bluuebunny/crossref_metadata_2025_split huggingface dataset:
Assessing the Prevalence of Artificial Intelligence in Mechanical Engineering and Design Curricula
Authors: Khanolkar Pranav Milind , Lu Jerry , Hurst Ada , Olechowski Alison 
Published: November 2024

Abstract
Engineering curricula undergo frequent change, driven by new technologies and industry needs. Today we are witnessing a significant rise in artificial intelligence (AI) tools, with applications not only across engineering, but specifically in critical endeavors such as design. Given the interest in students in AI techniques, the demand of engineering design employers to hire students with such knowledge, and the fast-evolving nature of the AI field, compared to the slower pace of curriculum evolution, there is thus a need to assess curricular content related to AI in the mechanical engineering curriculum. The purpose of this paper is to provide a baseline assessment of the current prevalence of AI-driven methods and approaches in engineering design education. Current approaches for curricular assessment tend to be resource-intensive and narrow in scope, limiting our capability for large-scale and timely data analysis. Thus we develop a using a novel approach for curriculum data collection and assessment: First, we use web-scraping to collect the titles and descriptions of 2,195 courses in 28 undergraduate mechanical engineering programs. Next we use a list of relevant keywords to search for AI topics in these courses. We find 32 AI-focused courses available to mechanical engineering students, in which nine courses integrate AI and engineering design. These results indicate the limited but emerging prevalence of AI-based courses in engineering design education.
LLM4CAD: Multi-Modal Large Language Models for 3D Computer-Aided Design Generation
Authors: Li Xingang , Sun Yuewan , Sha Zhenghui 
Published: November 2024

Abstract
The evolution of multimodal large language models (LLMs) capable of processing diverse input modalities (e.g., text and images) holds new prospects for their application in engineering design, such as the generation of 3D computer-aided design (CAD) models. However, little is known about the ability of multimodal LLMs to generate 3D design objects, and there is a lack of quantitative assessment. In this study, we develop an approach to enable two LLMs, GPT-4 and GPT-4V, to generate 3D CAD models (i.e., LLM4CAD) and perform experiments to evaluate their efficacy. To address the challenge of data scarcity for multimodal LLM studies, we created a data synthesis pipeline to generate CAD models, sketches, and image data of typical mechanical components (e.g., gears and springs) and collect their natural-language descriptions with dimensional information using Amazon Mechanical Turk. We positioned the CAD program (programming script for CAD design) as a bridge, facilitating the conversion of LLMs’ textual output into tangible CAD design objects. We focus on two critical capabilities: the generation of syntactically correct CAD programs (Cap1) and the accuracy of the parsed 3D shapes (Cap2) quantified by intersection over union. The results show that both GPT-4 and GPT-4V demonstrate potential in 3D CAD generation. Specifically, on average, GPT-4V outperforms when processing only text-based input, exceeding the results obtained using multimodal inputs, such as text with image, for Cap 1 and Cap 2. However, when examining category-specific results of mechanical components, while the same trend still holds for Cap 2, the prominence of multimodal inputs is increasingly evident for more complex geometries (e.g., springs and gears) in Cap 1. The potential of multimodal LLMs in enhancing 3D CAD generation is clear, but their application must be carefully calibrated to the complexity of the target CAD models to be generated.
ANN Crowds: An Investigation of Individual Voices in the Crowd
Authors: Adebayo Oredola , Patel Apurva , Summers Joshua D. 
Published: November 2024

