bluuebunny/crossref_metadata_2025_split huggingface dataset:
Energy, Economic, and Environmental Assessment of an Energy Pile-Based Solar-Assisted Residential Ground Source Heat Pump System in a Canadian Cold Climate
Authors: Beragama Jathunge Charaka , Dworkin Seth B. , Wemhöner Carsten , Mwesigye Aggrey 
Published: January 2025

Abstract
The building sector’s high energy consumption and related emissions signify the urgency for cleaner space conditioning technologies. While ground source heat pumps (GSHPs) and solar-assisted ground source heat pumps (SAGSHPs) offer cleaner and more efficient solutions, their wide adoption is hindered by the high initial costs. This study investigates the energy, economic, and environmental aspects of energy pile-based GSHP and SAGSHP systems in a Canadian cold climate. A finite volume numerical model of the energy pile is developed, validated and coupled with a realistic building energy load profile and photovoltaic/thermal solar collector model to evaluate the energy performance of the systems. The economic and environmental performance of the system is compared with two conventional space conditioning technologies, i.e. electric baseboard heater + air conditioner and natural gas furnace + air conditioner. The results indicate that the payback period of the GSHP and the SAGSHP systems is in the range of 6–17 years when the ground heat exchanger is incorporated into the building foundation during construction. This highlights the potential of energy piles to drastically cut down the initial cost of ground-coupled heat pump systems. Moreover, based on Alberta’s grid emission factors as of 2023, the GSHP and SAGSHP systems lower emissions by 50.2–68.2% and 88.6–91.8% compared to baseboard heater + air conditioner and 47.6–66.6% and 82.1–87.2 % compared to natural gas furnace + air conditioner, respectively.
A Pedagogical Exercise of Integrating AI Image Generator Tool: A Pilot Experience in Design Product Education
Authors: Rojas Juan-Carlos 
Published: January 2025

Abstract
This pilot study explores the integration of technological tools, particularly Artificial Intelligence (AI) Interactive Generative Technologies (IGTs), into pedagogical exercises within design product education (DPE). The aim is to present the impact of this integration on traditional creative exercises. The results of the exercise reveal promising results, suggesting that the use of AI IGTs enhances students’ motivation and application of design principles in less time compared to traditional methods. Through a structured exercise involving the creation of a design firm and the subsequent application of AI IGTs to generate design solutions, students demonstrated natural engagement and proficiency. The study underscores the importance of integrating technological trends, such as AI IGTs, into design education to bridge the gap between academic learning and professional practice. However, several areas for improvement and further research are identified, including the standardization of exercise approaches, optimal tool selection, and comprehensive evaluation of student perception, and learning outcomes. This study contributes to the ongoing discourse on the evolving pedagogical approaches in design education and highlights the potential of AI IGTs to revolutionize the learning experience for future designers.
Development of an Open-Source Magnetic Modular System for End-Effectors in 3D Printed Humanoid Robots
Authors: Michels William , Van Milligen James , Radhakrishnan Pradeep 
Published: January 2025

Abstract
Humanoid robots that are being developed for at-home use require versatile end-effectors or hands to perform tasks ranging from handling fruits to complex medical equipment. At Worcester Polytechnic Institute (WPI), efforts have been underway to develop toddler-sized humanoid robots, based on the Poppy platform, for various at-home tasks involving senior citizens. One of these robots is Finley, a 4.5kg and 92 cm tall robot that is completely 3D printed with 27 degrees of freedom. To handle various tasks and complexities, grippers with many actuators cannot be installed as Finley’s end-effector due to the robot’s weight restrictions and 3D printed nature. One possibility is to use function-specific end-effectors that can be swapped based on the need. But swapping each gripper requires assembly of various components and electronics apart from the downtime during such events. To make it easier to swap these end-effector grippers, a magnetic modular wrist system has been developed and integrated into Finley. It employs a purely magnetic connection and provides electrical continuity to the gripper from the forearm so that all included actuators can function. This would allow Finley to handle diverse activities using simpler end-effectors, thereby reducing difficulties related to installation and use of such devices. The paper will discuss the detailed design, testing, implementation, and future work.
Thermal Performance Analysis of an Air Source Heat Pump Coupled With a Solar Air Collector for Domestic Water Heating in a Cold Climate
Authors: Bhadra Sheel , Mwesigye Aggrey 
Published: January 2025

