bluuebunny/crossref_metadata_2025_split huggingface dataset:
Modeling of Constrained Variable Mass Systems Based Upon the Boltzmann-Hamel Approach
Authors: Jarzębowska Elżbieta 
Published: November 2024

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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.
Design of a Mass-Manufacturable Passive Prosthetic Foot
Authors: Geil Autumn , Irani Urvaksh , Reddie Madison , Shorter Amanda L. , Winter Amos G. 
Published: November 2024

Abstract
A lack of affordable energy storage and return (ESR) prosthetic feet leads many amputees in low- and middle-income countries (LMIC) to use feet with biomechanical performance inappropriate for their activity levels. To create better options, the GEAR Lab at MIT developed the LLTE design framework, which systematically designs ESR feet to enable replication of able-bodied gait. LLTE-optimized foot prototypes have been tested in a long-term field trial in India and in gait labs in the United States. The feet demonstrated robustness to use for activities of daily living in India, and as good or better biomechanical performance and user satisfaction compared to commercial carbon fiber feet sold in the United States. However, these prototypes were not designed to be commercial products, but rather to demonstrate the viability of the LLTE design framework. This work translates these costly proof-of-concept prototypes to affordable commercial products by making the foot mass-manufacturable and improving user satisfaction via aesthetics, coronal compliance, and a sandal toe. The keel is revised to be compatible with injection molding manufacturing constraints. It is then encased in a polyurethane foam overmold with a ruggedized sole that resembles a biological foot. Commercialization of this foot will offer amputees an affordable ESR option with improved biomechanical performance over currently available options.
Simultaneous Thermal and Electrical Cloaking Via Level-Set-Based Topology Optimization With Isotropic Materials
Authors: Xu Xiaoqiang , Chen Shikui 
Published: November 2024

Abstract
Cloaks are devices designed to conceal objects from detection. With the advancement of metamaterials, there is an increasing interest in developing multi-functional cloaks rather than single-functional ones to cater to various application scenarios. This paper proposes to design simultaneous thermal and electrical cloaking devices via a level-set-based shape and topology optimization scheme. Unlike popular methods such as coordinate transformation and scattering cancellation, which are vulnerable to high material anisotropy, the proposed method only employs naturally occurring bulk materials, which can greatly facilitate physical realization. The bifunctional cloak is achieved by reproducing the reference temperature and electrical potential fields in the evaluation domain through the optimal layout of two thermally and electrically conductive materials in the design domain. Due to the clear boundary characteristic of the level-set formulation, no filtering schemes are required to render a “0-1” design as opposed to density-based counterparts. The feasibility and validity of the proposed method are demonstrated through a number of numerical examples with parallel or orthogonal inflow direction combinations. The dependency of the optimization results on the initial designs is also briefly investigated. Despite exhibiting a notable reliance on the initial guesses, the objective functions based on least square error are sufficiently small to be considered an effective cloak. This work has the potential to spark broader exploration into meta-devices carrying multiple functionalities.
An Order-Free End-to-End Multi-AGV Scheduling Method Based on Safe Deep Q-Network and Asynchronous Coordination
Authors: Qu Jingbo , Li Mian , Wang Tianyu 
Published: November 2024

Abstract
Automated Guided Vehicle (AGV) scheduling is critical to enhance the resource utilization and ensure the timely task completion in the shop floors. Some prior works deal with the AGV scheduling problem through heuristic algorithms. Due to the challenge of accurate mathematical modeling, deep reinforcement learning-based methods have been developed by recent works. However, they mostly require the dispatching orders to be generated in advance, which can be difficult for some end users. The computational efficiency and scalability also have been rarely explored. To tackle these issues, this paper proposes an end-to-end, lightweight and scalable scheduling method relying only on high-level descriptions of the ultimate goal. A Markov decision process formulation is specially designed to encode the studied problem involving extra buffer zone and pallet. Then a lightweight deep Q-network is developed to fuse the multi-source state information and learn optimal policy. Considering the physical constraints, a shielding mechanism is further employed to ensure the accessibility of the learnt policy. Finally, a multi-AGV coordination algorithm is designed to realize joint dispatching operations of multiple AGVs. Experiments on a real shop floor of a large plant show the superiority of the proposed method in terms of the total makespan as well as the scalability towards prevalent changes in the shop floor layout.
Bridging Design Gaps: A Parametric Data Completion Approach With Graph-Guided Diffusion Models
Authors: Zhou Rui , Yuan Chenyang , Permenter Frank , Zhang Yanxia , Arechiga Nikos , Klenk Matt , Ahmed Faez 
Published: November 2024

