Process Parameters Optimization of Laser Cladding for HT200 with 316L Coating Based on Response Surface Method

In order to improve the sealing surface performance of gray cast iron gas gate valves and achieve precise molding control of the cladding layer,as well as to reveal the influence of laser cladding process parameters on the morphology and structure of the cladding layer,we prepared the 316L coating on HT 200 by using Design-Expert software central composite design(CCD)based on response surface analysis.We built a regression prediction model and analyzed the ANOVA with the inspection results.With a target cladding layer width of 3.5 mm and height of 1.3 mm,the process parameters were optimized to obtain the best combination of process parameters.The microstructure,phases,and hardness variations of the cladding layer from experiments with optimal parameters were analyzed by the metallographic microscope,confocal microscope,and microhardness instrument.The experimental results indicate that laser power has a significant impact on the cladding layer width,followed by powder feed rate;scan speed has a significant impact on the cladding layer height,followed by powder feed rate.The HT200 substrate and 316L can metallurgically bond well,and the cladding layer structure consists of dendritic crystals,columnar crystals,and equiaxed crystals in sequence.The optimal process parameter combination satisfying the morphology requirements is laser power(A)of 1993 W,scan speed(B)of 8.949 mm/s,powder feed rate(C)of 1.408 r/min,with a maximum hardness of 1564.3 HV0.5,significantly higher than the hardness of the HT200 substrate.

Optimization Study of Active-Passive Heating System Parameters in Village Houses in the Southern Xinjiang Province

Aiming at the problems of large energy consumption and serious pollution of winter heating existing in the rural buildings in Southern Xinjiang,a combined active-passive heating system was proposed,and the simulation software was used to optimize the parameters of the system,according to the parameters obtained from the optimization,a test platform was built and winter heating test was carried out.The simulation results showed that the thickness of the air layer of 75 mm,the total area of the vent holes of 0.24 m^(2),and the thickness of the insulation layer of 120 mm were the optimal construction for the passive part;solar collector area of 28 m^(2),hot water storage tank volume of 1.4 m^(3),mass flow rate of 800 kg/h on the collector side,mass flow rate of 400 kg/h on the heat exchanger side,and output power of auxiliary heat source of 5∼9 kWwere the optimal constructions for active heating system.Test results showed that during the heating period,the system could provide sufficient heat to the room under different heating modes,and the indoor temperature reached over 18°C,which met the heating demand.The economic and environmental benefits of the system were analyzed,and the economic benefits of the systemwere better than coal-fired heating,and the CO_(2) emissionswere reduced by 3,292.25 kg compared with coalfiredheating.The results of the study showed that the combinedactive-passiveheating systemcouldeffectively solve the heating problems existing in rural buildings in Southern Xinjiang,and it also laid the theoretical foundation for the popularization of the combined heating systems.

Optimization of chemistry and process parameters for control of intermetallic formation in Mg sludges

Intermetallic formation in sludge during magnesium(Mg)melting,holding and high pressure die casting practices is a very important issue.But,very often it is overlooked by academia,original equipment manufacturers(OEM),metal ingot producers and even die casters.The aim of this study was to minimize the intermetallic formation in Mg sludge via the optimization of the chemistry and process parameters.The Al8Mn5 intermetallic particles were identified by the microstructure analysis based on the Al and Mn ratio.The design of experiment(DOE)technique,Taguchi method,was employed to minimize the intermetallic formation in the sludge of Mg alloys with various chemical compositions of Al,Mn,Fe,and different process parameters,holding temperature and holding time.The sludge yield(SY)and intermetallic size(IS)was selected as two responses.The optimum combination of the levels in terms of minimizing the intermetallic formation were 9 wt.%Al,0.15 wt.%Mn,0.001 wt.%(10 ppm)Fe,690℃ for the holding temperature and holding at 30 mins for the holding time,respectively.The best combination for smallest intermetallic size were 9 wt.%Al,0.15 wt.%Mn,0.001 wt.%(10 ppm)Fe,630℃ for the holding temperature and holding at 60 mins for the holding time,respectively.Three groups of sludge factors,Chemical Sludge(CSF),Physical Sludge(PSF)and Comprehensive Sludge Factors(and CPSF)were established for prediction of sludge yields and intermetallic sizes in Al-containing Mg alloys.The CPSF with five independent variables including both chemical elements and process parameters gave high accuracy in prediction,as the prediction of the PSF with only the two processing parameters of the melt holding temperature and time showed a relatively large deviation from the experimental data.The Chemical Sludge Factor was primarily designed for small ingot producers and die casters with a limited melting and holding capacity,of which process parameters could be fixed easily.The Physical Sludge Factor could be used for mass production with a single type of Mg alloy,in which the chemistry fluctuation might be negligible.In large Mg casting suppliers with multiple melting and holding furnaces and a number of Mg alloys in production,the Comprehensive Sludge Factor should be implemented to diminish the sludge formation.