Abstract
Reducing building-related CO2 emissions requires decreasing energy consumption and/or adopting cleaner, more efficient technologies. Air source heat pumps (ASHPs) are increasingly favored for building heating, especially in mild climates. When powered by clean and renewable energy sources, they offer significant potential for emission reduction. However, cold climates present unique challenges for these conventional heating systems, prompting interest in integrating renewable energy sources such as solar energy and energy storage with ASHPs to maintain consistent performance and lower their energy use during the coldest days. This study investigates the performance of an air source heat pump integrated with a solar air collector for domestic water heating in a cold climate. The study aims to quantitatively evaluate the contribution of the solar air collector to the overall reduction in energy consumption, enabling a thorough assessment of the integrated system’s efficiency and operational performance. The coupled air collector – ASHP – TES tank model was developed using a transient system simulation tool (TRNSYS). The impact of collector orientation, with tilt angles between 30 and 70°, collector surface areas between 8 and 16 m2 and mass flow rates ranging from 0.25 to 1 kg/s, which are considered suitable for a 2-ton heat pump, were systematically examined. Results show that the optimal tilt angle for maximizing useful annual energy gain is 51°, aligned with Calgary’s latitude, surpassing alternative tilt angles (30, 40, 60, and 70°). Furthermore, the integration of the solar air collector improves the annual average coefficient of performance (COP) by 16 % and reduces the annual average compressor work by 8%. This comprehensive analysis highlights the potential of integrating solar energy with ASHPs to enhance hot water efficiency and reduce energy consumption in cold climates.
Multi-Objective Optimization for Performance Enhancement of a Toroidal Propeller Using CFD and FSI
Authors: Ciftci Omer , Cadirci Sertac 
Published: January 2025

Abstract
This study aims to optimize the toroidal propeller design to be used for commercial mini-drones. Preliminary CFD simulations were carried out for validation purposes using APC11x5e propeller available in open-source database. In order to validate the numerical results, thrust coefficient, power coefficient, and aerodynamic efficiency obtained from quiescent air and cross-flow-conditions were compared with experimental data. The verified CFD approach showed good agreement with the experimental data for all conditions in obtaining propeller thrust, power, and efficiency values. The same numerical approach was later adopted for the optimization analyses. Optimization was applied to the in-house designed toroidal propeller and was conducted using NSGA-II and Sobol method for DoE combined with FSI. The design variables for multi-objective optimization are the vertical angle, camber angle, and side inclination angle. The aerodynamic objective functions are the thrust and efficiency coefficient and the structural objective function is the strength (von-Mises stress) of the propeller. Ultimately, Pareto optimal designs indicating increased aerodynamic efficiency, higher thrust, and a safer structure are obtained in the defined range of the design variables suggesting that higher thrust, efficiency and strength shall be regarded in the design process of a toroidal propeller for enhanced performance.
Reduced Order Modeling of Thermal Runaway Propagation in Li-Ion Battery Packs With Comprehensive Treatment of Heat Source Terms
Authors: Parhizi Mohammad , Ostanek Jason , Premnath Vinay , Jeevarajan Judith 
Published: January 2025

Abstract
This work aims to develop a novel framework for thermal runaway and propagation modeling, focusing on balancing between computational efficiency and accuracy. The proposed framework leverages a zero-dimensional (0D) modeling approach for heat transfer, facilitating ease of extension to battery pack-level simulations. The model integrates multiphysics phenomena such as decomposition reactions, internal heat generation, gas generation, electrolyte evaporation, and a simplified model for burning the vent gas to enhance accuracy without sacrificing computational efficiency. The thermal node model is also integrated with an electrochemical model, i.e., the pseudo-two-dimensional (P2D) model. These additions enable the framework to capture key physical processes contributing to thermal runaway while maintaining computational feasibility. The thermal node representing the Li-ion cell in this study can be replaced by a thermal mass and finite element component to improve the spatial resolution of the thermal model. The developed model was used to simulate the thermal runaway of a single 18650 Li-ion cell triggered using external heating and was calibrated against experimental data. The computational efficiency of the model enabled its integration with optimization tools for parameter estimation and model calibration. The model was then extended to a battery pack containing 25 Li-ion cells to investigate the likelihood of propagation under varying conditions.
Nonlinear Dynamics Simulation of a Shoulder Link of KINOVA GEN3 Robotic Arm Over a Range of Frequencies
Authors: Yousuf Louay S. 
Published: January 2025