Abstract
This paper presents an in-depth analysis of architectural behavior within the Artificial Neural Network (ANN) Crowd, focusing on its impact on prediction accuracy. The concept of the ANN Crowd is introduced as a means to make predictions from small training set sizes. A single architecture has not proven robust enough in previous studies, so 189 different architectures are employed in the crowd, introducing diversity. Convergence is stopped early to prevent overfitting for each architecture. Therefore, the individual architectures are replicated 100 times with different initial weights. In this manner, the crowd size is increased to 18,900 total voices. Using the Architecture Behavior Interpretation scheme, this study explores how variations in architectural compositions influence the overall performance of the ANN Crowd. Employing One-Way Analysis of Variance (ANOVA) as the primary statistical tool, the research examines differences among architectural groups, maintaining a significance level of 0.05 throughout the analysis. The investigation explores two key questions: (1) whether architectural variations categorized as ‘Good’ and ‘Bad’ based on Normality, Centrality, and Width exhibit statistically significant differences in prediction accuracy, and (2), if the number of nodes within architectural groups significantly impacts the accuracy of prediction models in the ANN Crowd. The study provides valuable insights into the intricate relationship between architectural characteristics and prediction accuracy, offering implications for building ANN Ensembles and enhancing predictive modeling processes.
Human-Robot Interactions - Estimating Force Feedback of the Human Wrist
Authors: Ochitwa Zachary , Fotouhi Reza , Obaid Haron 
Published: November 2024

Abstract
This research is part of the MSK-TIM (Musculoskeletal Tele-robotic Imaging Machine) project. The MSK-TIM is a device which facilitates remote ultrasound diagnosis through remote control of an ultrasound probe. The work reported here is to facilitate human-robot interaction in remote ultrasound imaging; the research work tried to incorporate force feedback into MSK-TIM device such that the operator (radiologist specialist) can examine the patient more efficiently, remotely.
Understanding and modeling the biomechanics of the wrist is important in several fields including medicine and robotics. Up to now, most models have been developed to accurately represent the internal structure of the wrist. However, these models are complex and computationally time-consuming. This study reports on investigation on how we developed a time-efficient finite element model of the wrist. The material parameters were derived using model-correction finite element analysis. The force-displacement experimental measurements were taken at three areas of the human wrist using a robotic tele-sonography manipulator. The finite element model was then incrementally modified to improve the computational time, while measuring the corresponding error. The finite element model successfully predicted force feedback from the human arm with a small computation time (18.4s) per step. The subsequent finite element models further reduced this time (0.95s). As a result of this study, it has been shown that soft tissues can be generalized as a phenomenological material to decrease model complexity. Finally, the geometry of anatomy can be simplified, without a major reduction in accuracy, to greatly reduce the computation time — as shown by the 95% reduction in this study.
Concentric Helical Actuator Design and Characterization
Authors: Quarnstrom Joel , Xiang Yujiang 
Published: November 2024

Abstract
In the fields of traditional robotics, bio-inspired robotics, and mechanism design, the two fundamental types of actuators are rotational and prismatic actuators. The dominant rotational actuator is the electric motor (less common rotational actuators include hydraulic and pneumatic motors). Within prismatic actuators, hydraulic and pneumatic pistons as well as screw based linear actuators vie for dominance. One implementational drawback of most versions of these three actuators is that they cannot rigidly contract to less than half of their original length. This is not typically a problem in traditional robotics or mechanism design, but it can be a problem for bio-inspired robotics and other novel robots that attempt very large changes in volume while maintaining some rigidity. This paper will describe the design and simulation of a novel concentric helical actuator that can achieve a contraction/extension ratio larger than 1:3 (200% strain). The mechanical advantage between the input torque and the output linear force of the actuator changes as a function of the actuator position. For a given force, the motor torque is the largest when the actuator is fully contracted. This actuator can produce 1N of linear force for every 30Nmm of motor torque, which makes it useful for light weight robot applications. This concentric helical actuator can serve as a useful mechanism in bio-inspired and novel robotics.
Effects of Iron Layer Thickness and Growth Time on Carbon-Infiltrated Carbon Nanotube Growth Heights on Ti6Al4V Medical Grade Titanium
Authors: Arias Michelle , Jensen Brian D. 
Published: November 2024

