bluuebunny/crossref_metadata_2025_split huggingface dataset:
Automated Functional Basis Modeling Using Multi-Label Machine Learning on 3D Models
Authors: Connor Zachariah A. , Sable Shital , Campbell Matthew I. , Stone Robert B. 
Published: November 2024

Abstract
Recent progress in neural network-based object detection algorithms allows engineers to automatically detect and track objects using camera data or LiDAR data. These algorithms have been applied to many engineering applications to automatically detect obstacles, defects, or objects of interest. However, these algorithms are only trained to detect the class label of an object representing a physical entity, not the object’s function, a concept that is more nebulous than a single entity. Functional Basis Modeling is a technique developed in mechanical engineering to abstract an object’s functionality from the form of the object. This paper describes a methodology for the automation of Functional Basis Modeling using the PointNet algorithm. A set of point clouds extracted from 3D models of common products were manually labeled with multiple functional basis labels. The resulting dataset was used to train and validate a modified version of the PointNet convolutional neural network algorithm. PointNet is traditionally a multi-class classification algorithm, but the authors modified it to perform multi-label classification. The authors were able to train PointNet to predict several mechanical functions of an input object. The method achieves a single-label maximum F1 Score of 89.6%. This work paves the way for an AI text input CAD generator and more useful object detection methodologies.
Designing for Grand Challenges: An Interactive Method for Tackling Coupled Device-System Designs
Authors: Jetton Cole , Hoyle Christopher , Campbell Matthew I. 
Published: November 2024

Abstract
When designing a system of devices to accomplish a large task, there is an inherent relationship between the capability of each device and the overall system effectiveness. If one is designing the devices from scratch, the engineering specifications depend on a budget and hence the number of devices in the fleet. Determining these engineering specifications depends on some unknown system strategy where the devices must cooperate to accomplish the overall goals. For grand challenges like ocean cleanup or forest fire fighting, the coupled problem of system effectiveness and device specifications can be impossible to determine. One may specify the capabilities of a device which may not be achievable by a design team or may be prohibitively expensive. To overcome this problem, we present an approach that guides an optimization process; one that can both simulate the fleets as well as incorporate designer feedback on design feasibility. This research proposes an interactive framework to address these issues by using Agent-Based Modeling to simulate fleets, utilizing Gaussian Process models to capture user feedback on feasibility and applying constrained Bayesian optimization to guide the design process. This method was tested on two case studies: a simplified ocean cleanup fleet and a higher-fidelity model of firefighting drones. This study provides insight into how performance parameters can be managed simultaneously at the device-level and the system-level.
Self-Supervised Multi-Label Melt Pool Anomaly Classification in Powder Bed Fusion Additive Manufacturing
Authors: Kim Jaehyuk , Yang Zhuo , Lu Yan , Hong Gisuk 
Published: November 2024

Abstract
Laser Powder Bed Fusion (LPBF) is a key technique in metal additive manufacturing (AM) that enables the fabrication of parts with complex geometries and enhanced mechanical properties. Despite its advantages, LPBF’s susceptibility to defects remains a challenge, directly linked to melt pool instability. Real-time Melt Pool Monitoring (MPM) systems offer immediate feedback on melt pool characteristics, which is crucial for identifying potential defects during the LPBF process. Recently, Machine Learning (ML) based approaches with these monitoring systems have been introduced for real-time process anomaly detection and classification. However, one major hurdle is the requirement for large volumes of labeled and balanced data for ML model development, which is necessary for accurate process outlier identification and categorization. To address the issues, this paper introduces a Self-supervised learning-based Visual Transformer (SiT) for multi-label classification of melt pool anomalies, such as abnormal sizes, irregular shapes, spatters, and plume. Experiments reveals that the self-supervised approach, with an average F1-score of 0.979, surpasses the performance of the supervised approach, which has an average F1-score of 0.836 across all classification cases, particularly in imbalanced datasets. The SiT model efficiently classifies multiple anomalies simultaneously, without extensive manual labeling, addressing data imbalance and enhancing MPM systems’ efficiency and reliability. This advancement marks a significant contribution to improving part quality in LPBF processes.
High-Dimensional Variance-Based Adaptive Sampling for Prediction of Aerodynamic Flows Using Non-Intrusive Reduced Order Models
Authors: Dikshit Abhijnan , Leifsson Leifur 
Published: November 2024

