bluuebunny/crossref_metadata_2025_split huggingface dataset:
Enhancing Defective Casting Classification in Manufacturing Using Deep Learning: A Case Study With VGG16
Authors: Gurupatham Sathish , Palreddy Chandana , Vijayakumar Harshith , Loignon Tyler 
Published: January 2025

Abstract
In the manufacturing industry, the detection of defective products, such as castings, is crucial for ensuring product quality and preventing potential safety hazards. Deep learning techniques, particularly convolutional neural networks (CNNs), have emerged as powerful tools for automating the detection and classification of defects in manufacturing processes. This study explores the application of deep learning, specifically utilizing the VGG16 model, for classifying defective castings from normal castings in the manufacturing field.
The dataset comprises a collection of images depicting both normal and defective castings. Initially, the VGG16 model was trained on a dataset consisting of 4100 training images, 1200 validation images, and 500 testing images. The model achieved a moderate accuracy of approximately 60% on the testing set, indicating its ability to classify defective castings to some extent. However, the model’s performance in terms of precision, recall, and F1 score was suboptimal.
To enhance the model’s performance, several strategies were employed. First, the VGG16 layers were fine-tuned by selectively unfreezing and training certain layers of the pre-trained model. This process allowed the model to adapt more effectively to the nuances of the specific dataset. Additionally, regularization techniques, such as dropout and L2 regularization, were incorporated to prevent overfitting and improve generalization performance.
Moreover, the learning rate of the model was adjusted, and different values were experimented with to optimize its performance. Learning rate schedules and adaptive optimizers, such as the Adam optimizer, were utilized to dynamically adjust the learning rate during training.
Furthermore, the training dataset was augmented by applying various data augmentation techniques, such as rotation, flipping, and zooming, to increase the diversity of the images. By balancing the dataset to include an equal number of training, validation, and testing images, consisting of 4100, 1200, and 500 samples respectively, a significant improvement in the model’s performance was observed.
After implementing these enhancements, the model achieved impressive results, with an accuracy of 89.6%, precision of 91.4%, recall of 89.6%, and F1 score of 89.5% on the testing set. These findings underscore the effectiveness of deep learning approaches, particularly when combined with fine-tuning, regularization, and data augmentation techniques, in accurately detecting and classifying defective castings in the manufacturing industry. Such advancements have the potential to streamline quality control processes, reduce production costs, and enhance overall product reliability and safety in manufacturing operations.
Enhancing Degradation Models of Mechanical Systems Through Uncertainty Quantification
Authors: Ajayi David Omodolapo , Ekwaro-Osire Stephen , Gandur Nazir Laureano , Lopez-Salazar Camilo 
Published: January 2025

Abstract
Over the years, various techniques have been developed to construct remaining useful life (RUL) models for reliability analysis based on run-to-failure experiments for rolling element bearings. The naturally produced experimental data from open-source repositories, XJTU-SY and PRONOSTIA ball-bearing datasets, were used in this study. The PRONOSTIA dataset has no records for its location of damage, and knowledge of the bearings’ damaged components will benefit studies requiring it. Both datasets are testaments to fault commencement and complete failure occurring at different times for identical bearings running under the same operational conditions. Hence, a bearing will have multiple points where incipient fault points (IFs) and end-of-life points (EOLs) could occur. Thus, attributing all this information to a singular degradation model is essential. This study answers the research question: Can quantification of uncertainty enhance degradation models for rolling element bearings? The objectives of this paper are to (1) identify the unknown damage locations, (2) quantify the uncertainty of the IFs and EOLs, and (3) determine the degradation model considering uncertainty. Due to limited data from both datasets, the study employs the maximum entropy principle (MaxEnt) to define uncertainty for both the IFs and EOLs. This is achieved by plotting probability density functions (PDFs) such that both the IFs and EOLs transcend as random variables (RV) instead of deterministic parameters. MaxEnt is further used to construct a degradation model by connecting the PDF, cumulative distribution function, and reliability model. An approach based on uncertainty quantification was proposed to determine a degradation model of ball bearings. The approach is shown to be generalizable to other engineering systems.
Analysis of Convection Heat Transfer on a Plate With Porous Media
Authors: Khoso Abdul Qadeer , Fareedi Atiq Ur Rehman , Khan Hurmat , Buonomo Bernardo , Manca Oronzio , Nardini Sergio 
Published: January 2025

