Variation Of Parameters Research Articles

A Study on the Influence of FDM Parameters on the Compressive Behavior of PET-G Parts

This article presents the results of a study on the influence of Fused Deposition Modeling (FDM) 3D printing parameters on the compressive behavior of test specimens made of PET-G. In this context, 45 test specimens, made by FDM on the Anycubic 4 Max Pro 2.0 printer, were compressive tested on a universal testing machine Barrus White 20 kN, with the height of the layer applied in one pass being Lh = 0.10/0.15/0.20 mm and filling percentage Id = 50/75/100%. The two considered variable parameters, Lh and Id influence the compression resistance of the PET-G parts, with Id having a more significant influence. The scope and novelty of this work is to find the optimal parameters for maximum compressive strength (Cs) of PET-G samples made of FDM.

Investigation of coherent Fourier scatterometry as a calibration tool for determination of steep side wall angle and height of a nanostructure

Nanostructures with steep side wall angles (swa) play a pivotal role in various technological applications. Accurate characterization of these nanostructures is crucial for optimizing their performance. In this study, we propose a far-field detection method based on coherent Fourier scatterometry (CFS) for accurate quantification of steep swa and heights in cliff-like nanostructures. Our approach introduces a parameter termed ‘visibility’, derived from the unique far-field signatures of cliff-like nanostructures. This parameter serves as a quantitative metric for the calibration of swa and heights. The heightened sensitivity of our method is demonstrated, particularly when the incident polarization is perpendicular to the invariant direction of the nanostructure for swa calibration, while both polarization states exhibit sensitivity to height calibration. Furthermore, a comprehensive sensitivity analysis reveals the stable nature of our method, showcasing that even with fluctuations of ±10 nm in the position of the nanostructure, the resulting swa remains stable within a range of ±0.5∘ . The exponential variation of the visibility parameter with edge roundness is observed, with fluctuations in edge roundness within 10 nm resulting in swa variations within 1.7∘ for both polarization states. In experimental validations, our results demonstrate reasonable agreement between CFS-derived and AFM measurements. The AFM data for swa ( 77.99∘±1.37∘ ) and height (148.35 nm ±2.11 nm) are corroborated with CFS-derived value of swa ( 77.75∘±3.61∘ , 78.36∘±3.89∘ ) and height (149.42 nm ±1.66 nm, 150.05 nm ±1.04 nm) obtained from calibration curves for TM and TE incident beams, respectively. Overall, our findings underscore CFS as a potential and reliable tool for nanostructure characterization, offering precise measurements that are pivotal for advancing nanotechnology.

Multi-index control strategy from cement calcination denitration system: a model predictive control method for combined control of nitrogen oxide and ammonia escape.

The cement industry is one of the main sources of NOx emissions, and automated denitration systems enable precise control of NOx emission concentration. With non-linearity, time delay and strong coupling data in cement production process, making it difficult to maintain stable control of the denitration system. However, excessive pursuit of denitration efficiency is often prone to large ammonia escape, causing environmental pollution. A multi-objective prediction model combining time series and a bi-directional long short-term memory network (MT-BiLSTM) is proposed to solve the data problem of the denitration system and achieve simultaneous prediction of NOx emission concentration and ammonia escape value. Based on this model, a model predictive control framework is proposed and a control strategy of denitration system with multi-index model predictive control (MI-MPC) is built based on neural networks. In addition, the differential evolution (DE) algorithm is used for rolling optimization to find the optimal solution and to obtain the best control variable parameters. The control method proposed has significant advantages over the traditional PID (proportional integral derivative) controller, with a 3.84% reduction in overshoot and a 3.04% reduction in regulation time. Experiments prove that the predictive control framework proposed in this paper has better stability and higher accuracy, with practical research significance.