Abstract
This study introduces a generative imputation model leveraging graph attention networks and tabular diffusion models for completing missing parametric data in engineering designs. This model functions as an AI design co-pilot, providing multiple design options for incomplete designs, which we demonstrate using the bicycle design CAD dataset. Through comparative evaluations, we demonstrate that our model significantly outperforms existing classical methods, such as MissForest, hotDeck, PPCA, and tabular generative method TabCSDI in both the accuracy and diversity of imputation options. Generative modeling also enables a broader exploration of design possibilities, thereby enhancing design decision-making by allowing engineers to explore a variety of design completions. The graph model combines GNNs with the structural information contained in assembly graphs, enabling the model to understand and predict the complex interdependencies between different design parameters. The graph model helps accurately capture and impute complex parametric interdependencies from an assembly graph, which is key for design problems. By learning from an existing dataset of designs, the imputation capability allows the model to act as an intelligent assistant that autocompletes CAD designs based on user-defined partial parametric design, effectively bridging the gap between ideation and realization. The proposed work provides a pathway to not only facilitate informed design decisions but also promote creative exploration in design.
Advancing Vehicle Trajectory Prediction: A Probabilistic Approach Using Combined Sequential Models
Authors: Ren Lichuan , Xi Zhimin 
Published: November 2024

Abstract
In the domain of autonomous vehicle control, the foundation often lies in vehicle dynamics models, which simplify the complex behavior of vehicles to design effective controllers. However, these simplified models can introduce biases, particularly in extreme driving scenarios. Additionally, environmental factors introduce uncertainty into vehicle trajectories. Even with identical control actions, vehicles can display varying behaviors in different scenarios. This paper proposes methods for predicting the probabilistic distribution of a vehicle’s trajectory based on initial conditions and control action sequences. We leverage sequential learning and bias learning techniques to enhance learning efficiency. Our systematic study compares the Seq2Seq model, the particle-based model, and the hybrid model regarding prediction accuracy. The performance of these methods is evaluated and compared using both point error metrics and probabilistic prediction metrics. This research introduces novel approaches to probabilistic modeling of vehicle trajectories by combining physics-based and data-driven models, which can further contribute to the development of more robust and adaptable autonomous vehicle control systems that can navigate complex, uncertain, and extreme driving scenarios.
Multifidelity Topology Design for Turbulent Heat Transfer Problems Using a Darcy Flow Model Incorporating Pseudo-Boundary Layer
Authors: Ohtani Kaito , Yaji Kentaro , Fujita Kikuo 
Published: November 2024

Abstract
Topology optimization for turbulent heat transfer problems is challenging due to the multimodality of the solution space caused by the strong nonlinearity of the physical model. In this study, we utilize the framework of multifidelity topology design (MFTD), which is effective for such computationally hard problems. The framework aims to yield promising design solutions by dividing the original problem into low-fidelity (LF) and high-fidelity (HF). In the first step, we aim to generate exhaustive solutions in the solution space by executing TO using a LF model under a wide variety of optimization conditions. The best design is selected from these solutions by evaluation using a HF model. The performance of the final design depends on whether the LF model can generate promising solutions. Therefore, the construction of an appropriate LF model is critical for the MFTD. In this study, we propose a Darcy flow model incorporating pseudo-boundary layer. This model achieves higher accuracy than the conventional Darcy flow model by considering viscous effects. We use the proposed flow model as a LF model of the MFTD and confirm the effectiveness by showing that promising solutions to turbulent heat transfer problems exist among the various solutions generated by the proposed flow model.