bluuebunny/crossref_metadata_2025_split huggingface dataset:
Fracture Analysis of Additive Friction-Stir Deposited Non-Ferrous and Ferrous Alloys
Authors: Alam Chowdhury Sadid , Abid Abyaz , Ahmed Radif Uddin , Rahman M. Shafiqur 
Published: January 2025

Abstract
In the dynamic field of metal 3D printing, solid-state additive manufacturing (AM) techniques like Additive Friction Stir Deposition (AFSD) are revolutionizing the production and repair of critical parts in various industries due to their efficiency, environmental benefits, and enhanced material properties. AFSD stands out by enabling the creation of high-strength, fully dense non-ferrous and ferrous alloys that exhibit superior mechanical characteristics. This study offers a detailed examination of the fatigue crack growth and fracture behaviors of non-ferrous (AA6061-T6) and ferrous (4340 steel) alloy specimens processed through AFSD. Employing numerical methods, the mixed-mode fracture properties and propagation of cracks in the structures are evaluated. The model involves modified compact tension specimens (MCTS) subjected to tensile loads to analyze fatigue crack propagation and determine Paris Law constants. The study utilizes ANSYS software’s Separating Morphing Adaptive Remeshing Technology (SMART) tool to predict crack trajectories and fatigue growth under constant amplitude loading. Results from the numerical models, showing stress intensity factors, crack extension, and stress deformation, are compared with analytical outcomes to validate the precision of the models in simulating the mechanical integrity of AFSD-produced parts. Additionally, a comparative analysis of the mechanical properties between traditionally produced alloys and those manufactured via AFSD highlights significant improvements, thereby confirming the potential of AFSD for high-strength structural and repair applications with both ferrous and non-ferrous alloys.
Real-Time Bayesian Estimation of Drilling Tool Lateral Motion by Fusing Physics With Sensor Data
Authors: Song Fei , Shi Kevin , Li Ke , Mahjoub Amine , Ossia Sepand , Loretz Ives , Serafim Robson 
Published: January 2025

Abstract
The performance and health of logging while drilling (LWD) tools can be significantly impacted by the drilling dynamics of the bottomhole assembly (BHA). With drilling dynamics becoming increasingly harsh, BHA whirl is one of the undesired motions the tool can face during drilling operations, where the BHA follows an eccentric rotation about a point along the wellbore other than the geometric center. For example, parasitic motion that causes a plethora of distortions to the echo train, at times manifesting as erratic noise or a complete loss of cohesion has been a measurement quality concern for LWD tools. Advanced motion sensors can be installed only at discrete and limited locations of various LWD tools. If we simply take the sensor measurements as the dynamic states at locations away from the sensors, risks associated with tremendous uncertainty can be present in decision making. The farther away from the sensors, the more uncertain the dynamic states will become. Therefore, robust inference of the motion states at the LWD measurement sensor of interest with motion sensors at discrete locations is critical for risk flagging and quality assurance of LWD answer products.
In this paper, we present a Bayesian data assimilation method that fuses physics with sensor data for inferring the dynamic states at points of interest on the BHA with proper uncertainty quantification. A 4.75 inch-LWD tool has been used as a use case, where the dynamic states at the LWD measurement sensor can be predicted in real time with the measurements at the motion sensor as the required inputs. This was achieved with a developed transfer function that utilizes unscented Kalman filtering technique. The robustness of the transfer function was evaluated with synthetic data obtained from drilling dynamics and finite element analysis (FEA) simulations for various BHA configurations and drilling conditions. It was found that the prediction by the transfer function agrees favorably well with the true states of motion at the LWD measurement sensor. The developed transfer function method was further assessed with experimental roll test data, which is considered as close to drilling conditions. The prediction by the transfer function was found consistently close to the ground truth at various rotation speeds.
Towards Zero Defects Manufacturing: Metrological Automatic Recognition of Elementary Functional Geometries
Authors: Costa Carlos Alberto , Mendonça João Pedro 
Published: January 2025