Abstract
Non-intrusive reduced order modeling methods (ROMs) have been widely used as a computationally cheap method of modeling aerodynamic flows. A problem with the construction of these models is that a large number of samples are required to achieve global accuracy leading to a high cost of obtaining samples to create the ROMs. In this work, a novel adaptive sampling strategy is proposed that uses the uncertainty estimate of the high-dimensional prediction of the ROM as a sampling objective. The high-dimensional uncertainty estimate is obtained by performing Monte Carlo simulations to propagate the uncertainty of the low-dimensional space obtained using a multi-task Gaussian process model. The proposed algorithms are demonstrated on a two- and fourteen-dimensional aerodynamics modeling test case and compared to the performance of Halton sequences. The results of the two-dimensional case illustrate that the adaptive sampling strategies can achieve up to a 15% improvement over Halton sequences in terms of the mean relative error for the same number of samples. In the fourteen-dimensional test case, the adaptive sampling strategies achieve up to a 7.6% improvement over the Halton sequences at the same number of samples. All sampling strategies have comparable performance once the computational budget is exhausted, achieving a mean relative error of approximately 0.050 and 0.085 in the two- and fourteen-dimensional test cases, respectively. The adaptive sampling strategies can also better predict important features of the pressure distribution, such as shock waves and suction peaks.
Enhancing VR Shopping With Dynamic Space Re-Arrangements: Towards Contextualized Information Displays and Improved Locomotion
Authors: Chen Liuqing , Cai Yu , Wang Ruyue , Hansen Preben , Zhou Tingting , Zhen Yankun , Sun Lingyun 
Published: November 2024

Abstract
Traditional VR shopping applications often rely on static designs for shopping spaces, necessitating frequent teleportation. However, an exploratory case study involving experienced online shoppers revealed several issues with this approach, including problems with interactivity, efficiency, motion sickness, and product presentation. To address these challenges, a workshop was organized with professional designers to gather design strategies, proposing the concept of the dynamic rearrangement of shopping space. This solution enables users to dynamically rearrange the shopping space and the products within it, utilizing space forms such as the department store, the shelf, and the room to suit various shopping intentions: exploration, product comparison, and product matching. In this way, users can obtain access to desired products by rearranging the shopping space rather than through excessive navigation in the static shopping space, creating a contextualized and efficient shopping experience. The solution was evaluated in a within-subjects comparison experiment, which demonstrated our solution’s significant improvements in involvement, motion sickness, efficiency in task performance, product perception, and overall user experience. Our findings suggest that treating the VR shopping space as a flexible, manipulable object through dynamic rearrangements and enriching it with contextualized design elements can provide users with an immersive and efficient VR shopping experience. Space rearrangements may also become a design reference for other VR application scenarios that require frequent locomotion events.
Melt Pool Depth Prediction in Directed Energy Deposition Single-Track Prints Using Point Cloud Analysis
Authors: Mahmoud Youmna , Lyu Jiaqi , Vallabh Chaitanya Krishna Prasad , Manoochehri Souran 
Published: November 2024

Abstract
Traditional assessment methods of melt pool size in metal additive manufacturing (AM) are known for their time-consuming and expensive nature, involving tasks such as cutting, polishing, and detailed microscopy. In this paper, we present a novel approach to predict the melt pool depth in directed energy deposition (DED) AM process, by exclusively relying on point cloud data acquired through a laser scanner coupled with machine learning (ML) tools. Our method streamlines the process of identifying the size of a melt pool by automating the entire procedure, negating the need for any manual intervention starting from the initial data collection stage. This improved analysis opens up possibilities for point-cloud-based real-time predictions and control, making it a highly efficient and adaptable solution.
By training multiple ML models using track width and height data extracted from the point cloud, we surpass the limitations of relying solely on process parameters to accurately predict melt pool depth — a parameter not visible during print inspection, especially using a laser scanner. Among the various regression models utilized, the Gaussian Process Regression model demonstrates superior performance, yielding a Mean Absolute Error (MAE) of 18.89 μm and a Root Mean Squared Error (RMSE) of 25.5 μm. The results indicate a promising potential for the developed methods to eventually subside the requirement for external post-processing of print tracks in order to characterize the melt pool.
Transfer Learning in Multi-Objective Generative Design of Metamaterials
Authors: Apaza Gabriel , Selva Daniel 
Published: November 2024

Abstract
This paper investigates leveraging transfer learning to improve the performance of reinforcement learning based generative agents for multi-objective design optimization. First, a transformer-based generative agent is developed and its training procedure is outlined. Then, the generative agent is pre-trained on a metamaterial design problem in two different multitask settings. One setting is defined by different parameterizations of the metamaterial design problem. The objective of this setting is to determine the agent’s ability to transfer to problems with new parameterizations and new physics models. The second setting is defined by the same parameterizations, but each problem in the training set has unique constraints. The objective of this setting is to determine the agent’s ability to transfer to unseen constraints. For each multitask setting, the pre-trained agent is fine-tuned on different validation metamaterial design problems to assess its ability to transfer to new problem parameterizations, new stiffness models, and new stiffness ratio constraints. Finally, the agent’s performance on the validation problems is baselined against a genetic algorithm (GA) designed to handle constrained problems. Results show that pre-training greatly improves the performance of the generative agent on the validation problems. Furthermore, the pre-trained agent is able to achieve the same or better hypervolume of the GA while only requiring a fraction (10–20%) the number of function evaluations.
Evaluating Design Rationale
Authors: Mirabito Yakira , Liu Xiaowen , Goucher-Lambert Kosa 
Published: November 2024

