bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Comparative Classification Study on the Use of Acoustic Emission Signals for Surface Roughness Condition Monitoring in End Milling of Stainless Steel
Authors: Abu-Mahfouz Issam , Banerjee Amit , Rahman Esfakur AHM 
Published: February 2024

Abstract
Surface roughness quality has implications on the functionally, assembly, service life, and appearance of the machined product. Considering the complex nature of metal cutting processes, computer simulation models may not provide the needed accuracy to predict surface conditions under all cutting conditions. Therefore, machine learning (ML) techniques can provide more reliable predication models that are based on real time cutting process sensory data. The implementation of artificial intelligence (AI) techniques in the monitoring of manufacturing processes has been gaining momentum. The focus of this study is to predict the surface roughness using acoustic emissions (AE) signals during the dry end milling of stainless steel. AE sensors have been widely used to monitor the condition of structures and manufacturing processes. Furthermore, acoustic sensors are non-invasive and can be used at any location without disrupting or stopping the machining process. Features extracted from the AE signals are used as surface roughness quality indicators. These features include frequency bands averaged amplitudes, statistical quantities of the wavelet decompositions, raw signal RMS values, and crest factor. In this work, several machine learning algorithms are used to process the extracted AE features for surface roughness characterization. The total AE features are first processed for feature set reduction since many of the features are highly correlated. This is done using both supervised and unsupervised feature reduction and subset selection methods. The features extracted from supervised feature reduction methods are used to train three supervised classifiers — k-nearest neighbor (kNN) classifier, a radial-basis function support vector machine (RBF-SVM), and a random forest (RF) classifier. The reduced feature set from the unsupervised feature reduction methods are used as input to two unsupervised clustering methods — K-Means and DBSCAN. The classifier models are trained using multi-fold cross-validated mix of subsets of the reduced features. In this study we have used ten models using two-fold cross validation for training and validation for the supervised learning methods. The results of supervised classification are compared to unsupervised clustering and are reported for an average of the ten models (or ten runs with distinct initializations of the clustering algorithm), along with a detailed nonparametric testing to verify statistical significance in performance level between pairs of algorithms.
Tension Control in Filament Winding Using Constant Force Mechanisms
Authors: Ye Siwei , Mo Yaru , Ou Haihua , Bi Shun , Johnson Shane 
Published: February 2024

Abstract
The control of fiber tension has a significant impact on the geometrical and structural characteristics of the final product quality in filament winding. Controlling the tension and reducing disturbance in robotic filament winding is a great challenge due to the complex winding interface, sharp turning in trajectory, and fiber mechanics involved. Typical tension control strategies, e.g., rigid actuator or series elastic actuators, require costly electronic components or complex tension feedback control. Alternatively, constant force mechanisms (CFMs) may be incorporated into the filament winding system to control the tension force passively for a fixed displacement range. The objective of this study is to develop a large-stroke constant-force mechanism through design optimization and experimentally study the performance of force control with Proportional-Integral-Derivative (PID) and fuzzy controllers. A 2 × 2 Graph based path topology optimization was developed, and the resulting fixed-fixed beam was out-of-plumb with a slight out-of-straightness imperfection. In the experimental setup of the mock filament winding system, the fiber starts from a motorized feed mandrel, passes through pulleys with mounted tension/position sensors on a CFM array and finally reaches a motorized inlet mandrel. Both a PID controller and a Fuzzy controller were developed to evaluate and compare the performance of: (1) the CFM and rigid actuator systems, and (2) the controller types. The result shows that compared to the rigid actuator system, the CFM actuator system reduces peak tension overshoot by 34.17%∼45.42% and the tension coefficient of variation by 7.14%∼8.40%. In addition, the Fuzzy controller provides greater robustness, i.e., it allows more errors in position while continuing to maintain tension control performance. In conclusion, the CFM control strategy with PID or compatible Fuzzy control presents a great performance advantage over the existing tension control actuator strategies. The application of CFMs shows great potential in various force control in cable-based systems, e.g., vibration mitigation against strong wind in bridge cables, energy absorbing from earthquakes in buildings, elevators, etc.
Development of a LoRaWAN-Enabled Unmanned Aerial System for Autonomous Real-Time Surveillance and Monitoring
Authors: Sunny Manish Edassery , Umesh Pruthviraj , Gangadharan K. V. , Shetty Devdas 
Published: February 2024

