expediting scientific information extraction. We propose a framework that enables collaboration between AM and AI experts to continuously extract scientific information from data-driven AM literature. A demonstration tool is implemented based on the proposed framework and a case study is conducted to extract information relevant to the datasets, modeling, sensing, and AM system categories. We show the ability of LLMs (Large Language Models) to expedite the extraction of relevant information from data-driven AM literature. In the future, the framework can be used to extract information from the broader design and manufacturing literature in the engineering discipline.
Enhancements to a Topology Optimization Interpreter for Features Common in Aircraft Structures
Authors: Myszka David H. , Studnicka Jack A. , Murray Andrew P. , Joo James J.
Published: November 2024

This paper describes enhancements made to a topology optimization interpreter to specifically address features that are common in aircraft structures. Structural topology optimization is a computational method that distributes material within a design domain to achieve a structurally efficient layout. Topology optimization is particularly attractive for the design of aircraft structures, where low weight and high stiffness are essential criteria. Yet, a barrier to the broad implementation of topology optimization is that the results are not typically in a form that is readily manufactured. Consistent with a well-known understanding that trusses are efficient structures, many topology optimization results resemble a spaceframe structure. To create a manufacturable design, an interpreter has previously been developed to automatically convert topology optimization results into a producible welded-tube spaceframe. That interpreter accepts the optimization results file, uses image processing techniques to decipher a matching spaceframe, and creates a computer-aided-design model.

This paper describes the enhancements made to the interpreter that were made to explicitly address characteristics that arise with air vehicle applications. Specifically, air frames support a curved outer skin that mostly requires a hollow core. Straight frame members are prone to violate the structure’s design domain. Additionally, aircraft often exhibit symmetric geometry and loading. To properly address these circumstances, the results interpreter is expanded to provide the capabilities to generate curved members and accommodate symmetry. The enhancements to the interpreter are demonstrated on the frame of an aircraft with a bio-inspired, rotating empennage.
Innovating Emitter Inlet Design for Enhanced Clog Resistance and Longevity in Drip Irrigation Systems
Authors: Huynh Christy , Molina De Jesus Andrea M. , Sams Sarah A. , Weizer Benjamin T. , Zaza Nadine , Ghodgaonkar Aditya A. , Judge Benjamin M. , Winter Amos G.
Published: November 2024

This study addresses the critical issue of emitter clogging in drip irrigation systems, a prevalent challenge affecting their efficiency and lifespan. The emitter inlet functions as the gateway between the drip line and the inner hydraulics of the emitter, making it an ideal location to filter out particles from the water flow-path. We developed a novel emitter inlet design focused on enhancing clog resistance, inspired by natural hydraulic and filtration processes such as those found in aquatic gills, wake formation, and nozzle principles. The design was analyzed using Computational Fluid Dynamics (CFD) in ANSYS Dynamics Simulation Software, enabling detailed modeling of fluid-surface interactions. Experimental clog testing was conducted to assess the inlet’s clog resistance. Our findings demonstrate a substantial improvement in clog resistance with our design, maintaining 85 ± 28% of initial flow rates after exposure to 150 micron diameter aluminum oxide grit, significantly better than a comparable industry-standard design. After exposure to 425 micron diameter grit and a system restart, our design maintained 91 ± 53% of the initial flow rate, showcasing enhanced recovery performance. This research offers a promising solution to the challenge of irrigation system clogging, with implications for better water conservation and more sustainable agricultural practices.
Modeling of Constrained Variable Mass Systems Based Upon the Boltzmann-Hamel Approach
Authors: Jarzębowska Elżbieta
Published: November 2024

The paper details derivation of the Boltzmann-Hamel equations modified to encompass variable mass and inertia systems that are constrained by holonomic or nonholonomic constraints. The motivations to modify the original equations derived for unconstrained constant mass systems are the equations advantages in modeling moving systems, i.e. they can be derived in body fixed frames, the modeling parameters are quasi-velocities that can be selected arbitrarily and as such can satisfy the constraint equations. Also, there is a need for obtaining unified dynamics and control frameworks for variable mass systems, e.g. an inertia-propelled underwater vehicle, which illustrates the theoretical development in the paper. The vehicle is propelled by the change of mass in its water tanks and motion of a movable mass enables maneuverings. Also, the vehicle may be constrained by kinematic or programmed constraints. The typical approach to modeling of variable mass systems is based upon the Newton-Euler or Lagrange constant mass systems approaches, in which the change of mass is additionally added. The general dynamics framework for variable mass and inertia systems developed in the paper is based upon the Boltzmann-Hamel equations. It is free of the constraint reaction forces and can be directly apply to generate dynamics control models. The Boltzmann-Hamel equations are applied to prove its effectiveness in analyses of motion performance of an underwater inertia-propelled vehicle model.
The Carbon Intensity of Generative Design: Emissions Analysis of Training and Sampling From Generative Models
Authors: Wilson Sara L. , Bagazinski Noah J. , Yang Maria C.
Published: November 2024