An ANN-Based Output-Error-Driven Incremental Model Predictive Control for Buck Converter Against Parameter Variations

DC/DC buck converters have been widely applied in the power processing of distributed energy resources (DERs) penetrated DC microgrid. Model predictive control (MPC) provides optimal control for the buck converter, but it often requires multiple sensors or complex observers to realize zero-steady-state-error control against parameter variations. An output-error-driven incremental MPC (OEDIMPC) for buck converter is proposed in this paper. Different from the traditional concept of calculating the optimal duty cycle, the proposed MPC calculates the optimal rate of change of the duty cycle by using a reduced-state output-error-driven prediction model. As a result, the OEDIMPC achieves zero-steady-state-error control against parameter variations, including load value, input voltage, output inductor, and output capacitor, with significantly improved dynamic performance and minimal sensor and observer requirements Besides, small- and large-signal analyzes are used to study the stability boundary of the OEDIMPC under parameter variations, and a shallow layer artificial neural network (ANN) is utilized to realize the OEDIMPC on the hardware. The proposed technique has been evaluated experimentally on a buck converter with resistor load and constant power load (CPL), confirming its effectiveness.

Effect of vault on predicting postoperative refractive error for posterior chamber phakic intraocular lens based on a machine learning model

Purpose: To investigate how vault and other biometric variations affect postoperative refractive error of implantable collamer lenses (ICLs) by integrating artificial intelligence and modified vergence formula. Setting: Eye and ENT Hospital of Fudan University, Shanghai, China. Design: Artificial intelligence and big data-based prediction model. Methods: 2845 eyes that underwent uneventful spherical ICL or toric ICL implantation and with manifest refraction results 1 month postoperatively were included. 1 eye of each patient was randomly included. Random forest was used to calculate the postoperative sphere, cylinder, and spherical equivalent by inputting variable ocular parameters. The influence of predicted vault and modified Holladay formula on predicting postoperative refractive error was analyzed. Subgroup analysis of ideal vault (0.25 to 0.75 mm) and extreme vault (<0.25 mm or >0.75 mm) was performed. Results: In the test set of both ICLs, all the random forest-based models significantly improved the accuracy of predicting postoperative sphere compared with the Online Calculation & Ordering System calculator (P < .001). For ideal vault, the combination of modified Holladay formula in spherical ICL exhibited highest accuracy (R = 0.606). For extreme vault, the combination of predicted vault in spherical ICL enhanced R values (R = 0.864). The combination of predicted vault and modified Holladay formula was most optimal for toric ICL in all ranges of vault (ideal vault: R = 0.516, extreme vault: R = 0.334). Conclusions: The random forest-based calculator, considering vault and variable ocular parameters, illustrated superiority over the existing calculator on the study datasets. Choosing an appropriate lens size to control the vault within the ideal range was helpful to avoid refractive surprises.

Improved Adaptive CCS-MPCC for Distorted Model Parameters Mitigation of IPMSM Drives

In this study, a robust adaptive model continuous-control-set model predictive current control (CCS-MPCC) method is proposed for interior permanent magnet synchronous motors (IPMSMs) considering the existing disturbances, unmodeled dynamics, and parameter variations. The novel aspect of this research is that it merges the improved dynamic, useful properties of model predictive control (MPC), which includes easy implementation and fast dynamic response, together with the most effective criterion of an adaptive controller (i.e., robustness to model uncertainties), that result in enhanced dynamic and steady-state control performance despite unknown and changing disturbances. Unlike the conventional CCS-MPCC approach that heavily depends on the accurate knowledge of the system model to attain improved control performance, the proposed method achieves satisfactory control performance (e.g., fast dynamic response, smaller steady-state error, and low stator current THD) by stabilizing the state errors to approach zero and compensating for variations in the model parameters using the designed feedback control terms and adaptive control terms, respectively. The stability of the proposed control method is ensured by the state errors asymptotically decaying to zero. The proposed approach was simulated and then implemented on a PSIM simulation tool and a prototype IPMSM test bench using TI TMS320F28335 DSP, respectively, to confirm its feasibility.

A versatile setup for nanosecond laser polishing processes with in situ analysis capabilities.