Abstract
This research paper proposes a LoRaWAN-enabled Unmanned Aerial System (UAS) for autonomous real-time surveillance and monitoring for agriculture fields. Traditional security measures in agriculture face challenges due to their high costs and labor-intensive nature, especially on large farms. The paper outlines the development of the complete system, explaining each block that forms its components. Additionally, it presents an experiment where a UAS successfully flew to a specific location triggered by motion sensors connected to LoRa nodes as part of surveillance and monitoring of an area. The results demonstrate the system’s effectiveness in real-time monitoring and autonomous operation. By leveraging LoRaWAN technology, this system offers a promising solution to enhance agricultural security and efficiency. The integration of technologies showcased in this research contributes to the field of precision agriculture.
Generative Adversarial Networks Guided Lightweight Design Based on Shakedown Strength Constraint
Authors: Huang Songhua , Zhang Lele , Chen Min , Liu Zhiyuan , Lim Eng Gee 
Published: February 2024

Abstract
Compared to the classically performed elastic limit rule in structural lightweight optimization design, the shakedown analysis can help determine the non-failure external load region which is greater than the elastic limit but less than the plastic limit, without accounting for the loading history. By sacrificing some abundant load-bearing capacity, a lightweight design of the structure can be achieved. Combining the shakedown analysis with parameter optimization can help resolve parameter optimization intervals while considering structural fatigue in complex loading conditions. This approach ultimately translates into solving a max-min optimization problem. However, due to the nonlinear relationship between the shakedown limit and massive design parameters, a rugged ‘performance landscape’ which is considered to be extremely unfriendly will be led through a traditional surrogate model, sometimes it is not even possible to generate. Moreover, the total time-consuming of simulation data acquisition is unacceptable. The current research utilizes Generative Adversarial Networks (GANs) to enrich the surrogate model with shakedown strength boundary constraints. Benefiting from the unsupervised learning feature and the similarity between the generated data and the real data, GANs are able to build relationships in the above optimization problem quickly, which is an innovative and executable technique to the lightweight design under structural strength constraints. The research first presents the shakedown theory and its numerical implementation. Then, a shakedown strength constrained for mass minimization form and the GANs flowchart for solving this problem are built, and a benchmark example is presented to validate the suggested technique. By using this effective workflow combining shakedown theory and GANs to optimize lightweight frame structures, the structure was able to quickly achieve a lighter configuration design while avoiding failure forms like incremental collapse, ratcheting, and alternate plasticity. Furthermore, the method is used to determine an optimal lightweight design for a manned airtight module. The parametric study presented in the paper demonstrated the effectiveness of the proposed method in reducing the total mass of the structure. Moreover, the study highlights the improvement in design performance by allowing redundant shakedown load capacity and reducing material usage.
Investigation of Kinetics of Crystallization From a Concentration Controlled Supersaturated Solution in a Two-Phase Microfluidic System
Authors: Onel Selis , Ahmed Tijani Ahmed , Hatiboglu Anil 
Published: February 2024