Abstract
The use of porous media for the purpose of heat transfer is gaining ground these days. It also gives an opportunity to combine impinging jet flows (IJF) with porous media for the purpose of heat transfer enhancement. This paper presents the experimental and numerical study of the analyses of heat transfer on the plate with diverse nozzle to plate distance or channel height (H) in the presence of the porous media placed over the heated plate using IJF. IJF works by throwing the pressurized fluid from the nozzle exit and pounding on the heated plate after passing through the metal foam (MF) in our case. This passage of fluid from the MF extracts heat within the MF and cools down the heated plate. The repeated experiments have been performed with various variables under consideration mainly: diversity of metal foam in terms of PPI and various channel height to jet diameter ratio and diverse fluid flow. The outcome of the experimental study suggests that generally MF of 20 PPI with H/Dj ratio 8.57 has a greater value of Nusselt number (Nu) on the given Reynolds number (Re) in turbulent case while in laminar case MF of 10 PPI has greater value of Nu. The results are different when the H/Dj ratio changes. The crux of this study describes the suitable configuration of MF at specific H/Dj ratio for greater heat transfer. The results could assist in designing a suitable heat exchanger for application.
Environmental Impact and Techniques for the Reuse and Disposal of Automotive Batteries
Authors: Capata Roberto , Calabria Alfonso 
Published: January 2025

Abstract
Lithium batteries represent the best solution for storing electrical energy; they are now an integral part of electronic devices (laptops and smartphones) and electric vehicles. Their reuse (Second Life) has taken hold for some time now: at the end of their initial operating life cycle, they can be used for other purposes. As far as recycling is concerned, several studies and research have focused on recycling methods. There are over 300 studies on various recycling methods and techniques, of which more than 75% are concerned with metallurgical processes, and about 70% are about metallurgical processes. The results show that all the materials that make up the battery, including lithium, can be recycled. In this paper, the authors want to analyze the state of the art of reuse and recycling, detect any critical issues of the process in its entirety, and — where possible — proceed to its optimization. The recycling and reuse of batteries constitute a new economic and industrial flow; batteries represent virtual mines from which to extract materials not present in the national territory. The production of batteries constitutes the most significant environmental impact of the same technique, and their reuse is beneficial for reducing emissions. Nevertheless, there is a need for standardization of disassembly methods on a large scale, which could be ensured by the increase in the market but requires constant research.
Enhancing Tubular Structure Integrity Assessment: A Machine Learning-Integrated Failure Assessment Diagram Approach
Authors: Elkhodbia Mohamed , Negi Alok , Barsoum Imad , AlFantazi Akram 
Published: January 2025

Abstract
Failure Assessment Diagrams (FADs) are a cornerstone of structural integrity analysis, but their complexity can limit their adoption in certain engineering contexts. This study introduces a novel machine learning approach designed to make FAD analysis more accessible and computationally efficient. The method leverages artificial neural networks (ANNs) trained on FAD models derived from rigorously calculated numerical contour integrals. Finite Element Analysis (FEA) was used to generate the FAD envelopes that is used to train the ANNs. The resultant ANNs demonstrate several capabilities: generating precise FAD envelopes, predicting limit loads, and incorporating the effects of residual stress via the Φ parameter. This work highlights the potential of ANN-based FADs as a powerful and adaptable tool, streamlining complex structural integrity evaluations without sacrificing accuracy.
Anatomical Data Acquisition Protocol Using Low-Cost 3D Scanners for Project Zule: 3d-Printed Breast Prostheses for Women Undergoing Mastectomy
Authors: Helguero Carlos G. , Cansing Jose , Maldonado Fausto A. , Saldarriaga Carlos , Amaya-Rivas Jorge L. 
Published: January 2025