Abstract
A robotic arm of KINOVA GEN3 with a shoulder link is considered in this paper over a range of frequencies. Robotic arms perform precise manipulations in a wide variety of applications from factory automation to medical surgery. Chaos motion is happened due to the impact between the actuator and its base frame due to the tension and compression pressure at the shoulder link. In industry when the robot is working, the stress is concentrated around the shoulder link and might cause failure and fracture the mentioned part due to the chaotic motion. The level of chaos is investigated using sample entropy, an approximate entropy, permutation entropy, and fuzzy entropy. A fast and slow algorithm in MatLab software is prepared to solve for the entropy values. Range-A and Range-B algorithm of entropy is added to check the results of entropy. The higher value of entropy, the more chaos motion. The KINOVA GEN3 robotic arm is moved with seven degrees of freedom. The chaos level is discussed at different frequencies ranges of shoulder links such as 250000, 500000, 750000, and 1000000 Hz are used. The frequency and mode shape have been calculated using Finite Element Method of partial differential equation toolbox in MatLab software. It can be noticed that the motion of shoulder link is chaotic at frequency range 1000000 Hz.
Monitoring Invasive Insects Using Artificial Intelligence
Authors: Golan Aviad , Liao Yabin , McNeill Seth 
Published: January 2025

Abstract
The Asian citrus psyllid (ACP), Diaphorina citri Kuwayama (Hemiptera: Liviidae), is a significant pest for citrus growers in the U.S., due to its ability of transmitting the bacterium responsible for huanglongbing (HLB) disease among citrus trees. The traditional ACP monitoring and controlling methods are time-consuming and labor-intensive. Developing innovative methods to detect ACP and design intelligent traps could greatly enhance management of psyllid infestations and HLB transmission. Monitoring ACP calls between males and females provides an opportunity to detect ACP presence, lure males into traps, or disrupt communication by introducing synthetic calls that interfere with the male signals. This paper presents an artificial-intelligence-based method to detect and classify ACP calls leveraging the multi-resolution feature of the continuous wavelet transform (CWT) technique and the feature extraction and classification capability of deep learning neural networks (DLNN). This method successfully identifies calls with a high accuracy rate exceeding 93%.
Feasibility Study of Self-Lubricated Load Bearings’ Design Within Plug Sliding Door System on Railway Vehicles
Authors: Zhang Liangliang , Zhang Long , Sun Peng , Zhang Tao , Lu Hang , Chen Dongdong , Wu Lvxian , Yin Yanbo 
Published: January 2025

Abstract
The door system on modern railway vehicles is a crucial element in ensuring smooth operation and passenger safety. The current essential load-bearing components within the plug sliding door systems on urban rail vehicles are mainly constructed from metal materials. The imperative maintenance to ensure door safety and operational efficiency is recognized within the industry. During the maintenance of the metal load bearing, the repairers need to remove the old lubricant grease and apply a new one. This procedure increases the cost of the door system’s maintenance and poses the risk of environmental contamination. To address the above consequences, the car builder and the door supplier embark on an initiative to explore the feasibility of replacing these key metal components with non-metal alternatives. This change is aimed at potentially getting rid of the need for lubrication during a single vehicle overhaul period. This article underscores a strategic approach toward enhancing the reliability and cost-effectiveness of urban rail vehicle operations.
AI-Based Machine Condition Monitoring System
Authors: Morris Cameron , Chauhan Vedang , Panchal Sunny 
Published: January 2025

