bluuebunny/crossref_metadata_2025 huggingface dataset:
Vibration of Thin-Walled Tubes in Thermal Environment
Authors: Babilio Enrico
Published: November 2024

Vibrations induced by fast varying thermal gradients were first investigated by Boley in the Fifties with respect to the case of beams operating in extra-atmospheric microgravity environment. Indeed, spacecrafts and orbiting satellites crossing from the Earth’s shadow into sunlight experience rapid changes in the thermal loading due to the solar radiation. Such loading is responsible of time dependent bending moments and transverse shear forces on appendages and booms. Those are typically lightweight structures having high flexibility with low frequency and damping characteristic. The thermally-induced bending moment produces a sudden deflection of such flexible structures, accompanied by a change of sunlight incidence angle. These two set up the onset of vibrations with fluctuating moment that could not be correctly analyzed if the inertia effect and the coupling between temperature and strain fields are not taken into account. A correct comprehension of the phenomenon is of interest since thermally-induced vibrations may interfere with system operations of booms, often carrying a payload for measurement. In the worst cases thermal flutter, a self-excited vibration of increasing amplitudes, may lead to the failure of the structural member. In problems in which thermal effect induces deflections and vibrations of a slender body, thermal conductivity, material stiffness and mass density play an important role. If correctly selected these properties could improve the material robustness against deflection and oscillation. Hence, the question is if indeed does exist a certain combination of those material properties which can help to lower deflections and damp oscillations. In the proposed contribution a cantilever beam with a circular hollow cross-section is considered and the possibility to protect the beam from thermal effects by optimizing the structure of the cross-sectional wall is investigated. Some preliminary results seem encouraging and may pave the way for passive control of deflection and thermal flutter effects.
Objective Colormaps for Minimizing Interpretation Bias in Science and Engineering
Authors: Iliopoulos Athanasios P. , Michopoulos John G. , Stueben John C. , Apetre Nicoleta , Birnbaum Andrew
Published: November 2024

While working on the implementation of hygro-thermo-structural models for composite materials like those used in aircraft, we started noticing discontinuities in the fringe plots of the validation tests. This led us down a time-consuming path of debugging the implementation, that indicated that there were no problems with our numerical code. After further investigation we established the the issue emanated from the non-uniform nature of the colormap used to map the field variables. In researching the matter further we identified several contemporary attempts in establishing metric to judge the perceptual non-uniformity of colormaps. In this work we extend these attempts to encompass objective functions that allow for the automated generation of colormaps that minimize non-uniformity with the goal to reduce visual bias to the extent allowed by modern color theory and color standards. The objective functions are formulates on the basis of lightness, chromaticity, gamut completion, and desired color in both explicit and implict forms. We present several application examples and illustrate them based on typical benchmark data. We furthermore demonstrate the appearance of colormaps for the three types of color vision deficiency using simulated mapping. These studies are a precursor of refinement of the proposed framework towards more universal colormaps.
Structural Design, Modal Analysis and Manufacturing of an Intelligent Robotic Walker
Authors: Sharma Anubhav Kumar , Sharifi Mojtaba
Published: November 2024

People with physical disabilities and chronic conditions often rely on assistive tools for their daily activities. Enhancing the affordability, design, and comfort of these devices can significantly improve their practicality for home use and enhance the life of individuals in need. Despite mechanical advancements in traditional walkers, they have not fully integrated robotic capabilities. This research aims to address this gap by providing practical solutions to a range of motor impairment challenges and developing a robotic walker with an optimum design for stability. The innovative walker features a 4-wheel structurally stable design, supported by the fundamental implementation of a Walker Tipping Index (WTI). Additionally, it includes motion planning based on the user’s intention, collision detection, and fall prevention, all with the goal of enhancing the walking experience for individuals with motor impairments. The design of the walker chassis prioritized postural stability in all directions, utilizing a WTI calculator to determine overall dimensions while ensuring the index remained below 0.5 in all directions (front-back, left-right). For the structural integrity of the design, Finite Element Analysis (FEA) was conducted for symmetric and asymmetric loading, modal analysis, and buckling analysis. Results indicated a factor of safety above 2 for all worst-case loading conditions, with a buckling load factor of 5.94 and a natural frequency optimized to exceed the motor’s operational frequency. Fabrication of the walker utilized AISI 4130 steel tubes to minimize weight while maximizing strength. LiDAR and force Sensor, electronics, and battery mounting components were 3D printed using carbon fiber-infused PETG, and Aluminum 6061-T was used for the motor mounts which were manufactured using CNC milling. Finally, a comprehensive physical stability test in all directions was conducted to validate the WTI calculations and confirm the postural stability of the walker structure.
Advancing Fluid-Based Thermal Management Systems Design: Leveraging Graph Neural Networks for Graph Regression and Efficient Enumeration Reduction
Authors: Bayat Saeid , Shahmansouri Nastaran , Peddada Satya RT , Tessier Alexander , Butscher Adrian , Allison James T.
Published: November 2024