Particle Swarm Optimization-Based Hyperparameters Tuning of Machine Learning Models for Big COVID-19 Data Analysis

Analyzing big data, especially medical data, helps to provide good health care to patients and face the risks of death. The COVID-19 pandemic has had a significant impact on public health worldwide, emphasizing the need for effective risk prediction models. Machine learning (ML) techniques have shown promise in analyzing complex data patterns and predicting disease outcomes. The accuracy of these techniques is greatly affected by changing their parameters. Hyperparameter optimization plays a crucial role in improving model performance. In this work, the Particle Swarm Optimization (PSO) algorithm was used to effectively search the hyperparameter space and improve the predictive power of the machine learning models by identifying the optimal hyperparameters that can provide the highest accuracy. A dataset with a variety of clinical and epidemiological characteristics linked to COVID-19 cases was used in this study. Various machine learning models, including Random Forests, Decision Trees, Support Vector Machines, and Neural Networks, were utilized to capture the complex relationships present in the data. To evaluate the predictive performance of the models, the accuracy metric was employed. The experimental findings showed that the suggested method of estimating COVID-19 risk is effective. When compared to baseline models, the optimized machine learning models performed better and produced better results.

Parameters Optimization and Performance Evaluation of the Tuned Inerter Damper for the Seismic Protection of Adjacent Building Structures

In order to improve the seismic performance of adjacent buildings,two types of tuned inerter damper(TID)damping systems for adjacent buildings are proposed,which are composed of springs,inerter devices and dampers in serial or in parallel.The dynamic equations of TID adjacent building damping systems were derived,and the H2 norm criterion was used to optimize and adjust them,so that the system had the optimum damping performance under white noise random excitation.Taking TID frequency ratio and damping ratio as optimization parameters,the optimum analytical solutions of the displacement frequency response of the undamped structure under white noise excitation were obtained.The results showed that compared with the classic TMD,TID could obtain a better damping effect in the adjacent buildings.Comparing the TIDs composed of serial or parallel,it was found that the parallel TIDs had more significant advantages in controlling the peak displacement frequency response,while the H2 norm of the displacement frequency response of the damping system under the coupling of serial TID was smaller.Taking the adjacent building composed of two ten-story frame structures as an example,the displacement and energy collection time history analysis of the adjacent building coupled with the optimum design parameter TIDs were carried out.It was found that TID had a better damping effect in the full-time range compared with the classic TMD.This paper also studied the potential power of TID in adjacent buildings,which can be converted into available power resources during earthquakes.

Prediction of Shear Bond Strength of Asphalt Concrete Pavement Using Machine Learning Models and Grid Search Optimization Technique

Determination of Shear Bond strength(SBS)at interlayer of double-layer asphalt concrete is crucial in flexible pavement structures.The study used three Machine Learning(ML)models,including K-Nearest Neighbors(KNN),Extra Trees(ET),and Light Gradient Boosting Machine(LGBM),to predict SBS based on easily determinable input parameters.Also,the Grid Search technique was employed for hyper-parameter tuning of the ML models,and cross-validation and learning curve analysis were used for training the models.The models were built on a database of 240 experimental results and three input variables:temperature,normal pressure,and tack coat rate.Model validation was performed using three statistical criteria:the coefficient of determination(R2),the Root Mean Square Error(RMSE),and the mean absolute error(MAE).Additionally,SHAP analysis was also used to validate the importance of the input variables in the prediction of the SBS.Results show that these models accurately predict SBS,with LGBM providing outstanding performance.SHAP(Shapley Additive explanation)analysis for LGBM indicates that temperature is the most influential factor on SBS.Consequently,the proposed ML models can quickly and accurately predict SBS between two layers of asphalt concrete,serving practical applications in flexible pavement structure design.