Abstract
Carbon-infiltrated carbon nanotubes (CICNT) have been discovered to have an antibacterial effect on medical grade metallic surfaces. In order to improve materials used for antimicrobial experiments, the primary aim of this investigation was to provide a more thorough understanding of the effects of controlling the height of carbon nanotubes by changing the iron catalyst layer thickness prior to carbon nanotube growth, and changing the time of the growth phase of the carbon nanotubes via chemical vapor deposition (CVD) on Ti6Al4V. All samples were coated with a barrier layer of alumina (Al2O3) of 200 nm between the Ti6Al4V and an iron growth catalyst layer. Three different groups of iron deposited thicknesses (2 nm, 4 nm, and 6 nm) were used, along with four separate growth times: 40 s, 80 s, 160 s, and 320 s. Samples were grown at 750 C for each setting of the variables, with each trial repeated three times. A subsequent infiltration period of one minute at 900 C was implemented on all batches. The samples were then analyzed under a 3D profilometer to observe the varying heights. Results show that there is a statistically significant increase in growth height with increasing growth time. Analysis of the variation in height shows that samples with 6 nm of iron and a 40 s growth time have the smallest height variation. To promote more repeatable nanotube growths, we recommend that these settings should be used for growth of nanotube films for future experiments on bacterial resistance.
Augmenting Engineering Design With AI: Introducing the AI Design Assistant (AIDA)
Authors: Renji Naveen Mathews , Veera Sagar Chakravarthy Mathada , Yan Bei , Liao Ting 
Published: November 2024

Abstract
It’s critical to understand how to use artificial intelligence (AI) to foster innovation in the modern world as AI becomes more integrated into creative and problem-solving tasks. Using the sustainable washing machine as a primary example, this study designed and developed AI design assistant AIDA as a web-based chatbot to facilitate design ideation, leveraging large language models. AIDA prompts design tasks and assesses user-generated ideas for validity, novelty, and feasibility using RoBERTa-based models. As in the initial phase of an ongoing project, we conducted a human-subject experiment to validate a baseline version of AIDA and examined user performance and perceptions. The participants demonstrated smooth interaction with AIDA and consistent performance. They reported mostly positive perceived usefulness, enjoyment, and trust. Moreover, females and participants equal to or over 25 showed a comparable level of trust for general automated systems and AIDA, whereas male and under 25 participants were more skeptical about AIDA. This research offers a framework for technical development, tailored interactions, and real-time feedback, as well as insights into the use of AI chatbots to mediate engineering design. By analyzing user behavior and survey responses, we identified future directions in designing AI systems in engineering education and early-stage design.
Amputee Gait on Slopping Ground and Uneven Surfaces: A State of the Art Review
Authors: Franco Ochoa Mariana , Ortega Bedoya Yessika María , Rendón Velez Elizabeth 
Published: November 2024

Abstract
Objective: to clarify the current state of the art on transtibial amputee gait on sloping ground and uneven surfaces by means of the analysis of kinetic, kinematic, spatiotemporal and physiological variables, so that prostheses can be designed to suit the specific needs of the rural context.
Methods: A systematic literature search was performed using specific electronic databases, search terms, and inclusion/exclusion criteria to identify studies on transtibial amputee gait in uneven terrain. Additionally, a reasoning model was created to facilitate structured extraction of pertinent data from the literature.
Results: Transtibial amputees adapt their gait on uneven surfaces by adjusting foot positioning and increasing base of support. Research indicates heightened hip and knee flexion during both stance and swing phases, aiding in lowering the center of mass and improving toe clearance compared to level ground walking.
Conclusions: Transtibial amputees utilize compensatory strategies to maintain dynamic stability on uneven surfaces, like non-amputees, due to limited ankle mobility and sensory feedback. However, existing research predominantly focuses on level ground biomechanics, neglecting uneven terrains combined with slopes. Given the prevalence of such terrain in rural Colombia and Latin America, further investigation is vital to understand amputees’ gait. This research would aid in designing prostheses that improve stability and address the unique needs of transtibial amputees in these environments.
An Affordable Hip Exoskeleton for Assistance and Rehabilitation: Design, Analysis, and Fabrication
Authors: Shrestha Dhurba , Sharifi Mojtaba 
Published: November 2024