This research examines the environmental impact of artificial intelligence (AI) in manufacturing, focusing on the energy consumption and emissions from AI technologies. While computer vision can enhance production efficiency and minimize waste by analyzing data, its deployment is not without environmental costs, primarily due to the energy-intensive processes of training and operating AI systems. Existing methodologies for evaluating AI’s carbon footprint predominantly cover emissions during model training, neglecting the significant emissions from sampling — generating data from trained models. Our study bridges this gap by developing a carbon accounting method for training and sampling emissions, utilizing the carbon emissions of diffusion models as a surrogate for those of computer vision in the manufacturing sector. We introduce novel contributions by characterizing the emissions of generative models in terms of fixed and variable costs, akin to manufacturing terminologies. Our findings reveal that image-based diffusion models produce 772x more carbon emissions per sample compared to tabular models with a similar number of parameters. Moreover, for small models, emissions are dominated by sampling, highlighting the necessity of quantifying sampling emissions for sustainable applications of AI. This comprehensive approach to quantifying AI’s environmental footprint is crucial for ensuring that the benefits of AI in manufacturing substantially outweigh its energy and resource costs.
Can Unconscious Verbal Mimicry Measure Empathy in Design?
Authors: Fabunmi Oluwatoba , Halgamuge Saman , Hölttä-Otto Katja
Published: November 2024

Developing empathy towards a user is a vital part of user-centered design. Several approaches developed in psychology, sociology, and neuroscience have been adopted in measuring empathy in design. However, these approaches have limitations, such as subjective bias, time consumption, the need for specialized equipment, and expensive laboratories. To address these shortcomings, there is a need for a quick, easy-to-implement, and automatic measure of empathy in design. We explore empathy measurement using transcripts from a user interview. More specifically, we explored whether language style matching (LSM), a measure of unconscious verbal mimicry, can be an indicator of empathic mental processes. We further investigated its relationship with the designer’s empathic understanding of the user and the emotion similarity between the designer and the user. The results show that verbal mimicry exists between the designer and the user. However, this mimicry, as detected with LSM was not correlated with empathic understanding accuracy. Instead, we found that LSM has a significant correlation with the similarity between the designer’s and the user’s expressed emotions during the interview. Verbal mimicry using LSM shows the potential to measure the designer’s empathic understanding of the user, which is both cognitive and affective. Further research should explore other measures of empathic understanding.
Development of a Compact Hybrid Gripper for Automated Harvesting of White Button Mushroom
Authors: Koirala Bikram , Shen Guoyang , Nguyen Huy Canh , Kang Jiming , Zakeri Abdollah , Balan Venkatesh , Merchant Fatima , Benhaddou Driss , Zhu Weihang
Published: November 2024

Automated harvesting of white button mushroom (Agaricus bisporus) has gained global popularity in recent times aiming to enhance harvesting efficiency while reducing the reliance on human labors. Mushrooms are typically growing on multi-layer Dutch shelves with limited space between every two layers. This requires mushroom grippers to be compact and work in a narrow space. End effectors used in automated harvesting have mostly been pressure suction cups or finger-like soft grippers. The reported grippers are oversized, bulky, and slow, making them unsuitable for commercial harvesting where mushrooms grow on narrow shelves. Furthermore, there has been noticeable absence of analysis on the gripper tailored specifically for cluster mushroom harvesting. The gripper development is a critical step towards fully automated mushroom harvesting. This paper reports the development of a compact hybrid gripper and the harvesting performance on single mushrooms as well as clustered mushrooms. The effect of gripping force on different sizes of mushroom has been analyzed. The safe picking force was found to be 5N for single grown mushroom. For both single and clustered mushrooms, the success rate of picking was 100% with average picking times of 3s and 5.5s, respectively. However, picking clustered mushroom was associated with a high damage rate, which calls for further enhancement.
Towards a Multi-User Experience Data-Driven Design Framework for Sustainable Smart Take-Away Food Packaging
Authors: Camilleri Tamasine , Farrugia Philip , Refalo Paul , Bugeja Marvin
Published: November 2024