Abstract
This article addresses the growing demand for efficient and high-quality manufacturing processes, highlighting the importance of the “Zero Defects Manufacturing” concept. It is explained how a standard tool such as a Coordinate Measuring Machine (CMM) — a current and standard method for dimensional inspection — contributes to ensure product quality in such and more rigorous procedure. Subsequently, the paper explains and compare the automatic recognition of Elementary Functional Geometries with the standard method, exploring differences and analyzing the advantages of automatic recognition in terms of efficiency and precision. The article then emphasizes the importance of recognizing elementary geometries and their tolerancing, including the recognition of flat, spherical, cylindrical, and conical surfaces. The primary focus of the work is to adapt partial derivatives to the equations of Gaussian and mean curvatures, highlighting their potential as tools for automatic geometric shape recognition. The article concludes by presenting decision criteria for different elementary geometries, emphasizing their relevance for implementing an automatic recognition algorithm for these geometries.
Chat GPT Inclusive Student Active Teaching for Engineering Education
Authors: Tyagi Pawan , Alshweiki Ali 
Published: January 2025

Abstract
ChatGPT assistive tools can dramatically enhance or limit the intellectual growth of engineering students based on how they are utilized. The utilization of ChatGPT in the assignments given generally after the instructor-focused lectures-based courses is considered a challenge; it is extremely difficult and burdensome to separate students’ original work from the copy-pasted portion of the ChaTGPT-like resources. Here, we present a case study of how ChatGPT was utilized to enhance student mastery of the course content when applied in Student Presentation-based Effective Teaching (SPET), a method invented and advanced by the author. Under this approach, students are allowed to use ChatGPT or any AI resource along with the suggested textbook to prepare answers to the assignments/questions regarding the upcoming discussion topics. Students generally get the assignment 7–15 days prior to the discussion date to do self-study and independently answer the questions. In addition, students are expected to work in groups of 3–5 students to prepare a group presentation for class discussion. Each group member answers the same questions individually before working in the group to develop a threshold level of content mastery and familiarity with the concepts to be discussed. Students are required to submit individual and group presentations by the discussion date in the scheduled class. Students are provided instructions about the rubric that will be used for evaluating the student’s mastery and efforts. Generally, 30% marks are assigned to the level of completion of the assigned questions, 20% grade for the quality of presentation, and 30% grade is assigned for the quality of presentation and demonstrating mastery by giving examples and analogies. 20% marks are assigned for answering the questions or “what if” scenario. The SPET approach allowed us to gauge individual students’ concept mastery levels, mainly via their in-class presentations. This paper discusses insights into the usage permitting the use of ChatGPT in the context of several topics ranging from mental resilience to innovating novel engineering design using the principle of fluid flow and heat transfer.
Developing Carbon Fibre Composite Current Collectors for Structural Battery Applications
Authors: Raju Karthikayen , Dong Sihan , Tay Tong-Earn 
Published: January 2025

Abstract
Structural batteries can perform structural load bearing functions in addition to electrochemical storage. Current collectors that can bear load in addition to their essential function to gather electric current generated by the battery’s electrochemical reactions and convey it to the external circuit can be beneficial in structural battery design.
The objective of this work is to fabricate current collectors and to improve both the mechanical and electrical performance of these to make them both structurally and electrically functional, so that they can be used as structural battery current collectors. The composite current collectors after fabrication are tested in tension to measure the tensile modulus and tensile strength. The electrical performance is evaluated by measuring the resistance of the current collectors along different directions such as along the axis of the fibres and across the thickness of the composites. The measurement of resistance is carried out with a test set-up which eliminates the effect of contact resistance in the measurements by applying pressure during resistance measurements.
The current collectors’ tensile properties and resistivities are compared with one another. Coin cells are assembled with some of the better performing current collectors as a demonstration. The work is concluded with considerations for potential future directions.
Solar Recycling of Aluminum Cans
Authors: Tastet Collin , Alsayyed Basel 
Published: January 2025