Abstract
Inspired by the Zule Project, which aims to assist underprivileged women in developing countries who have undergone mastectomies and seek affordable, personalized breast prostheses with additive manufacturing, this study establishes a data acquisition protocol for 3D scanning. This non-invasive technique captures patient anatomy to generate a digital representation of the body surface. The primary objective is to quantify breast volume, morphology, and bilateral symmetry, under normal brassiere pressure, for subsequent prosthesis fabrication using additive manufacturing (AM) technology. To develop the data acquisition protocol, a standardized marker system was employed. This system incorporates a global reference marker and localized markers consistently positioned on each patient. Healthy volunteers participated in the study, undergoing bra and bra-free scans using a low-cost 3D scanner. Subsequent image processing utilized computer-aided design (CAD) software. Leveraging the global marker, corresponding anatomical landmarks were identified and processed through a custom code to quantify positional variations between the two named scenarios.
Structural Health Monitoring of Wind Turbine Blades Utilizing Guided Waves
Authors: Lu Runye , Shen Yanfeng 
Published: January 2025

Abstract
Wind turbine blades (WTBs), as crucial components of wind turbine generators, are susceptible to structural damage due to severe weather conditions during operation periods. This paper proposes an active structural health monitoring (SHM) system, leveraging guided waves for the WTBs diagnosis. It comprises electro-magnetic actuators, piezoelectric sensing units, and data acquisition and signal processing modules. An algorithm incorporating spectral wave energy and cross-correlation is developed for precise damage detection. To validate the system and algorithm, a simplified SHM system is implemented on a laboratory-scale WTB prototype. Artificial damage simulated by the bonded mass block can be accurately detected and located. For the subsequent phase, a full-scale test is conducted on a XX-192 WTB (anonymity of XX for confidential purpose), exceeding 90 meters in length and fixed on a foundation tower as a cantilever beam under fatigue tests. The practical durability and stability of the system is demonstrated, subjected to over 500,000 flapping and edging cyclic loadings. The system precisely detects the artificial delamination in the fashion of a bonded composite sample on the WTB, achieving the detection of a small delamination area of 80 mm* 30 mm. The paper culminates in summary, concluding remarks, and suggestions for future work.
Estimating Scaled Cloud Height in Pulse Jet Mixed Vessels
Authors: Pease Leonard F. , Bamberger Judith Ann , Daniel Richard C. , Minette Michael J. 
Published: January 2025

Abstract
To suspend and mix radioactive slurries without pumps or impellers, the performance of pulsed jet mixed vessels has been investigated. The slurry in the pulse tubes is expelled impinging upon the vessel floor and lifting solids to a “cloud height” in a cyclic process. Experiments measuring cloud height for a variety of waste simulants have been conducted for vessel configurations that varied vessel diameter, fill height and pulse tube number, volume, and nozzle diameter.
The elevation to which suspended particles rise is termed the cloud height and derives from a balance between the fluid forces of the jet and the gravitational forces acting on the particle ensemble. Here we use scaling to estimate the cloud height based on conservation of mass and momentum and jet similarity.
We find that the cloud height varies with the nozzle exit velocity to the 2/3 power in agreement with observations.
Development of a Geometric Accuracy Machine Vision Application for Metal Castings
Authors: Jones Michael , Chigurupati Poojith Chowdary , Taheri Hossein 
Published: January 2025