Abstract
Solvothermal synthesis of advanced materials with complex metal-organic frameworks, superlattice crystalline structures, or intermediate phases requires precisely controlled thermodynamic and kinetic conditions. Droplet-based microfluidics provide a controlled platform for the investigation of chemical and physical kinetic processes confined in picoliter reactors for faster transfer of heat and mass and uniform distribution of temperature and concentration. We used a transparent thermo-fluidic device on an inverted microscope with a fast camera to observe the solidification kinetics in monodisperse microdroplets of a supersaturated solution in a continuous phase of silicon oil. The precursor solution involved a zirconium chloride salt, organic ligands, and a solvent. We optimized the two-phase flow conditions to control the reactor size. Nucleation experiments were conducted at 50, 80, and 100 °C. The residence time required to start nucleation was identified by a change in color and form in the droplets indicating a phase change. Higher temperatures resulted in shorter residence times due to faster nucleation kinetics. We used an Arrhenius relation to model droplet shrinkage due to dissolution of the solvent in oil and to calculate the concentration of the precursor solution at nucleation. Miniaturization of the reaction environment in microfluidic systems proved to be powerful for better control and observation of phase transitions in supersaturated solutions and in optimizing the precursor recipes towards desired products.
Effect of Miniaturization Using Droplet Based Micro Fluidic Systems on the Synthesis of UiO-66 MOF Nanoparticles
Authors: Onel Selis , Parlak Buse , Korkmaz Gaye , Dilci Elif Gokcen 
Published: February 2024

Abstract
Nanoparticles with metal-organic framework (MOF) structures are emerging advanced materials with exceptional internal structural and external surface properties. Particles with MOF crystalline structure can be synthesized with the conventional solvothermal methods using a metal salt and an organic ligand dissolved in a solvent in proper ratios to prepare a precursor solution. Precise control of the concentration of the precursor solution and the thermodynamic conditions, such as the temperature, pressure, and volume of the reactor, is critical in obtaining the desired MOF structure, but difficult for systems in bulk amounts. Droplet-generating microfluidic devices offer a controlled environment and inherent compartmentalization. Heat and mass transfer is dramatically enhanced at the micro-scale. In this study, we comparatively investigated the synthesis of zirconium(IV)-based UiO-66 nanoparticles using both a macro-scale batch reactor and a droplet-based microfluidic system. The resulting products from both the macro and micro scale systems were analyzed using XRD and SEM. We determined the recipes and the residence times that yielded UiO-66 nanoparticles with a size less than 100 nm at 100 °C. We found that more concentrated precursor solution recipes reduce the time required for nucleation and crystal growth, which is favorable in the microfluidic system.
Design and Development of an Adjustable Constant Force Mechanism
Authors: Johnson Shane , Rehman Tanzeel Ur 
Published: February 2024

Abstract
“Constant force mechanisms (CFMs) are widely used in various applications, including force regulators, surgical graspers, grippers, micro injectors, vibration isolation and constant force actuators. CFMs, designed for a single constant force, necessitate active mechanical control for force adjustability. The objective of this paper is to develop a semi-active, structural controlled adjustable CFM (ACFM) with a large change in constant force. A path based structural optimization is conducted using graphs. A 2 × 2 grid is selected, and nodal locations are perturbed to obtain a large stroke CFM with high quality constant force as defined by the energy similarity index, i.e., ratio of the energy capacity of the CFM to an ideal CFM. The slenderness of the CFM was tuned in order to change the constant force of the mechanism using a parametric study. The parametric study showed a change in constant force, maximum stroke and the quality of constant force as the slenderness was changed. In the experiment using a 2-layer unidirectional fiberglass specimen, slenderness was tuned by modifying a fixed boundary condition location in a fixed-fixed setup. The constant force (CF) was doubled from its value while maintaining a high energy similarity index of 0.9. Compared to mechanisms in the literature, the selected proposed mechanism was found to be 4.1% more compact. In conclusion, semi active control using boundary condition location adjustment in CFM was implemented for the first time to develop an ACFM. The ACFM is ideal for diverse applications including impact attenuation, vibration isolation, and surgical graspers.”
Atomistic Study on the Cooling Rate Induced Mechanical Properties Variations in Additively Manufactured Inconel-718
Authors: Islam Toushiqul , Aziz Md Samin Ashiq , Motalab Mohammad , Faiyad Abrar 
Published: February 2024