This study introduces a graph-based framework developed for representing various aspects of optimal thermal management system design, with the aim of rapidly and efficiently identifying optimal design candidates. Initially, the graph-based framework is utilized to generate diverse thermal management system architectures. The dynamics of these system architectures are modeled under various loading conditions, and an open-loop optimal controller is employed to determine each system’s optimal performance. These modeled cases constitute the dataset, with the corresponding optimal performance values serving as the labels for the data. In the subsequent step, a Graph Neural Network (GNN) model is trained on 11,134 (30%) of the labeled data to predict the systems’ performance, effectively addressing a regression problem. Utilizing this trained model, we estimate the performance values for the remaining 26,195 (70%) of the data, which serves as the test set. The reason for larger number of test points was to ensure that the model is capable of predicting performance across all the diversity of inputs. In the third step, the predicted performance values are employed to rank the test data, facilitating prioritized evaluation of the design scenarios. Specifically, a small subset of the test data with the highest estimated ranks undergoes evaluation via an open-loop optimal control solver. This targeted approach concentrates on evaluating higher-ranked designs identified by the GNN, replacing the exhaustive search (enumeration-based) of all design cases. The results demonstrate a significant average reduction of over 92% in the number of system dynamic modeling and optimal control analyses required to identify optimal design scenarios.
The Beginning of IDIVE: Interactive Design in Immersive Virtual Environments
Authors: Pawar Vathsa Rao , Mocko Gregory , Turner Cameron J. , Castanier Matthew P.
Published: November 2024

With the advancing technology in the field of immersive reality, bringing the whole world closer is getting easier. For projects that involve multiple stakeholders, the IDIVE (Immersive Design in Virtual Reality) is a system in which a complex CAD model is placed in a virtual environment, which helps one understand the requirement gathering of the displayed model’s nature and its objective. The impact of using extended reality in early design reviews is that it supports accelerated iterations, quick testing, informed decision-making, simulations, and cost savings. This enables designers to assess various aspects of the design’s functionality and performance in the early stage of the project. We also represent a toolchain for developing a virtual environment using a game engine – Unreal Engine. The method briefly describes the basic principles and best practices for the development of a suitable location-based virtual environment for the model from initial study to implementation. Stakeholders can utilize a head-mounted device, allowing them to immerse themselves in the virtual environment, enabling each person to form their unique assessment of its merits and shortcomings. We ran a pilot study among homogeneous graduate students, this paper describes the existing tools and capabilities for each of these steps and highlights instances where further toolchain development is necessary to produce a useful design environment for distributed collaboration through an immersive design virtual environment.
Characterization of a Compliant Mechanism Gripper With Visual Force Feedback Capabilities
Authors: Mohanty Vineet K. , Ranganathan Sukesh Jagannathan , Vallinayagam Haris Raja , Aguirre Milton E.
Published: November 2024

Teleoperating systems enable humans to perform material handling tasks that otherwise are inaccessible or hazardous to humans. Applications range from micro- to heavy-assembly, minimally invasive surgery, to sea and space exploration. Teleoperating machines often involve remote control of machinery or devices in environments where the operator is not physically present, which creates a physical barrier that separates the human operator’s sensory system from the working area. Force feedback systems can provide crucial information about the environment, which helps the operator make informed decisions and adjust their actions accordingly. This work examines the force-deflection properties of a compliant-based visual force feedback gripper. Experiments determine a linear relationship between force transmission and the deformation of flexible elements, which serve as a visual cue to interpret force feedback information. Since humans tend to have a preference or bias toward perceiving and understanding the behavior of datasets as linear rather than nonlinear, the existing mechanism design may sufficiently act as a testing platform for future assessment. Further research is needed to determine whether this compliant-based solution offers advantages over conventional force feedback interfaces by offering continuous force feedback cues without complex mechatronics. Expanding the scope of our study to encompass real-world scenarios and diverse object shapes, sizes, and stiffness will enhance the practical utility of our findings.
Emulating Cognitive Style in Large Language Models
Authors: Agarwal Vasvi , Jablokow Kathryn , McComb Christopher
Published: November 2024