Smart packaging transcends traditional packaging’s basic functions by meeting emerging consumer expectations for quality and sustainability. COVID-19 has notably increased take-away orders, spotlighting the need for sustainable food packaging. By incorporating smart features, packaging usability is enhanced, and the likelihood of disposal due to obsolescence is reduced. Moreover, smart packaging acceptance relies on consumer values. To address these evolving needs, designers are encouraged to embrace a user-centered design (UCD) approach to develop sustainable smart take-away packaging. However, a review of literature reveals a gap in design support systems that could assist designers in this endeavor. Interviews with packaging designers outlined framework requirements, leading to the creation of SUSTAIN-MULTI-UCD. This framework advocates for a multi-UCD approach tailored for sustainable smart take-away packaging development. The innovative use of big data analytics in this context enables gathering diverse user data to help enhance user experience. The SUSTAIN-MULTI-UCD contribution lies in applying big data analytics to the data-driven UCD of sustainable smart take-away packaging, offering insights that could be beneficial across various food packaging applications. Furthermore, innovations in the framework’s data-driven approaches, such as the application of topic modelling and text classification, identifies user requirements, and furthermore guides the designer on how to meet these needs.
Automatic Feature Recognition and Dimensional Attributes Extraction From CAD Models for Hybrid Additive-Subtractive Manufacturing
Authors: Khan Muhammad Tayyab , Feng Wenhe , Chen Lequn , Ng Ye Han , Tan Nicholas Yew Jin , Moon Seung Ki
Published: November 2024

The integration of Computer-Aided Design (CAD), Computer-Aided Process Planning (CAPP), and Computer-Aided Manufacturing (CAM) plays a crucial role in modern manufacturing, facilitating seamless transitions from digital designs to physical products. However, a significant challenge within this integration is the Automatic Feature Recognition (AFR) of CAD models, especially in the context of hybrid manufacturing that combines subtractive and additive manufacturing processes. Traditional AFR methods, focused mainly on the identification of subtractive (machined) features including holes, fillets, chamfers, pockets, and slots, fail to recognize features pertinent to additive manufacturing. Furthermore, the traditional methods fall short in accurately extracting geometric dimensions and orientations, which are key factors for effective manufacturing process planning. This paper presents a novel approach for creating a synthetic CAD dataset that encompasses features relevant to both additive and subtractive machining through Python Open Cascade. The Hierarchical Graph Convolutional Neural Network (HGCNN) model is implemented to accurately identify the composite additive-subtractive features within the synthetic CAD dataset. The proposed model demonstrates remarkable feature recognition accuracy of around 97% and a dimension extraction accuracy of 100% for identified features. Therefore, the proposed methodology enhances the integration of CAD, CAPP, and CAM within hybrid manufacturing by providing precise feature recognition and dimension extraction. It facilitates improved manufacturing process planning by enabling more informed decision-making. The key novelty and contribution of the proposed methodology lie in its ability to recognize a wide range of manufacturing features and to precisely extract their dimensions, orientations, and stock sizes.
Influence of Root Clearance on Helical Gear Pocketing Power Loss
Authors: McDonald Kyler , Hong Isaac , Talbot David , Kahraman Ahmet
Published: November 2024

An experimental study is conducted to investigate individual contributions of components to the total spin power loss of a high-speed, gearbox operating under realistic lubricant flow conditions. An experimental test setup capable of operating the test gearbox containing helical gears at desired speeds, temperatures and lubrication conditions is designed and fabricated. Dedicated measurement systems to monitor bearing and oil temperatures as well as the torque loss of the gearbox are devised, and accompanying data analysis systems are developed. A test matrix consisting of two different lubricant flow conditions is defined, each covering a wide range of input speed. These tests are performed with the gearbox having variations of hardware content to separate gear drag, gear mesh pocketing, and bearing power losses from each other. These tests show that the power losses increase with increased lubricant flow rates. They further demonstrate that all three power losses are comparable to each other, and hence, must be considered in evaluating spin power loss of such systems. A parametric study is conducted to investigate the effect of the trapped volume in the gear mesh on pocketing power loss. A family of gears having different addendum coefficients are meshed with the same gear forming different root clearance values. The results of these tests are compared to quantify the influence of root clearance on resultant pocketing power loss.
A Structural Re-Parameterization Network for Bearing Fault Diagnosis Under Variable Working Conditions
Authors: Hua Zehui , Dumond Patrick
Published: November 2024

Deep convolutional networks are well known for their strong performance in feature characterization of massive data. Performing intelligent fault diagnosis (IFD) by training an end-to-end model could facilitate the acquisition of health state information of key machinery components while avoiding the need for human intervention and expert knowledge. However, for traditional machine learning, most methods are proposed and implemented based on the assumption that known data collected from the source domain and unknown data collected from the target domain share the same feature distribution, which can hardly be satisfied due to limitations in real industrial applications, as well as variable working conditions. To address this problem, a structural re-parameterization network is proposed, which is used to automatically extract deeper features and minimize the domain discrepancy between the source and target domain by using both metric learning and adversarial learning strategies. To be specific, an initial multi-branched network including shortcut structure is first established. This network enhances feature extraction by fusing features from multiple branches and aligning data when performing IFD under variable working conditions. After completing the training phase, the network can be re-parameterized during the inference stage, transitioning into a single-branched network while maintaining identical results, which is much more computationally efficient. Experiments have shown the effectiveness of the proposed method.