Abstract
Inconel 718 is a highly sought-after material for use in additive manufacturing, which is also known as 3D printing. Additive manufacturing is a process of building three-dimensional objects by layering materials on top of one another, based on a computer-generated design. Inconel 718 is very popular in additive manufacturing because of its unique properties, including high strength, high-temperature resistance, and excellent corrosion and oxidation resistance. This paper uses atomistic tensile test simulation to investigate the mechanical properties of additively manufactured Inconel 718. The study explores the effects of different cooling rates on Inconel-718 material using the large-scale atomic/molecular massively parallel simulator (LAMMPS). Throughout this study, the added layers on a pristine substrate have been equilibrated at 2000K and subsequently cooled to 300K using a cooling rate ranging from 5 K/ps to 100 K/ps including a variable cooling rate. Embedded atom method (EAM) potential has been used to investigate the dislocations, voids, defects, and atomic interactions, which control the material properties and failure mechanisms. A nano bar of (22nm × 5.5 nm × 5.5 nm) has been cut from the solidified layer for tensile test. The tensile force has been applied along [001] direction. The strain rate has been varied from 108 s−1 to 1010 s−1. Tensile test simulation shows that the additively manufactured Inconel 718 has a lower yield strength and elongation than the conventionally manufactured material but exhibits comparable ultimate tensile strength. The results show that the variation of cooling rate significantly impacts the mechanical properties, demonstrating that increasing the cooling rate causes the final material to have a lower density, higher number of defects, and lower Young’s modulus. In contrast, lower cooling rates cause the final structure to exhibit properties similar to its pristine counterpart.
These MD simulations provide insights into the deformation behavior, ultimate tensile strength, Young’s Modulus, fracture strain, and dislocation density at the atomic level and suggest that the observed differences in mechanical properties can be attributed to the presence of defects and microstructural features induced by different cooling rates of the additive manufacturing process. The fracture evolution at different cooling rates has also been analyzed. Results from this computational study also show that extensive sliding in {111} shear plane that again depends on the cooling rate causes necking before fracture. Overall, the findings highlight the importance of careful process control in additive manufacturing and demonstrate the potential of MD simulations for predicting the mechanical behavior of such materials.
Recovering and Regenerating of Spent Anode Material From the Scrap of Used Lithium Ion Batteries
Authors: Al-Shammari Hammad , Farhad Siamak 
Published: February 2024

Abstract
Lithium-ion batteries (LIBs) have been used intensively as energy storage for products powered by electricity, such as phones, laptops, electric vehicles, etc. Nevertheless, the increasing usage of LIBs is estimated to grow in the upcoming years due to the involvement of the technology of the electric vehicle. Therefore, the spent LIBs will increase in parallel to the demand growth which considers an obstacle to the environment as well as the economy in case left without recycling. So far, there are some recycling procedures that are focusing on the valuable metals existed in the spent cathode active materials, such as Ni, Co, and Li. However, the anode active material (graphite) in most cases consider as a waste. In this paper, the focus will be in an environmentally friendly and practical method for regenerating the spent anode active material. The aim of this study is to recover the anode material from the scrap of LIBs that contains the most popular types of LIBs such as LCO, LFP, LMO, NMC, and NCA. Then, the recovered anode active material will be heat treated to enhance its properties before being regenerated in a new LIB and farther evaluated.
For the validation, an experiment was designed where a scrap of used LIBs has physically separated and the recovered anode active material has directly regenerated in new cells and evaluated by galvanostatic cycling. Finally, the results of the SEM/EDX show that the anode and cathode mixture materials have separated from each other completely within a short time (typically 0.2 sec). Also, the performance of the regenerated anode is very close to the performance of the commercial anode material indicating the validity of the regeneration process.
The Effect of Char Particle Morphology on the Drag Coefficient Under Combustion Condition Using Micro-CT and Particle-Resolving Simulation
Authors: Liang Dongyu 
Published: February 2024