Laser polishing of material surfaces is a complex process depending on many variable parameters, such as, e.g., the properties of the used laser and optics (wavelength, pulse duration, fluence, and profile), as well as the processing (spot size, feed rate, and line or point overlap), and the thermodynamical properties of the material to be polished (heat capacity, heat conduction, etc.). For the successful laser polishing of any material, a systematic variation of all the process parameters is required to obtain satisfactorily polished surfaces with an appropriate set of parameters for the material of interest. In order to allow systematic studies of laser polishing processes, a new setup employing a highly stable nanosecond laser with an adjustable wavelength has been realized. The sample is located in a small high-vacuum chamber with the capability of introducing additional gases in a controlled manner, and the entire chamber is scanned in the beam to allow laser polishing of selected spots, lines, or larger areas. The setup is fully remote-controlled and allows in situ inspection of the initiated processes by means of a long-range microscope, electrical measurements, reflected laser light from the sample surface, and an analysis of the vacuum within the process chamber. The main properties of the setup will be presented, and some exemplary results on niobium and molybdenum metal samples will be discussed.

Adaptive Neural Network-Based Event-Triggered SOC Observer With Application to a Stochastic Battery Model.

Accurate state of charge (SOC) is crucial to achieving safe, reliable, and efficient use of batteries. This article proposes an adaptive neural network (NN)-based event-triggered observer to estimate SOC. First, a stochastic battery equivalent circuit model (ECM) is established, where an adaptive NN is employed to approximate the unknown nonlinear part. The learning process of network weight is conducted online to observe the variations of model parameters and avoid time-consuming processes for parameter extraction. Besides, for the purpose of saving computational cost, an event-triggered mechanism (ETM) is employed in the weight updating law, which means the weights only update when it is necessary. Then, an adaptive radial basis function (RBF) NN-based SOC observer is designed, and its stability is proven by the Lyapunov theory. Moreover, the strictly positive lower bound of interevent time is derived, and undesirable Zeno behavior can be excluded. Finally, the accuracy and robustness of the proposed observer are evaluated by experiments and simulations. Results show that the proposed method can estimate SOC accurately in the presence of initial deviation and sensor noises.

The impact of thixotropic behavior on microfluidic mixing in a staggered-herringbone mixer

This study numerically explores the influence of thixotropy on flow and mixing in a staggered-herringbone micromixer (SHM), employing a structure-kinetics model to simulate the microstructure formation and breakup in thixotropic fluids. This study represents the first of its kind to incorporate a thixotropic fluid model into microfluidic mixing in channels with patterned grooves. Specifically, we examine the effects of the destruction factor (kd) and the thixotropy number (Th) on microstructure, flow, and mixing in the SHM, covering extensive ranges (0.01≤kd≤10 and 0.01≤Th≤100). Higher values of kd and Th, particularly for kd>1 and Th>1, lead to larger spatial variations of the structure parameter (λ) and viscosity (η), with a notable decrease in λ near the groove tops, leading to the lowest viscosity in these areas. Conversely, at the groove bottoms, where shear deformation is minimal, there is less reduction in λ, leading to increased viscosity and higher flow resistance. The viscosity variations inside the grooves impede lateral flows, adversely affecting mixing in the higher kd and Th regimes. Therefore, an in-depth understanding of the complex thixotropic behaviors, as influenced by kd and Th near the grooves, is essential for achieving effective mixing in the SHM when using thixotropic fluids. Our results suggest that lowering the channel height, for a given groove depth, slightly improves the mixing of thixotropic fluids. Further enhanced mixing is achieved by combining a reduced channel height with double-sided groove patterns.