Abstract
Planet Earth receives about twelve hours of sunlight per day on average, but global warming poses a serious threat to its climate and ecosystems. One of the main causes of global warming is the emission of greenhouse gases, which trap heat in the atmosphere. Recycling is one of the many actions that can help reduce greenhouse gas emissions and conserve natural resources. Examining the case of Jackson County, NC, where the solid waste and recycling department manages the collection and transportation of recyclable materials from the surrounding area. The transfer station in Jackson County oversees the collection of various recyclable resources, such as paper, cans, and aluminum. These materials are sorted and distributed by Curbside Management, a company based in Woodfin, NC. This transfer station handles about 1,400 tons of paper, 700 tons of cans, and 35 tons of aluminum per year. The cost of transporting these materials to the sorting facility is about $660 per load. Aluminum accounts for approximately 5% of the total recyclable materials, but it has a high market value of $1,231 per ton. According to the American Chemical Society, “Aluminum recycling is profitable because extracting this metal from aluminum ore is expensive, it pollutes the environment, and it consumes a significant amount of energy” [1]. Therefore, recycling aluminum not only saves money, but also reduces environmental impact and energy consumption. However, recycling aluminum is not without its challenges. For instance, Jackson County’s transfer station uses diesel trucks and electric lifts to store and collect recyclable goods. These vehicles and equipment consume fossil fuels and electricity, which generate greenhouse gas emissions and contribute to global warming. Thus, the question arises: how can we achieve zero emissions recycling? This paper hopes to expose the environmental and economic benefits of recycling aluminum and the challenges and opportunities for achieving zero-emission recycling. In addition, the paper will explore some practical solutions, modernizing the method of recycling aluminum cans and recovering their material using simple equipment that uses the sun’s energy to melt a material, instead of using electricity or other traditional sources that uses fossil fuel. This scope aims to investigate environmentally friendly recycling with zero carbon footprint, improving recycling efficiency, and implementing sustainable manufacturing techniques.
Mechanical Engineering Curriculum Modifications and Adaptions to Address Climate Mitigation and Changes in the Energy Mix
Authors: Zenouzi Mansour , Kowalski Gregory J. , Metghalchi Hameed 
Published: January 2025

Abstract
The world needs to reduce carbon emissions to combat climate change. Electrification of building and transportation, data centers that are being built to support commerce and enabling artificial intelligence (AI) necessitates innovative energy solutions to reduce greenhouse gas emissions because of global warming. Decarbonization of the economy, shifting to electricity produced with low-carbon energy sources and building up adequate capacity of renewable energy will take time and will require the discovery of new clean energy sources and innovative technologies to replace the traditional fossil fuel combustion driven power cycles. It is imperative that academic institutions design appropriate curricula that prepare students to tackle the complex energy-related problems faced by society. Students need to understand existing technologies, such as cogeneration or combined heat and power (CHP), which reduces energy consumption, and it can be integrated with renewable energy sources. Weatherization of buildings, which consume more than 40% of the energy in the United States, is necessary to reduce energy consumption; however, many mechanical engineering programs are not offering HVAC as a required or even as an elective course. Curricula should include such topics as: energy storage technologies, e.g., batteries, green hydrogen-generation, power transmission, electrochemistry and membrane technologies that are needed for innovative new power generation technologies such as fuel cells (FC) and salinity gradient energy (SGE) generation.
An Improved and Enhanced Depth Map Approach for Obstacle Detection in Unknown Environments for Reactive Autonomous Mobile Robots
Authors: Raju Vomsheendhur , Selekwa Majura F. 
Published: January 2025