Optimization Strategies of Na_(3)V_(2)(PO_(4))_(3) Cathode Materials for Sodium‑Ion Batteries

Na_(3)V_(2)(PO_(4))_(3)(NVP)has garnered great attentions as a prospective cathode material for sodium-ion batteries(SIBs)by virtue of its decent theoretical capacity,superior ion conductivity and high structural stability.However,the inherently poor electronic conductivity and sluggish sodium-ion diffusion kinetics of NVP material give rise to inferior rate performance and unsatisfactory energy density,which strictly confine its further application in SIBs.Thus,it is of significance to boost the sodium storage performance of NVP cathode material.Up to now,many methods have been developed to optimize the electrochemical performance of NVP cathode material.In this review,the latest advances in optimization strategies for improving the electrochemical performance of NVP cathode material are well summarized and discussed,including carbon coating or modification,foreign-ion doping or substitution and nanostructure and morphology design.The foreign-ion doping or substitution is highlighted,involving Na,V,and PO_(4)^(3−)sites,which include single-site doping,multiple-site doping,single-ion doping,multiple-ion doping and so on.Furthermore,the challenges and prospects of high-performance NVP cathode material are also put forward.It is believed that this review can provide a useful reference for designing and developing high-performance NVP cathode material toward the large-scale application in SIBs.

High temperature deformation behavior and optimization of hot compression process parameters in TC11 titanium alloy with coarse lamellar original microstructure

The high temperature deformation behaviors of α＋β type titanium alloy TC11 （Ti-6.5Al-3.5Mo-1.5Zr-0.3Si） with coarse lamellar starting microstructure were investigated based on the hot compression tests in the temperature range of 950-1100 ℃ and the strain rate range of 0.001-10 s-1. The processing maps at different strains were then constructed based on the dynamic materials model, and the hot compression process parameters and deformation mechanism were optimized and analyzed, respectively. The results show that the processing maps exhibit two domains with a high efficiency of power dissipation and a flow instability domain with a less efficiency of power dissipation. The types of domains were characterized by convergence and divergence of the efficiency of power dissipation, respectively. The convergent domain in a＋fl phase field is at the temperature of 950-990 ℃ and the strain rate of 0.001-0.01 s^-1, which correspond to a better hot compression process window of α＋β phase field. The peak of efficiency of power dissipation in α＋β phase field is at 950 ℃ and 0.001 s 1, which correspond to the best hot compression process parameters of α＋β phase field. The convergent domain in β phase field is at the temperature of 1020-1080 ℃ and the strain rate of 0.001-0.1 s^-l, which correspond to a better hot compression process window of β phase field. The peak of efficiency of power dissipation in ℃ phase field occurs at 1050 ℃ over the strain rates from 0.001 s^-1 to 0.01 s^-1, which correspond to the best hot compression process parameters of ,8 phase field. The divergence domain occurs at the strain rates above 0.5 s^-1 and in all the tested temperature range, which correspond to flow instability that is manifested as flow localization and indicated by the flow softening phenomenon in stress-- strain curves. The deformation mechanisms of the optimized hot compression process windows in a＋β and β phase fields are identified to be spheroidizing and dynamic recrystallizing controlled by self-diffusion mechanism, respectively. The microstructure observation of the deformed specimens in different domains matches very well with the optimized results.

Optimization of AZ80 magnesium alloy squeeze cast process parameters using morphological matrix

The squeeze cast process parameters of AZ80 magnesium alloy were optimized by morphological matrix. Experiments were conducted by varying squeeze pressure, die pre-heat temperature and pressure duration using L9（33） orthogonal array of Taguchi method. In Taguchi method, a 3-level orthogonal array was used to determine the signal/noise ratio. Analysis of variance was used to determine the most significant process parameters affecting the mechanical properties. Mechanical properties such as ultimate tensile strength, elongation and hardness of the components were ascertained using multi variable linear regression analysis. Optimal squeeze cast process parameters were obtained.

Application of EBSD technique to ultrafine grained and nanostructured materials processed by severe plastic deformation:Sample preparation, parameters optimization and analysis

With the help of FESEM, high resolution electron backscatter diffraction can investigate the grains/subgrains as small as a few tens of nanometers with a good angular resolution （~0.5°）. Fast development of EBSD speed （up to 1100 patterns per second） contributes that the number of published articles related to EBSD has been increasing sharply year by year. This paper reviews the sample preparation, parameters optimization and analysis of EBSD technique, emphasizing on the investigation of ultrafine grained and nanostructured materials processed by severe plastic deformation （SPD）. Detailed and practical parameters of the electropolishing, silica polishing and ion milling have been summarized. It is shown that ion milling is a real universal and promising polishing method for EBSD preparation of almost all materials. There exists a maximum value of indexed points as a function of step size. The optimum step size depends on the magnification and the board resolution/electronic step size. Grains/subgrains and texture, and grain boundary structure are readily obtained by EBSD. Strain and stored energy may be analyzed by EBSD.