Social Parameter’s Role in BEM-PSO-Based Inverse Analysis for Locating Corrosion of Reinforced Concrete

The objective of this research is to investigate the effect of social parameter on the performance of boundary element inverse analysis (BEIA) to determine the location of reinforcing steel corrosion in concrete. BEIA was constructed by integrating the Boundary Element Method (BEM) and Particle Swarm Optimization (PSO) (PSO). BEM was used to calculate the corrosion potential on the whole surface of reinforced concrete. The cost function for detecting corrosion of reinforcing steel in concrete was evaluated using PSO. BEIA was conducted with 15 electrical potential data on the surface of reinforced concrete as a reference, such as from half-cell potential measurement. Numerical simulations of reinforced concrete with a single reinforcement reveal a difference in the speed of BEIA in locating corrosion sites. The higher the social parameter value, the faster the particles tend to migrate. However, if the parameter value is too high, the number of iterations required for the particles to converge increases, which has a negative effect on locating the corrosion point. Consequently, the variation of the social parameter affects the performance of BEIA in detecting the location of corrosion in reinforced concrete.

INTERSESSION REPEATABILITY OF READING PERFORMANCE MEASURES IN PATIENTS WITH NEOVASCULAR AGE-RELATED MACULAR DEGENERATION.

To assess the intersession repeatability of reading performance measures in patients with previously treated neovascular age-related macular degeneration and good best-corrected visual acuity (≥20/40 Snellen). Ninety-one patients (91 eyes) with a diagnosis of previously treated neovascular age-related macular degeneration and good best-corrected visual acuity (≥20/40 Snellen) were prospectively enrolled. Reading performance metrics were assessed using Radner charts, and these measurements were repeated after 7 days to obtain the intersession repeatability. To test repeatability, we calculated the intraclass correlation coefficient, the 95% coefficient of repeatability, and the coefficient of variation for each reading parameter: 1) reading acuity (RA-LogRAD); 2) maximal reading speed-words per minute; 3) RA score (RA score-LogRAD); and 4) critical print size-LogRAD. Mean ± standard deviation best-corrected visual acuity was 0.13 ± 0.01 logMAR [range: 0.00-0.30 logMAR]. The intraclass correlation coefficient values indicated a good reliability for all the analyzed metrics (0.901 for RA; 0.859 for max reading speed; 0.906 for RA score; and 0.868 for critical print size). The coefficient of repeatability was 0.2 LogRAD for RA, 63.2 words per minute for max reading speed, 0.2 LogRAD for RA score, and 0.2 LogRAD for critical print size. Coefficient of variation was 5.5% for RA, 8.9% for max reading speed, 5.8% for RA score, and 6.9% for critical print size. Reading performance metrics are characterized by good values of intersession repeatability in patients with neovascular age-related macular degeneration with good best-corrected visual acuity. Our findings may grant the employment of such measures in trials assessing the visual outcome in these patients.

Variation of Nonlinearity Parameter and Acoustic Attenuation with Temperature in Few Semiconductors

Variation of Nonlinearity Parameter and Acoustic Attenuation with Temperature in Few Semiconductors

The effect of the laser beam intensity profile in laser-based directed energy deposition: A high-fidelity thermal-fluid modeling approach

Modeling the thermal and fluid flow fields in laser-based directed energy deposition (DED-LB) is crucial for understanding process behavior and ensuring part quality. However, existing models often fail to accurately predict these fields due to simplifying assumptions, particularly regarding powder particle-induced attenuation in laser power and energy density distribution, and the variable material properties and process parameters. The present work introduces a high-fidelity multi-phase thermal-fluid model driven by a combination of the discrete element method (DEM) and the finite volume method (FVM). Incorporating an enhanced attenuation model for laser energy enables a more precise approximation of powder particle-induced attenuation effects in the laser power and energy density distribution. The study focuses on the influence of laser beam intensity profiles during DED-LB of austenitic stainless steel (AISI 316 L), with model validation conducted through experimental measurements of deposited track dimensions for different beam shapes. The results of numerical simulations demonstrate the critical impact of powder-induced attenuation on the laser power and intensity profiles. Neglecting laser energy attenuation, a common assumption in numerical simulations of DED-LB, leads to overestimations of the absorbed energy of the laser beam, affecting thermal and fluid flow fields, and melt pool dimensions. The present study unravels the complex relationship between the attenuation coefficient (due to the powder stream) and powder stream characteristics, describing the variations of the attenuation coefficient with changes in the powder mass flow rate and powder stream incidence angle. The findings show the critical effects of laser beam shaping on melt pool behavior in DED-LB, with square beams inducing larger melt pool volumes and circular beams creating smaller but deeper melt pools. The proposed enhanced thermal-fluid modeling framework offers a robust approach for optimizing laser-based additive manufacturing across diverse materials and laser systems.