Abstract
Interest in autonomous vehicles has attracted research into developing better navigation systems. Autonomous navigation systems guide unmanned vehicles to traverse any environment without human intervention; they require clear knowledge of the environment to make correct decisions. Information about the environment can be obtained from either ranging or vision sensors. Although ranging sensors such as LiDARs, radars, or sonars can detect the presence and location of obstacles, they don’t provide any information on whether the obstacles are traversable. On the other hand, vision sensors produce images similar to how human eyes perceive the environment, which can be processed to determine whether the sensed obstacle is traversable. Since vision sensors are light, less costly, and consume low power compared to LiDARs or Radars, there is an increasing interest in using them in autonomous navigation of unmanned ground vehicles. The main limitation of vision sensors for this purpose is their failure to directly identify the locations of the obstacles. Often, the location is determined by using a depth map, which is not always accurate and can be very computational. This paper proposes a new method of processing a depth map with improved accuracy. The depth of the closest objects to the stereo camera is extracted using the horizontal and vertical field of view of the sensor. It is further enhanced using morphological operations and the P-Tile segmentation technique to segment the obstacle(s) from its background and noise. This approach generates a denser map without any discontinuities, which leads to improved accuracy in locating close-by objects at less computational cost. Information from this depth map can be used as a low-cost alternative to ranging information in the navigation system for ground vehicles.
Machine-Learning Based Curing Cycle Optimization in Wind Blade Manufacturing
Authors: Kamath Sahil , Caselato Gandia Guilherme , Adab Niloufar , Mehrdad Ehsan , Qian Dong , Lu Hongbing 
Published: January 2025

Abstract
The vacuum-assisted resin infusion mold (VARIM) process is widely used in wind blade manufacturing for its cost-effectiveness and reliability. However, the current method faces challenges such as long curing times and defects due to nonuniform heating across the blade structure. To address this, a multi-zone heated bed setup tailored to blade thickness has been considered. However, determining an optimal temperature for each zone poses a computational challenge, which can be tackled with a novel machine-learning approach. Using a digital twin based on a high-fidelity multiphysics solver, a time-distributed LSTM model was trained to understand complex resin curing dynamics. This eliminates the need for costly lab experiments, as the model learns heating patterns and curing behavior efficiently. Once trained, the ML model acts as a digital twin by predicting the degree of cure for a given temperature setpoint with 96.73% accuracy. This model, when used as a surrogate for a Nelder-mead optimization workflow, improves the curing time by roughly 12.5% and presents a more uniform curing rate throughout the part.
Modeling of Turbulent Non-Premixed Combustion Using High Order Compact Finite Difference Method Combined With a Steady Flamelet Approach
Authors: Mahamud Rajib , Hasan Md Kamrul , Haque Md Azazul 
Published: January 2025

Abstract
Non-premixed combustion accounts for most of the real-world applications; however, numerical modeling of non-premixed flames presents significant challenges due to the substantial computational time required to incorporate detailed chemical kinetic mechanisms and to accurately capture the effects of turbulent fluctuations on reaction rates. The proposed research focuses on developing compact finite difference schemes with high-order accuracy that allow for the detailed and accurate capture of small-scale turbulent structures and the steep gradients typical of combustion fronts, without requiring prohibitively dense computational grids. The compact finite difference scheme, exhibiting lower numerical dissipation and dispersion, is well suited for predicting flame propagation and shock waves. The flexibility of such schemes is beneficial for complex boundary conditions and for accurately modeling the combustion chamber geometries and the interactions of flames with walls and obstacles. The numerical discretization of Navier-Stokes-equations, energy equation, and mixture fraction equation using the compact finite difference method was performed on a FORTRAN code for non-premixed diffusion flame. The turbulence model will apply the standard k-ϵ turbulence model. The turbulence chemistry interactions model works on the principle of decoupling the combustion chemistry from the fluid flow through the fundamental assumption of a steady-state flamelet, where the steady flamelet structure was obtained from the combined statistical analysis of mixture fraction and the instantaneous scalar dissipation rate. The steady flamelet model predicts temperature and the species mole fraction using the mixture fraction obtained from the compact finite difference code and coupled it with the probability density function (PDF) model. The PDF method was applied with a beta distribution function to predict the temperature and species mole fraction and integrate the distribution across the mixture fraction and scalar dissipation space using the flamelet library. A counterflow geometry of fuel and oxidizer was accounted for in the modeling of the steady flamelet approach. The flamelet library was generated using a zero-dimensional (0-D) chemical kinetics solver called Cantera for methane-air combustion. The validation of the model will be carried out using experimental data from the literature to establish the accuracy and reliability of the code.
A Robotic Simulation Package: Architecture, Algorithms, and Applications
Authors: Dabiri Arman , Qi Shian , Zhao Shihao 
Published: January 2025