Abstract
Metal castings provide critical products to several industries such as aerospace, medical, and automobile. A prominent level of geometric accuracy is required in high-value casting components to ensure an adequate level of quality. While traditional methods of measuring critical features on metal castings such as laser scanning measurements have proven effective to determine geometric accuracy, these techniques are often time consuming and require costly equipment and labor time. The aspect of geometry measurement in Industry 4.0 involves the application of advanced digital technologies to streamline the measurement of geometric features through machine vision techniques. Machine vision systems utilize cameras and image processing algorithms to capture and analyze visual data from manufactured parts to accurately measure geometric features, enabling real-time inspection and quality control. While previous research has been conducted into the accuracy and efficiency of laser scanning methods and gauge measurement methods, little research has been conducted in the application of machine vision for geometric accuracy of metal castings. In this study, a machine vision application is introduced and utilized to analyze, measure, and evaluate parts based on the critical geometry of two castings. The geometry measurements have been done on as-built components using caliper and machine vision techniques. The results of the machine vision application are compared to manual measurements using a caliper. The study demonstrated the effectiveness of machine vision technology in accurately measuring geometric features of metal casting components. Potential for the introduction of this application of machine vision to the metal casting industry in the context of industry 4.0 is also discussed and an implementation framework is introduced as well.
Aerodynamic Analysis of a Dielectric Elastomer Membrane in the Gust Environment
Authors: Raihan Gazi Abu , Chakravarty Uttam 
Published: January 2025

Abstract
The design of the wings of UAVs using dielectric elastomer membranes is critical for analyzing and enhancing their maneuverability, particularly in turbulent atmospheric conditions, popularly known as gust environments, necessitating advanced design innovations. This study presents a comprehensive aerodynamic analysis of a dielectric elastomer membrane (VHB 4910) subjected to sinusoidal profile gust environments. A fluid-structure interaction model, coupling the finite element model of the membrane with a computational fluid dynamics model of the surrounding airflow, is developed to study the coupled effect of the gust and its structural response. Aerodynamic coefficients are derived from forces calculated in numerical simulations for unsteady gust velocities. Results demonstrate the aerodynamic and structural response of the membrane which is similar to the Dryden and von Karman gust models demonstrating minimal discrepancies in the gust velocity profile. This study offers valuable insights into the aerodynamics and maneuverability of UAVs in gust environments, contributing to the advancement of UAV design for improved stability and performance under adverse atmospheric conditions. Results from the computational simulations reveal that the electroactive membrane demonstrates varying aerodynamic response in unsteady sinusoidal load potential in enhancing aerodynamic performance under variable gust conditions. By actively adjusting its shape in real-time, the membrane effectively reduces drag and increases lift, thereby improving the overall aerodynamic efficiency.
Simultaneous Perturbation Algorithm Tuned Oscillation Controller for Automotive Driveline With Kalman Filtering-Based Compensation for Dead-Zone Nonlinearity
Authors: Yonezawa Heisei , Yonezawa Ansei , Kajiwara Itsuro 
Published: January 2025

Abstract
Advanced active control algorithms have addressed the reduction of vehicle driveline oscillations with the purpose of improving component lifespan and drivability. However, the majority of the current works require the important controller parameters to be determined subjectively, placing a significant burden on designers to make adjustments. This study offers an effective model-based driveline vibration controller tuning technique that specifically takes nonlinear backlash’s negative effects into account. Initially, a driveline model is created that incorporates a dead-zone impact of backlash. To reduce a driveline’s low-frequency resonance, a baseline controller reflecting H2 control theory is used. To handle the backlash nonlinearity, the controller is coupled with a control mode switching-based compensation. For identifying whether the system is in the backlash mode or the contact mode, the state vector is estimated by a Kalman filter. The Kalman filter-based state estimation enables switching of the two control modes systematically and stably. A computationally efficient approach, namely the simultaneous perturbation stochastic approximation (SPSA), finds out the best values of their significant design parameters that are incorporated in the active control system. Multiple comparative simulations confirm the effectiveness of the proposed active oscillation controller adjusted by the SPSA. The resilience is assessed for several patterns of driveline dynamics variations. Consequently, it is discovered that the improved vibration suppression performance is stemming from the backlash compensation.
Efficient Controller Tuning Technique for Model-Free Active Vibration Control Based on Virtual Controlled Object
Authors: Yonezawa Ansei , Yonezawa Heisei , Kajiwara Itsuro 
Published: January 2025