Abstract
Design rationale captures the justification behind a design decision. Often, rationales vary in the content and depth of information, making the study and comparison of rationales challenging. This project aims to characterize design rationale and develop a computational approach to evaluate design rationale quality at scale. In total, 2250 rationales were machine-generated using GPT across two different representations, and a portion of the rationales (n = 512) were evaluated by two raters across five dimensions of quality. Rationales were then characterized using natural language processing techniques, resulting in 108 linguistic features for each rationale. The evaluations and linguistic features were used to build eight predictive models for each quality dimension. The main results show that structured rationales were rated higher than unstructured rationales across the five dimensions. Thus, the tested feature, specification, and evidence (FSE) framework was shown to be a worthwhile approach to represent the justification behind a design decision. Moreover, key linguistic features that correlate with higher quality ratings were identified. Future work will explore how design rationale quality and characteristics impact design decisions, particularly in a human-AI teaming context where generative design recommendations could benefit from including generative design rationales.
Parametric Analysis of Relationships Among Part Geometry, Build Time, and Energy Consumption for Laser Powder Bed Fusion
Authors: Gee Kaitlyn , Hart A. John 
Published: November 2024

Abstract
Sustainable manufacturing systems must have sufficient throughput to meet demand, be cost-effective, and have an acceptably low environmental impact. For additive manufacturing by laser powder bed fusion (LPBF), part designers and process engineers must understand the underlying scalings among energy utilization, process parameters, and part geometry to attain efficient, low-carbon LPBF production.
To this end, we present a family of parameterized models that digitally analyze part geometry, estimate laser scan paths, and calculate energy consumption associated with single-geometry LPBF production. Using a custom-built slicer and computational pipeline, geometry-specific build time is numerically calculated. A unit process life cycle inventory (UPLCI) is applied to articulate energy consumption across printer warm up, cool down, idling, and scanning. For parts (n = 15) each with a mass of 100g in AlSi10Mg built on a simulated EOS m290 machine, per-part build time varies from 29.2 to 44.3 minutes, a difference of up to 51.7%. Specific energy consumption varies between 20.3 to 30.5 MJ/kg, a difference of up to 50.3%. This work articulates how throughput and energy scale with design decisions including part design and selection, and translate these findings into guidance for manufacturing and design engineers.
Long Term Performance Analysis of an Experimental Sliding Vane Energy Recovery Device for Village Scale Reverse Osmosis
Authors: Welsh Emily , Costello Jeffrey , Winter Amos G. 
Published: November 2024

Abstract
Many communities globally experience a combination of brackish groundwater, low coverage of piped water, and poor access to an electricity grid. Community-scale solar powered reverse osmosis systems can improve water access, but community adoption is hindered because the high specific energy costs of these systems require large expensive solar systems. Prior art identified a novel architecture for an energy recovery device using off-the-shelf sliding vane pumps that could recover up to 62% of the energy lost in the system thereby reducing the specific energy costs. This study assesses the feasibility of the energy recovery concept for resource constrained regions by measuring the power consumption and maintenance requirements of the system over time. Two long-term tests were completed using a prototype reverse osmosis system. One test measured power consumption over time during continuous water desalination. The second test evaluated system reliability and maintenance requirements in a run-to-fail test. This work demonstrated that a coupled sliding vane pump could reduce power consumption during desalination, however the results of the testing revealed a potential catastrophic failure mode that may be caused by pressure pulsing of the vane pumps as they interact with the hydraulic system. Future work should focus on improving system reliability for this configuration.
Gaussian Processes for Non-Differentiable Functions With Jump Discontinuities
Authors: van Beek Anton 
Published: November 2024

Abstract
Gaussian processes (GPs) are used in many disciplines, but the approximation of functions with jump discontinuities and non-differentiabilities remains a challenge. This is a substantial limitation as many contemporary scientific and engineering challenges involve functions with these characteristics (e.g., contact problems in mechanics, and material properties because of phase changes). Available GP models are inappropriate as jump discontinuities and non-differentiabilities are local phenomena that cannot be modeled through covariance structures that describe a function’s global behavior. To overcome this limitation, we introduce the discontinuous Gaussian process (DCGP) model that involves learning a parametric, yet flexible function, on the training data to ensure that the residuals of the random process are continuous and differentiable. Through validation on a set of six test problems and one engineering problem, we show that the DCGP model has superior performance over available models and that there exist physical systems for which the correlation between a function’s outputs can be modeled across discontinuities and non-differentiabilities (i.e., globally). We believe the latter to be a profound insight as it suggests that statistical models, such as DCGP, could be used to expedite scientific discovery even for discontinuous and non-differentiable functions.
Bond-DQN: Deep Q-Learning of Lumped-Element Systems Design via Bond Graphs
Authors: Nguyen Daniel , Tucker Conrad 
Published: November 2024