Passive Model-Predictive Impedance Control for Safe Physical Human–Robot Interaction

Various cognitive systems have been designed to model the position and stiffness profiles of human behavior and then to drive robots by mimicking the human’s behavior to accomplish physical human-robot interaction tasks through a properly designed impedance controller. However, some studies have shown that variable stiffness parameters of the impedance controller can cause the violation of the passivity constraint of the robot states, and make the robot’s stored energy exceed the external energy injected from the human user, thus leading to the unsafe human-robot interaction. To solve this problem, this paper proposes a novel passive model predictive impedance control method including two control loops. In the bottom-loop of the proposed controller, the robot is driven by a variable impedance controller to achieve the desired compliant interaction behavior. In the top-loop of the proposed controller, the model predictive control (MPC) is used to ensure that the robot states satisfy the passivity constraint by calculating a complementary torque to limit the stored energy of the robot. The passivity of the closed-loop robot system and the feasibility of MPC are guaranteed by theoretical analysis, ensuring the safety of the robotic movement in the human-robot interaction. The effectiveness of the proposed method is demonstrated by the simulation and experiment on the Franka Emika Panda robot.

Study of hybrid nanofluid flow in a stationary cone-disk system with temperature-dependent fluid properties

Cone-disk systems find frequent use such as conical diffusers, medical devices, various rheometric, and viscosimetry applications. In this study, we investigate the three-dimensional flow of a water-based Ag-MgO hybrid nanofluid in a static cone-disk system while considering temperature-dependent fluid properties. How the variable fluid properties affect the dynamics and heat transfer features is studied by Reynolds’s linearized model for variable viscosity and Chiam’s model for variable thermal conductivity. The single-phase nanofluid model is utilized to describe convective heat transfer in hybrid nanofluids, incorporating the experimental data. This model is developed as a coupled system of convective-diffusion equations, encompassing the conservation of momentum and the conservation of thermal energy, in conjunction with an incompressibility condition. A self-similar model is developed by the Lie-group scaling transformations, and the subsequent self-similar equations are then solved numerically. The influence of variable fluid parameters on both swirling and non-swirling flow cases is analyzed. Additionally, the Nusselt number for the disk surface is calculated. It is found that an increase in the temperature-dependent viscosity parameter enhances heat transfer characteristics in the static cone-disk system, while the thermal conductivity parameter has the opposite effect.

Research on the factors affecting the product filtration efficiency in mineral processing plants

Filtration is a long-standing process but the technology has changed slowly, mainly based on the increase in filtration pressure and changes in the characteristics of filter media. The problem is high moisture remains product, requiring more expensive technologies (thermal drying) and long processing times. In the mineral processing industry, not only concentrates but also tailings need to be dewatered. The test samples were Cam Duong apatite concentrated and tailing ore, Ta Phoi copper concentrated and Sin Quyen tailing ore, Cua Ong fine coal, and Dak Nong red mud. All samples’ properties have been defined by sieving analyses, SEM method, and gravitational density measurement. Then, samples were experimented with using the Nutsche high-pressure filtration in the variable operational parameters. While the sludge concentration affected the specific resistance, the filter cake height and filtration pressure did not affect as much. At the suitable condition (homogeneous filter cake, 1 bar pressure difference, and 50 grams solid weight), the specific resistance and residual moisture content of the filter cake are around 2.5x10-12÷1.5x10-13 m-2, 15÷25%, respectively. Through the test results, some recommendations are mentioned in order to perfect the process and scale up for industrial production.

Railway wagon bearing fault diagnosis method based on improved sparrow search algorithm optimizing variational mode decomposition and multi-level convolutional neural network.