Abstract
Vibration control based on a virtual controlled object (VCO) is a simple and practical model-free active vibration suppression strategy. To utilize VCO-based model-free control, controller tuning plays the critical role. This study presents a computationally efficient tuning strategy for the VCO-based model-free active vibration controller. A state equation for model-free controller design is derived based on an actuator model and the VCO. The parameters of the VCO are determined so as to achieve the vibration suppression in the predetermined controlled frequency band. Then, we introduce the reference controlled object (RCO). The control performance of the VCO-controller is evaluated through the vibration control simulation for the RCO. Iteratively executing the vibration suppression simulation for the RCO, the suitable design parameters of the VCO-controller are searched on the basis of the simultaneous perturbation stochastic approximation (SPSA) algorithm. The nonlinear parametrization technique enhances the tuning efficiency. The validity of the tuning scheme is demonstrated by applying it to the design problem of the VCO-based model-free linear quadratic regulator (LQR). The numerical simulations verify the applicability of the VCO-LQR tuned by the proposed approach to wide variety of controlled objects other than the RCO.
Mechanical Property Evaluation of Sublimation Agents on Nanostructure Stability in Semiconductor
Authors: Hanawa Yosuke , Zhang Jianliang , Sasaki Yuta , Sawada Koichi , Yoshida Junichi , Xu Minghan , Mohit Mohammaderfan , Sasmito Agus P. , Sakuma Atsushi 
Published: January 2025

Abstract
Collapsing nanostructures due to the surface tension of cleaning solutions pose a significant problem during the drying process in semiconductor manufacturing. The sublimation drying method has been proposed as a potential solution, but achieving collapse-free drying has been challenging. This study investigated the correlation between the mechanical properties of sublimation agent thin films and the collapse rate of nanostructures during sublimation drying. First, various mechanical properties — such as Young’s modulus, viscosity coefficient, plateau stress, and rupture stress — of eight sublimation agent thin films were determined by conducting indentation tests using a spherical indenter. Next, sublimation drying of Si substrates with nanostructures was conducted using these sublimation agents, and the pattern collapse rate of the nanostructures was assessed. The study found a correlation between the rate of viscous change during the phase transition of the sublimation agent from liquid to solid and the collapse rate of nanostructures. However, no correlation was found between other mechanical properties and the collapse rate. These results suggest that the viscous change during the solidification of the sublimation agent exerts stress on the nanostructures, leading to pattern collapse. This study highlights the importance of considering the viscous change during sublimation agent solidification for enhancing nanostructure stability in semiconductor manufacturing.
Investigation of Copper Interconnects Suffered Reliability Physics for Advanced Packaging Architecture
Authors: Chuang Jui-Chang , Lee Chang-Chun 
Published: January 2025

Abstract
This study offers an insight into copper interconnects subjected to diverse physical loading conditions associated with the thermal, electrical, and mechanical domains. In order to estimate primary contributions among stress, temperature and electrical effects, a novel specimen has been designed to analyze the influence under multiple loading through images of distinct failure defects in this study. Three geometric variations in line widths for copper interconnects have been designed, specifically 15 μm, 10 μm, and 5 μm. This research examines the reliability physics of copper interconnects in advanced packaging architectures, assessing the effects of electromigration (EM), electromigration combined with thermomigration (TM), and stress migration (SM), alongside three distinct physical loading scenarios. The analytic results indicated that electrical resistance of copper interconnection is increased rapidly with reliability physics especially temperature condition. Copper interconnects with a width of 5 μm exhibit a 19.32% increase in electrical resistance under combined EM and TM effects, relative to solely EM effect. This experiment provides a method to estimate electrical resistance change under various reliability physics loading for advanced packaging structures featuring diverse copper interconnection design.