Abstract
People with physical disabilities or chronic conditions are advised to utilize assistive tools for their day-to-day activities. Making these devices affordable, lightweight, and comfortable to use would increase their practicality for home use and substantially impact the lives of individuals who need them. This work presents a new human-inspired hip exoskeleton design that is cost-effective, and comfortable for the wearer during various movements. This exoskeleton also maintains structural strength and high torque power and is designed to be adjustable and can fit users with varying hip sizes. Furthermore, the large trunk support component features a curved surface intentionally designed to rest on the user’s pelvis, offering added support and effectively preventing any undesired downward movement from the hip. It has one degree of freedom (DOF) and can generate hip movement trajectories in the sagittal plane for both legs. The exoskeleton is fabricated using printable materials like thermoplastic polyurethane (TPU), Polyethylene terephthalate glycol (PETG), polylactic acid (PLA), and carbon fiber to reduce its weight, cost and to make it comfortable and accessible to wider range of end-users. SOLIDWORKS was ussed to design the hip exoskeleton and Ansys was used to perform the FEA on the components. All the components on the hip exoskeleton have a factor of safety (FOS) of a minimum of 3.23. The developed hip exoskeleton including battery, actuators, and electronics weighs 11 kg and is anticipated to be priced below $3,000. The exoskeleton is tested by an able-bodied user weighing 74 kg and 1.74 m tall validating the functionality of all its components. The tests successfully demonstrated that the exoskeleton’s parts efficiently handled the load generated by the motor and hip joint movements. This device is modular and could serve as a base for a full lower limb exoskeleton in the future, which will include components for the hip, knee, and ankle.
Can an LLM Learn an Unseen Engineering Language? An Empirical Study of Prompt Engineering on ChatGPT and Function Modeling
Authors: Loynaz Armando , Stanish Cassandra , Treeman-Reinke Aria , Donall Sebastian , Sen Chiradeep 
Published: November 2024

Abstract
In the early stages of engineering design, designers build various models using different languages. Meanwhile, large language models (LLMs) have evolved to a point where they can produce human-like intelligent behavior in many areas where the conversations are in text. In addition, these LLMs can also learn new linguistic patterns at runtime, through prompt engineering. This development creates the possibility of teaching LLMs design modeling languages too, as long as the modeling language is text-based. To this end, this paper examines the extent to which an LLM (specifically Open AI’s GPT-3.5) can learn a previously unseen engineering modeling language (specifically the function modeling language). Four experiments are conducted to test the LLM’s learning abilities with progressively more complex learning challenges. The results show that the LLM can learn function modeling vocabularies and simple grammar rules to formulate functional sentences and apply them to both individual devices and systems. However, the LLM performs inconsistently when it is asked to include the topology of a function model in the text-based functional sentences.
Analysis of an Electrically Interconnected Half-Car Suspension System With Resistive Loads Under Random Road Excitation
Authors: Baradaran Akbarzadeh Majid , Tai Wei Che 
Published: November 2024

Abstract
The primary objective of this paper is to study an electrically interconnected suspension (EIS) system designed to operate under random excitation conditions for energy harvesting purposes. The system comprises of two distinct sections: firstly, a four-degree-of-freedom half-car model suspension system; secondly, a two-port electrical network integrated with the suspension, facilitated by generators positioned on either side of the suspension. In this study, a network from the literature is used, which was shown to effectively decouple the bounce and roll motions of the system, corresponding to vertical force and torque, thus enabling power generation at each branch. The analysis of the system is conducted by utilizing three established methods suitable for systems subject to stochastic excitation: Monte Carlo Simulation (MCS), moment equations, and ∥H∥2 method. Additionally, by employing these methodologies, a comprehensive parameter study is conducted, focusing on how resistive load variations influence both mechanical and electrical behaviors. More specifically, we use the power spectral density (PSD) functions of the mechanical and electrical variables to gain knowledge about the system’s behavior in terms of vibrations and energy harvesting. In this regard, it is found that sprung mass roll and bounce motions are decoupled in the case of identical resistive loads at each branch of the electrical network. Also, we can generate power on each branch related to the roll and bounce motions of the sprung mass at the same time due to the property of the electrical circuit. Finally, we did optimization on the electrical network to find the optimal load resistance values on each branch for maximizing the total power which is six times higher than the internal resistance of the generators of the system.
A Case Study of Design Concept Generation Based on Knowledge Operation With a Large Language Model: Architectural Design Using Christopher Alexander’s Pattern Language
Authors: Tanaka Daichi , Kuroishi Kei , Utsunomiya Akira , Nomaguchi Yutaka , Fujita Kikuo 
Published: November 2024