Abstract
Lumped-element schematics (e.g. free-body diagrams, electrical circuit schematics) have been utilized for design synthesis in a wide variety of domains. They are invaluable for generalizing design synthesis in a process which consists of taking a set of available components and exploring the space of possible connectivity. The connectivity of a given lumped-element model (e.g. the netlist of an electrical schematic) represents a parameterized model which may be optimized using generalizable state space techniques and has the added benefit of being highly interpretable. Some recent machine learning models, particularly in the analog electrical circuit synthesis community, have attempted to leverage such lumped-element models in design synthesis or forward modeling tasks. They either learn faster neural network proxies for the forward dynamics of human-crafted lumped-element simulators or use hand crafted lumped-element representations in the networks themselves. An issue with these approaches is the models are difficult to generalize to different domains due to their reliance on a domain-specific dataset or simulator for design performance evaluation. In this work, we propose Bond-DQN, a Deep Q-Learning method to sequentially generate lumped-parameter designs for state-space based objectives. Specifically, our method uses a grammar based on bond graphs, which are an energy-based graph representation which have been historically used to simulate lumped-parameter models in a variety of domains. The combination of bond graphs with DQL allows us to accurately simulate and sample the large design space of multi-domain systems without retraining on external simulators or new datasets. We demonstrate Bond-DQN’s capability for generalizeable lumped-parameter design by benchmarking against a suspension optimization problem in which the previous SOTA utilizes the Harris Hawks Optimization algorithm, demonstrating a reduction in the peak vehicle translational and pitch acceleration of 20.5% and 4.1% respectively. Additionally, we showcase its ability for multi-domain design by applying to an electrical circuit voltage regulation problem.
A New Combined Finite Element - Analytical Model for Strip Profile Control in Cold Rolling Mills
Authors: Stabile Pietro , Ballo Federico , Sghirlanzoni Davide , Frascino Leonardo , Previati Giorgio 
Published: November 2024

Abstract
The strip profile control problem in cold rolling mills is of paramount importance to ensure product quality. The paper presents a new combined finite element - analytical model of a 20-high cold rolling mill suitable for strip profile simulation and control. The model features rolls, nonlinear contacts between rolls, backup bearings, and mill housing. Rolls are modelled with Timoshenko beam elements and consider roll crown and taper. Contacts between rolls are modelled by Winkler foundation elements with nonlinear stiffness constant derived by the Hertz contact model. The mill housing is introduced by the matrix condensation method and the backup rolling bearings are defined by nonlinear Winkler foundation elements. A full 3D finite element model of the 20-high rolling mill is built by using a commercial software and used to validate the developed finite element - analytical model. A comparison with data extracted from measurements on a real case study is also provided. Finally, the developed combined finite element - analytical model is exploited to solve a strip profile control problem. A Singular Value Decomposition (SVD) optimization of the taper and backup actuators positions is performed, aiming to achieve a desired strip profile. Actuators saturation is considered. The actuators’ influence maps are also obtained.
TRIZ-GPT: An LLM-Augmented Method For Problem-Solving
Authors: Chen Liuqing , Song Yaxuan , Ding Shixian , Sun Lingyun , Childs Peter , Zuo Haoyu 
Published: November 2024

Abstract
TRIZ, the Theory of Inventive Problem Solving, is derived from a comprehensive analysis of patents across various domains, offering a framework and practical tools for problem-solving. Despite its potential to foster innovative solutions, the complexity and abstractness of TRIZ methodology often make its application challenging. This can require users to have a deep understanding of the theory, as well as substantial practical experience and knowledge across various disciplines. The advent of Large Language Models (LLMs) presents an opportunity to address these challenges by leveraging their extensive knowledge bases and reasoning capabilities for innovative solution generation within TRIZ-based problem-solving process. This study explores and evaluates the application of LLMs within the TRIZ-based problem-solving process. The construction of TRIZ case collections establishes a solid empirical foundation for our experiments and offers valuable resources to the TRIZ community. A specifically designed workflow, utilizing step-by-step reasoning and evaluation-validated prompt strategies, effectively transforms concrete problems into TRIZ problems and finally generates inventive solutions. We present a case study in the mechanical engineering field that highlights the practical application of this LLM-augmented method. This showcases GPT-4’s ability to generate solutions that closely resonate with original solutions and suggests more implementation mechanisms.