Abstract
This paper presents the development of a robotic simulation package designed to address forward and inverse kinematic problems in multibody dynamic systems. The core solver is enhanced by incorporating the special Euclidean group 𝕊𝕆(3) and quaternions, which are essential for accurately handling orientation representations. This integration allows the simulation to capture rigid body motion and joint constraints precisely. Additionally, the package employs the Moore-Penrose inverse technique and Lagrange multipliers to solve the resulting constrained optimization problem effectively. The software architecture is thoroughly described, highlighting the framework within which the robotic simulation package operates. The package uses an object-oriented design, ensuring a flexible and user-friendly platform. Each module of the software undergoes rigorous unit testing and validation against a variety of multibody systems. Finally, the paper demonstrates the developed package’s motion planning and simulation capabilities through selected robotic systems.
A Reinforcement Learning Approach for Production Scheduling in Industry 4.0 Considering Limited Data
Authors: Lopez-Salazar Camilo , Ekwaro-Osire Stephen , Ekwaro-Osire Henry , Hopfmüller Stephan 
Published: January 2025

Abstract
As the global pursuit of net-zero emissions by 2050 intensifies, the role of renewable energy sources such as solar photovoltaics becomes increasingly relevant. However, their intermittent nature and energy price volatility present challenges for energy-intensive sectors like manufacturing. Integrating renewables into production schedules emerges as a strategy to mitigate costs and environmental impact. Industry 4.0 technologies, particularly machine learning, offer opportunities for optimizing energy consumption. This paper introduces a NeuroEvolution of Augmented Topologies (NEAT) algorithm for energy management operations tailored for industrial prosumers. Inspired by the evolution of biological nervous systems, NEAT evolves a neural network’s topology and parameters to make optimal decisions for a given environment. The study’s objective is to generate a day-ahead energy schedule that considers renewable energy and primary grid utilization using limited information on the system’s current state. A comparative analysis between the NEAT scheduler, a traditional supervised learning-based scheduler, and a deterministic mixed-integer linear programming (MILP) solver as a baseline demonstrates the effectiveness and robustness of the proposed method, which reduced the electricity bill by 18.69% over one week of real data simulation, using just six hours of previous measurements.
Study of the Stresses and Friction During Blink Between the Posterior Surface of the Eye Lid and Anterior Surface of Cornea, Using Finite Element Analysis
Authors: Pai Raghuvir Ballambat , S. Ve Ramesh , Sundaram Meenatchi , Shah Mohammed Abdul Khader , Hazarika Manali , Hariharan Girish , Rao Sohan , Hebbandi Abhilash 
Published: January 2025

Abstract
During the blinking process, the posterior surface of the eye lid interacts with the anterior surface of the cornea, which is a complex tribological system. In this paper, a simplified eye model is used to investigate the interaction between the above two surfaces during blink, using Finite Element Analysis (FEA).The analysis simulates the eye lid motion determined experimentally and available in the literature. The deformation and shear stresses on the ocular surface are recorded. Large deformations are observed during the movement of the eye lid at the point where the peripheral cornea forms the limbus. The large deformations due to the shear stresses is the result of high frictional forces generated due to poor quality of tear film between the interacting surfaces, resulting in dry eye condition. The FEA simulation enables a better understanding of the stresses and friction during the blinking process and therefore the dry eye condition.