Abstract
This paper describes the approach for an AI-based machine condition monitoring system that utilizes machine learning (ML) and deep learning (DL) techniques to monitor and analyze the condition of machines in various industries. This system employs advanced algorithms and ML models to continuously collect data from sensors, such as vibration sensors, level sensors, and vision sensors, among others. By analyzing this data, the AI system can detect anomalies, predict potential failures, and provide valuable insights into the health and performance of the machines. To demonstrate the feasibility of the concept, the system has been created and implemented for a rotating bowl feeder, a common piece of machinery used for the orientation of parts. Parts ranging from small washers to long-length screws can be dropped into a bowl feeder to be oriented for the next process. As parts become larger and more complex, bowl feeders tend to have more difficulty feeding parts at a high rate (200 to 300 parts/min) and can experience jams. This issue can be compounded if an operator is working on other machinery and doesn’t notice a fault immediately. This paper focuses on using AI for bowl condition monitoring using an ultrasonic level sensor, vibration sensor, part counting sensor, and vision sensor.
Design and Analysis of an Custom Battery Pack Using Solar Energy for an Autonomous Vehicle
Authors: Fernandez Nathan , Lopez Alan , Nandikolla Vidya K. 
Published: January 2025

Abstract
The paper demonstrates the development of a custom lithium-ion battery pack with incorporation of a solar charging system. The design process includes: meeting the power requirements, incorporating a Battery Management System (BMS), Computational Fluid Dynamics (CFD) analysis, and a solar charging system. The Orion Jr 2 BMS and Orion Jr 2 software utility were used to manage the 50V (nominal), 14S8P, 18650 lithium-ion battery pack. CFD analysis was conducted and it was found that simulations from CFD analysis were ideal but deviated from experimental results as the battery pack was not well-balanced, which affected its performance. An electronic DC load was used as an EV emulator. Higher load values exacerbated the imbalance of the battery pack; a load of 8A (448W max) resulted in a maximum delta voltage value of roughly .8V between the cells near the end of the test compared with a value of .16V near the end of the 2A (112W max) test. This project enabled the deeper understanding of the wide range of multidisciplinary engineering topics required to evaluate the design and manufacturing of a safe custom battery pack that can be used for a small intelligent electric vehicle.
Tactile-Based Autonomous Cable Manipulation: Pose Estimation and Connector Verification
Authors: Rosette Marcus , Armstrong Triniti , Nave Keegan , Brown Jostan , DuFrene Kyle , Fitter Naomi , Davidson Joseph R. 
Published: January 2025

Abstract
Automating dexterous tasks such as cable manipulation using robots has proven technically challenging. While vision systems and specialty grippers are available, they often prove unreliable in occlusion-heavy settings typical of cable manipulation tasks like cable tracing or electrical connectivity tests, and they lack the versatility offered by universal grippers. In this paper, we present two contributions related to cable manipulation in manufacturing operations. First, we show an approach for cable pose estimation that uses only in-hand tactile data from fingertip 3D force-sensing tactile sensors. We created a custom gripper and experimental testbed to collect a dataset of tactile cable grasps. We then trained a Long Short-Term Memory (LSTM) network to predict local cable pose and curvature within the grasp. Our second contribution is a classifier that uses tactile data while pulling on a wire to predict whether the wire is correctly seated in its electrical connector. Cable-pull tests were conducted on a testbed with variable cable attachment strengths. This method of tactile perception may better enable autonomous cable routing and contribute to a less subjective standard for cable-pull tests compared to current practices.
AI-Driven Design and Fabrication of Customized Prosthetic Limbs Using Additive Manufacturing and Patient-Specific Optimization
Authors: Al Kharusi Moosa S. M. , Al Maawali Matasam , Al Owiemri Mohammed S. , Al Mushaifri Said , Al Maimani Sulaiman 
Published: January 2025