Abstract
This paper examines a case study using Christopher Alexander’s Pattern Language in architectural design to explore the impact of knowledge operation processes on design concept generation with a Large Language Model (LLM). The knowledge operation for the pattern language involves the chain of thought of defining design context, functional requirements, and physical attributes of alternative solutions. Three prompting methods were compared: 1) presenting only the design problem, 2) adding plain text of the pattern language, and 3) including both the pattern language and instructions for knowledge operation. Experiments were conducted with an off-the-shelf LLM, generating three design concepts in text and image for each prompting method. Thirty-four mechanical and architectural design students evaluated the concepts for validity, novelty, and feasibility. Results showed that the prompt involving knowledge operation led to limited feasibility but the highest novelty and moderate validity. The study suggests that the knowledge of conceptual design theories and methodologies can facilitate LLMs in effectively generating novel and valid design concepts.
A Learning Framework for Enabling Robots to Autonomously Dispense Granular Material On-Demand
Authors: Kang Jeon Ho , Shukla Rishabh , Mehta Moksh , Gupta Satyandra K. 
Published: November 2024

Abstract
This paper presents a learning framework for enabling robots to autonomously dispense granular materials on demand. This framework enables robots to scoop and transfer the requested material amount with milligram scale accuracy. Our approach is capable of handling challenging cases where the amount left in the source container is significantly less than the container volume. In such cases, robots must build piles before scooping the material to capture enough material within the scooper. We use Gaussian Process Regression (GPR) to predict granular material behavior during scooping and pouring tasks. GPR is effective in learning the behavior of granular material with task parameters, such as robot joint angles, joint accelerations, and end-effector geometry. During task execution, we use GPR to solve the inverse problem and determine the task parameters based on the desired scooping and pouring amounts. The system performance is evaluated by showing GPR’s ability to predict scooped and poured amounts with reasonable uncertainty. We benchmark our method against the traditional approach of fine-tuning the amount via closed-loop control from the scale sensor feedback. Our method shows 55.2% improvement in time taken to dispense the granular material over the benchmark approach. The proposed framework shows promising results in terms of reducing dispensing times.
Experimentally Supported Design Optimization of Marine Hydrokinetic Turbine Systems With Adaptive Duct Contraction Control
Authors: Griffin Austin L. , Lee Yong Hoon 
Published: November 2024

Abstract
This study investigates a novel adaptive duct control strategy for maximizing power output in a ducted horizontal-axis hydrokinetic turbine (HAHkT) system. The strategy focuses on varying contraction ratios (CR) and rotor blade pitch angles to optimize energy production under specific inflow conditions. Experimental static data for three different CR and thirteen pitch angles are utilized to design and perform experiments aimed at creating a dynamic model tailored to the system’s behavior.
Parametric analyses are conducted using QBlade software to assess rotor blade performance across various geometry configurations and flow conditions, with water as the medium. An open-channel water flume experiment is established to evaluate the performance of the small-scale ducted HAHkT with the selected range of CR and pitch configurations. The experimental data collected is utilized to construct a dynamic surrogate model (SM) capable of predicting system behavior. Optimizations tests controlling CR and blade pitch angle for maximal power extraction are performed with a variety of velocity profiles.
The proposed adaptive duct control strategy presents promising potential for developing highly efficient ducted HAHkT systems. By dynamically adjusting CR and pitch based on inflow conditions, this approach aims to achieve optimal energy production, ultimately contributing to the realization of cost-effective renewable energy solutions.