Abstract
The study of solid fuel combustion and gasification is crucial for the development of clean and efficient energy systems, especially in light of concerns about climate change and environmental contamination. With Computational fluid dynamics (CFD) that is widespread in combustion and gasification studies, an accurate description, in numerical simulations of multiphase flows, of the interphase momentum exchange is crucial. The drag force coefficient based on the individual char particles which affects the final outcomes of the reactor-scale simulation is used to describe this interaction. However, the existing drag correlation models that are typically introduced in the reactor scale CFD simulations are established based on non-realistic particle morphology. The effects of the roughness of particle surface, and the irregular particle shape are normally neglected. Therefore, this study focuses on the morphology effect of real solid fuel char particles on drag coefficient with conversion reactions at low Reynolds number (Re = 20), comparing it to regular shapes such as spheres and cylinders. Micro-CT imaging and particle-resolving simulations are used to investigate the drag coefficient of real solid fuel particles under combustion conditions. The findings of this study highlight that the traditional approach of treating real char particles as idealized shapes such as spheres or cylinders may not accurately represent their movement with chemical reactions. The empirical drag coefficients used in this study still have limitations and require further improvement to accurately capture the complex nature of real char particles.
Cyber-Protection of a Wheel Rotational Kinematics Sensor for Agile Mobility
Authors: Paldan Jesse , Vantsevich Vladimir , Gorsich David , Vitta Pradeep , Moradi Lee 
Published: February 2024

Abstract
The modern ground vehicle has become a set of systems containing interconnected sensors, actuators, and embedded control systems and therefore the security of vehicles with Intelligent Physical Systems (IPS) responsible for vehicle motion and performance has become vital. Electromagnetic radiation has the potential to physically interfere with the operation of IPS components including sensors while bypassing typical methods of intrusion detection and thus negatively impact vehicle performance. Traditionally, cyber-security issues are considered in relation with software design. However, it is important to estimate and provide cyber security of vehicle mechatronic intelligent and cyber-based systems. From this perspective, a model of a new wheel rotational kinematics sensor is examined in the presence of an electromagnetic cyber-threat to the sensor’s signal. In this paper, an analysis of the sensor’s susceptibility to and potential protection from intentional electromagnetic interference (IEMI) is performed in transient terrain conditions when the wheels move from one terrain to another. A simulated IEMI attack is applied to the sensor model to examine the impact of the degraded signal on the quality of tire slippage and rolling radius estimation. The shielding effectiveness needed to maintain reliability of the signal, allowing it to provide a signal of the wheel rotational velocity under threat very close to the signal during the normal operation, is estimated.
Wavelet Based Nonlinear Time-Frequency Control Theory With Local Adaptability
Authors: Kuo Chi-Wei , Suh C. Steve 
Published: February 2024

Abstract
A novel nonlinear control concept featuring simultaneous control of vibration amplitude in the time domain and spectral response in the frequency domain is developed and subsequently incorporated to maintain dynamic stability in these nonlinear dynamics by denying bifurcation and route-to-chaos from coming to pass. This study mathematically examines the time-frequency control theory and derives the ranges of the regression step sizes that ensure fast convergence and solution stability of the controller design. The local adaptability is novel, intelligent, self-adjusting, and universally applicable to addressing bifurcated and chaotic responses. Incorporating the control theory into designing dynamic systems governed by discontinuous, time-delayed nonlinear oscillators would realize unconditional stability, high-performance quality, and operation efficiency.
In the end, a novel time-delayed vibro-impact oscillator was investigated. Its instability in manufacturing would result in premature tool breakage, increased wear rate, and poor workpiece quality. Improvements in the discontinuous system in performance and stability were consequential in implementing the wavelet-based time-frequency controller with parallel online modeling. The controller demonstrated the most unique property in the frequency domain. As was evident in the instantaneous frequency domain, the controlled motions of the oscillator were unconditionally stable, stationary, and periodic.