Abstract
This research paper presents the development of AI-driven design and fabrication of customized prosthetic limbs using additive manufacturing and patient-specific optimization. This innovative approach leverages advanced technologies including artificial intelligence (AI), machine learning, and 3D printing to tailor prosthetic limbs that not only meet but exceed the biomechanical and aesthetic needs of individuals. The study details the integration of high-density polyethylene (HDPE) reinforced with micro and nano-hydroxyapatite (HA) to enhance the mechanical properties and biocompatibility of prosthetics.
The methodology encompasses a comprehensive data collection phase using high-resolution imaging to capture detailed anatomical data. This data is processed using AI algorithms to optimize prosthetic designs, which are then brought to life through state-of-the-art additive manufacturing techniques. The prosthetics undergo rigorous mechanical testing according to ASME standards, with results validated via Finite Element Modeling (FEM) to ensure durability and reliability under physiological loads.
Key findings demonstrate that the inclusion of nano-HA in HDPE significantly improves the tensile strength and modulus, essential for supporting biomechanical functions. User feedback from initial trials underscores the enhanced comfort and functionality of the AI-customized prosthetics, highlighting significant improvements over traditional designs. This research not only advances the technological frontier of prosthetic design but also significantly contributes to personalized healthcare, offering patients prosthetics that are highly functional, aesthetically pleasing, and uniquely tailored to enhance their quality of life.
Toward Support-Free, Zero-Degree Overhang Additive Manufacturing Using a Wandering Center of Motion
Authors: Todd Brody A. , DeVries Levi D. , Kutzer Michael D. M. 
Published: January 2025

Abstract
In recent years, additive manufacturing (AM) has revolutionized traditional manufacturing processes by enabling complex geometries to be printed with enhanced design flexibility and reduced lead times. Current additive manufacturing processes utilize a planar, layer-by-layer deposition of materials ranging from plastics and ceramics to various composites and metals. While these traditional AM methods enable manufacturers to create made-to-order, functional parts, complex geometrical designs with large overhanging features often require sacrificial support structures to maintain material strength and integrity. This research aims to develop a method of AM that adheres to zero-overhang constraints and eliminates the support structure requirements for complex geometries. The proposed method leverages existing research on spherical layering and introduces a novel approach to AM involving a “wandering” remote center of motion (RCM). Using a wandering (i.e. dynamic) RCM, spherical layering techniques can be applied to more complex geometries, specifically geometries that are not within the set of ‘star-convex’ shapes. This paper introduces the wandering RCM method in 2D by developing an algorithm to define unique RCM positions for a given polygon. The goal is to decompose non-star convex polygons into multiple partitions of star-convex shapes, enabling the complete fill of a polygon using a 2D realization of spherical layering. This work leverages an extension to Chvátal’s Art Gallery Problem, using “guard” positions to define RCM positions within the part’s polygonal geometry. Results from 2D analysis demonstrate the feasibility of filling non-convex polygonal regions under a zero-overhang approach.
Gearbox Fault Diagnosis With Deep Learning Under Variable Operating Conditions
Authors: Kalay Onur Can , Karpat Fatih , Karpat Esin , Dirik Ahmet Emir , Ekwaro-Osire Stephen 
Published: January 2025

Abstract
The gearboxes are indispensable universal components to transfer power and handle precision functionalities in modern industry. Their health management is also essential to minimize workforce risks, increase the level of safety, and avoid machine breakdowns. From this standpoint, the present experimental research work developed a convolutional neural network-based method for diagnosing different levels of tooth root cracks (50%–100%) for spur gears. A series of vibration experiments were performed on a single-stage spur gearbox by employing tri-axial accelerometers under variable working conditions. The effects of different levels of tooth cracks on vibration amplitudes were also investigated within experiments. To address the impact of sensor location on the vibration responses of healthy and cracked spur gear pairs comparatively, a total of three sensor locations were determined and tested in the research work. Within the vibration experiments, the impact of two shaft speeds (300 rpm–600 rpm), three loading conditions (zero-load, 10%, and 20%), and three measurement axes (x, y, and z) on the vibrational amplitudes and fault recognition accuracy were evaluated. The findings showed that the proposed convolutional neural network model classified different root crack levels under variable operating conditions between the overall accuracies of 95.015% and 99.970%.