Ensuring the safe operation of trains hinges on precise bearing condition monitoring, given the pivotal role bearings play in railway wagons. The status and maintenance of wagon bearings are of paramount concern, necessitating a shift from traditional maintenance approaches reliant on schedules and experience, which often lack real-time precision and efficiency. To address this challenge, our research focuses on enhancing the sparrow search algorithm by incorporating logistic chaos mapping and the levy flight strategy. This enhanced algorithm optimizes variational mode decomposition parameters, utilizing intrinsic mode components' average dispersion entropy as the fitness function. This optimization is integrated with a multi-level convolutional neural network for bearing fault diagnosis. Our findings demonstrate the improved algorithm's enhanced spatial search capabilities and reduced modal aliasing in the frequency components. Experimental validation on public datasets and the group's experimental platform for railway wagons shows that multi-level convolutional neural networks have higher diagnostic accuracy and faster convergence speeds than traditional models such as LeNet-5, AlexNet, and convolutional neural network. Our research introduces a highly accurate and widely applicable methodology for mechanical equipment fault diagnosis, aligning with the requirements of the "smart" era.

Elucidating the components of genetic variation and genetic parameters for selected quantitative attributes in F1 rice genotypes

Elucidating the components of genetic variation and genetic parameters for selected quantitative attributes in F1 rice genotypes

Event-Triggered Federated Learning for Fault Diagnosis of Offshore Wind Turbines With Decentralized Data

Rapid developments of offshore wind industry offer a strong demand opportunity for offshore wind turbine remote diagnosis. As offshore wind turbines are often located in harsh and communication-constrained environments, the collection and transmission of data is severely restricted, which poses a serious challenge to the conventional centralized diagnostic paradigm that relies on data aggregation. To address this challenge, we propose a novel event-triggered federated learning framework for decentralized fault diagnosis of offshore wind turbines. Specifically, federated learning is first employed to learn decentralized local knowledge from geographically distributed offshore wind turbines, so that the communication objects are transformed from massive raw data into learned parameters, thereby relieving the communication burden. Then, we design an event-triggered communication mechanism and incorporate it into federated learning, the core of which is to modify the communication requirement from uploading all trained parameters periodically to communicating only when necessary. The proposed framework is verified by a real-world offshore wind turbine dataset from six large wind farms in China. An ablation study shows that the proposed framework can maintain high diagnostic performance while reducing communication costs. A comprehensive comparison based on three benchmark models demonstrates that the proposed framework can reduce the communication burden by up to 63% while obtaining better diagnostic performance. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This study was motivated by the problem of collaborative diagnosis of distributed offshore wind turbines under the constraints of data privacy and communication overhead. The method employs a federated learning-based fault diagnosis framework, which permits to obtain global fault diagnosis knowledge without aggregating raw data scattered in each end device, thus avoids the risk of data leakage. Moreover, a strategy integrating parameter variation and accuracy gain is designed to avoid communication redundancy for collaborative training. The practicability and superiority of our proposed framework is demonstrated using extensive experiments against actual industrial data collected from six offshore wind farms.

Dynamic-Linearization-Based Predictive Control of a Voltage-Source Inverter

In pursuit of accurate and fast trajectory tracking of power converters, an explicit model is commonly used in the finite control-set model predictive control (FCS-MPC) framework to predict precise behaviors of controlled variables. In reality, however, the model mismatch is inevitable, which causes the inherent challenges of parameter sensitivity and model uncertainties of the FCS-MPC method. This article proposes a dynamic-linearization-based predictive control architecture to circumvent such model dependence while keeping the attractive features of the conventional FCS-MPC method. By integrating the data-driven feature of the dynamic-linearization approach, the detailed model used in the FCS-MPC controller is replaced by a virtual equivalent data model, creating a data-driven predictive control architecture. The suggested method selects optimal control action solely based on the input-output data, exhibiting strong rejection against parameter variations whilst inheriting the distinctive property of the conventional FCS-MPC method. Finally, the proposed design is validated through comparative simulation and experimental results on a three-level neutral-point-clamped inverter.