Large Language Models (LLMs) have emerged as a pivotal technology in the evolving world. With respect to design, the significance of LLMs lies in their transformative potential for collaborating design teams. However, it is not known whether LLMs can emulate cognitive and social attributes which are known to be important during design, such as cognitive style. This research evaluates the efficacy of LLMs to emulate aspects of Kirton’s adaption innovation inventory, which characterizes approaches to problem-solving. Specifically, we use LLMs to generate solutions for three design problems using two different cognitive style prompts (adaptive and innovative), and evaluate the solutions with respect to feasibility and paradigm relatedness. We found that solutions generated using the adaptive prompt tend to display higher feasibility and are paradigm preserving, while solutions generated using the innovative prompts were more paradigm modifying. This aligns with prior work and expectations based on KAI theory. Ultimately, these results demonstrate that LLMs can be prompted to accurately emulate cognitive style.
Machine Learning Applications for the Synthesis of Planar Mechanisms — A Comprehensive Methodical Literature Review
Authors: Sonntag Sebastian , Brünjes Vincent , Luttmer Janosch , Corves Burkhard , Nagarajah Arun
Published: November 2024

Recent published works in mechanism synthesis have shown promising results by employing machine learning-based methods such as deep neural networks. As a foundation for further research, an extensive literature review on the application of machine learning algorithms for planar mechanism synthesis is provided. Following the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines for systematic review processes, the scientific landscape of the last two decades has been mapped. In total, 29 research articles have been identified and analyzed. The results were filtered, grouped by the machine learning algorithms used, and matched with the different tasks that occur in the mechanism synthesis process. Our analysis shows four major research aspects that allow improvement. First, including other requirements that occur in real-world motion tasks besides target paths, like velocities, transmission angles and other design requirements. Second, considering multiple kinematic chains and including different joint types, instead of learning on a few selected mechanism types, as in a real-world application the mechanism type is an output of the synthesis process, and not an input. Third, comparing the efficiency of different machine learning algorithms for synthesis problems, as well as applying algorithms that are, for example, widely used in similar design engineering tasks, and fourth, creating and utilizing standardized datasets of labeled mechanisms.
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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.
The Effects of Incubation, Fixation Stimuli, and Working Memory Capacity on Design Fixation
Authors: Brundage Amanda , Soria Zurita Nicolás F. , Starkey Elizabeth , Menold Jessica , Jablokow Kathryn
Published: November 2024

This paper examines design fixation in engineering classrooms, particularly focusing on the impact of incubation, fixation stimuli, and individual working memory capacity.146 first year engineering design students completed a presented design challenge under different experimental conditions. Their sketched solutions were evaluated using established fixation metrics. The results suggest no correlation between students’ working memory capacity and the percentage of example features integrated into their solutions. Results indicate that when students are presented with only a negative example, fixation levels are influenced by the length of the incubation period. Based on these results, instructors can be advised to present negative examples to students 96 hours before ideation to mitigate the inclusion of excessive negative example features. Furthermore, results show a significant increase in fixation on features present in positive examples, suggesting that instructors should be aware of presenting both positive and negative examples to their students if they want to avoid design fixation.
Effects of High Frequency Horizontal Base Excitation on a Bistable System
Authors: Ghoshal Pritam , Gibert James M. , Bajaj Anil K.
Published: November 2024

High frequency excitation (HFE) is known to induce various nontrivial effects, such as system stiffening, biasing, and the smoothing of discontinuities in dynamical systems. These effects become increasingly pertinent in multi-stable systems, where the system’s bias towards a certain equilibrium state can depend heavily on the combination of forcing parameters, leading to stability in some scenarios and instability in others. In this initial investigation, our objective is to pinpoint the specific parameter ranges in which the bistable system demonstrates typical HFE effects, both through numerical simulations and experimental observations. To accomplish this, we utilize the method of multiple scales to analyze the interplay among different time scales. The equation of slow dynamics reveals how the excitation parameters lead to a change in stability of equilibrium points. Additionally, we delineate the parameter ranges where stabilizing previously unstable equilibrium configurations is achievable. We demonstrate the typical positional biasing effect of high-frequency excitation that leads to a shift in the equilibrium points as the excitation parameter is varied. This kind of excitation can enable the active shaping of potential wells. Finally, we qualitatively validate our numerical findings through experimental testing using a simplistic model made with LEGOs.
Intuitive Physics-Driven Approach: Design and Calibration of a Flight Controller for Indoor Blimps
Authors: Salas Gordoniz Jorge Esteban , Saad Maarouf , St-Onge David
Published: November 2024

This research addresses the challenges of defining and tuning a flight controller for uncrewed aerial vehicles (UAVs), mainly, a lighter-than-air vehicle, like a blimp; a task often fraught with complexity for UAV designers. These challenges typically involve several iterations in both simulation and real-world settings that require the identification of the dynamic model. In this paper, we introduce a novel, but simple approach that harnesses intuitive physics principles to streamline the process of tuning and to ensure the safeness and robust path tracking for indoor blimps. Our method involves a threefold strategy: 1. A basic control rooted in Sliding Mode Control (SMC) with a saturation term that permits to achieve the motion in the three axes (X, Y and Z), while defining maximum cruising speeds and maximum desired control forces; used to complete the operation of path tracking and hovering. 2. A recursive simple moving average (SMA) term for the applied control forces is incorporated to the previous strategy to minimize steady-state error in real-time for altitude control, which can compensate for changes in the weight of the blimp. 3. A swing stabilizer mechanism designed to dampen oscillations around naturally occurred pitch and roll angles for enhanced stability. Experimental results validate the efficacy and simplicity of our approach, demonstrating robustness, rapid deployment and good path accuracy.
Real-Time State Estimation of Hydraulically-Driven Systems Based on Unscented Kalman Filter and Low-Fidelity Models
Authors: Khadim Qasim , Pyrhönen Lauri , Kurvinen Emil , Gerstmayr Johannes , Mikkola Aki , Orzechowski Grzegorz
Published: November 2024

The algorithms identifying machine health and predicting maintenance needs require accurate information about the machine’s state. Because of the great amount of collected data and limited data buffering and data transfer capabilities in many hydraulic machinery applications, the data should be processed in real time. The real-time requirement demands computationally efficient simulation models, while the self-correcting nature of estimation algorithms allows models with lower precision to be used.

The study combines the novel low-fidelity surrogate models with an Unscented Kalman Filter (UKF) for the real-time state estimation of the coupled mechanical systems. The surrogate-assisted modeling approach reduces the model complexity and improves computational efficiency while maintaining high accuracy. A hydraulic forestry crane case study is investigated, and the computational efficiency and numerical accuracy of the developed observers are evaluated. The encoder measurements are provided by the high-fidelity model. The high-fidelity model introduces imperfections in the form of the frictional forces in the hydraulic cylinders, which induce approximately 2 % error in actuated force. The case study results demonstrate that the surrogate-based state observer delivers estimations within the real-time computational range. It shows a maximum accuracy deviation of 7.31 % for unmeasured states compared to the high-fidelity model-based observer.
Operational Modal Analysis of a Rotating Structure Using a Novel Image-Based Long-Range Continuously Scanning Laser Doppler Vibrometer
Authors: Lyu Linfeng , Zhu Weidong
Published: November 2024

A novel image-based long-range continuously scanning laser Doppler vibrometer (CSLDV) is developed to measure vibration of a rotating structure with a complex background. The image-based long-range tracking CSLDV can track and scan the rotating structure by processing its images captured by a camera in the image-based long-range tracking CSLDV. A novel image processing method with a background averaging technique is developed to extract the rotating structure from the background in captured images and determine its location. A one-dimensional scan scheme is used to scan along a straight line along the rotating structure. An improved demodulation method is used to process measurements of the image-based long-range tracking CSLDV to estimate modal parameters of the rotating structure, including damped natural frequencies and undamped mode shapes. Experiments of tracking and scanning a rotating blade are conducted to validate vibration measurement of a rotating blade using the image-based long-range tracking CSLDV. Measured responses of the rotating blade are processed to estimate its speeds and modal parameters. First three one-dimensional damped natural frequencies of the rotating blade and undamped mode shapes along its middle line are successful estimated and compared with results from the finite element model of the corresponding stationary blade.
Structural Design Vs Manufacturability Costs of Complex Stamped Components
Authors: Alawadhi Ibraheem , Ramnath Satchit , Bolar Abhishek , Fu Yilin , Shah Jami J. , Zurbrugg Nathan , Detwiler Duane
Published: November 2024

This paper presents a study of tradeoffs between design and manufacturing cost objectives for complex sheet metal stamped components, taking into account both topological (shape) and size variants of geometric features. The study artifact is the support frame of automotive hoods for a given outer skin. Traditional optimization methods, such as Design of Experiments (DOE)/Response Surface Optimization (RSO), mathematical algorithms, and Genetic Algorithms (GA), are not suitable because different feature shapes cannot be described by a given set of uniform design variables. A large data set of shape and size variants for automotive hood frames was curated for the study. Work has already been completed on generating geometric data sets and evaluating their structural design objectives. In this paper, we focus on manufacturing cost as a metric for manufacturability. We look at both the material and process costs, the latter determined indirectly via press loads for trimming and drawing operations, to find the press loads associated with manufacturing processes used to produce the hood designs already available in the 10k hood dataset. A 10% sampling was used in the stamping simulation from which response surfaces were created to use in completing manufacturability responses on (almost) all 10,000 designs in the data set. The manufacturing costs data curated from this research can provide designers insights into manufacturing costs during the design phases.