Process parameters optimization of Ti-6Al-4V alloy sheet during hot stretch-creep forming

Hot stretch-creep forming （SCF） is a novel technique to produce hard-to-form thin-walled metal components. Comprehensively considering the analysis results of the springback angle, yield strength and microstructure, four hot SCF process parameters including temperature, stretch velocity, post stretch percentage and dwelling time of a Ti-6Al-4V alloy sheet were optimized using an orthogonal experiment. The results reveal that temperature is the most important factor on springback angle. The yield strength of the deformed material in 0° direction increases, while those in directions of 45° and 90° fluctuate around the original value. After hot SCF, the shape of some a phases changes from short thin grains to long slender ones, and the microhardness changes very little. The optimized parameters with temperature of 700 ℃, stretch velocity of 5 mm/min, post stretch percentage of 2% and dwelling time of 8 min are achieved finally.

Machine tool selection based on fuzzy evaluation and optimization of cutting parameters

The paper analyzes the factors influencing machine tool selection. By using fuzzy mathematics theory, we establish a theorietical model for optimal machine tool selection considering geometric features, clamping size, machining range, machining precision and surface roughness. By means of fuzzy comprehensive evaluation method, the membership degree of machine tool selection and the largest comprehensive evaluation index are determined. Then the reasonably automatic selection of machine tool is realized in the generative computer aided process planning （CAPP） system. Finally, the finite element model based on ABAQUS is established and the cutting process of machine tool is simulated. According to the theoretical and empirical cutting parameters and the curve of surface residual stress, the optimal cutting parameters can be determined.

Application of BP neural network model with fuzzy optimization in retrieval of biomass parameters

The retrieval of the biomass parameters from active/passive microwave remote sensing data （10.2 GHz） is performed based on an iterative inversion of BP neural network model with fuzzy optimization. The BP neural network is trained by a set of the measurements of active and passive remote sensing and the ground truth data versus Day of Year during growth. Once the network training is complete, the model can be used to retrieve the temporal variations of the biomass parameters from another set of observation data. The model was used in weights and microware observation data of wheat growth in 1989 to retrieve biomass parameters change of wheat growth this year. The retrieved biomass parameters correspond well with the real data of the growth, which shows that the BP model is scientific and sound.

Grain refinement of Mg-Al alloys by optimization of process parameters based on three-dimensional finite element modeling of roll casting

To study the influence of roll casting process parameters on temperature and thermal-stress fields for the AZ31 magnesium alloy sheets,three-dimensional geometric and 3D finite element models for roll casting were established based on the symmetry of roll casting by ANSYS software.Meshing method and smart-sizing algorithm were used to divide finite element mesh in ANSYS software.A series of researches on the temperature and stress distributions during solidification process with different process parameters were done by 3D finite element method.The temperatures of both the liquid-solid two-phase zone and liquid phase zone were elevated with increasing pouring temperature.With the heat transfer coefficient increasing,the two-phase region for liquid-solid becomes smaller.With the pouring temperature increasing and the increase of casting speed,the length of two-phase zone rises.The optimized of process parameters（casting speed 2 m/min,pouring temperature 640 ℃ and heat transfer coefficient 15 kW/（m2·℃） with the water pouring at roller exit was used to produce magnesium alloy AZ31 sheet,and equiaxed grains with the average grain size of 50 μm were achieved after roll casting.The simulation results give better understanding of the temperature variation in phase transformation zone and the formation mechanism of hot cracks in plates during roll casting and help to design the optimized process parameters of roll casting for Mg alloy.

Parameters optimization of electroless deposition of Cu on Cr-coated diamond

Electroless copper plating on diamond particles precoated with 1%Cr was carried out to evaluate the effects of various experimental parameters on coating quality and deposition rate to obtain the optimized reaction parameters. The formulated samples under optimized parameters were characterized by X-ray diffraction, scanning electron microscopy, energy dispersive spectroscopy, X-ray photoelectron spectra and optical microscopy. The best parameters, where uniform and maximum coating thickness was achieved, are etching with 20%NaOH for 30 min, sensitization and activation with SnCl2 and PdCl2 for 5 and 20 min, respectively. The composition of the copper solution bath was 16 g/L CuSO4＆#183;5H2O, 35 mL/L formaldehyde （HCHO）, 23 g/L KNaC4H4O6 at 60 ℃, pH=13 and stirring at （350±15） r/min under ultrasonication.

IMPROVED PARTICLE SWARM OPTIMIZATION ALGORITHM FOR INTELLIGENTLY SETTING UAV ATTITUDE CONTROLLER PARAMETERS

An improved particle swarm optimization （PSO） algorithm is investigated in the optimization of the attitude controller parameters of unmanned aerial vehicle （UAV）. Considering the stagnation phenomenon in the later phase of the basic PSO algorithm caused by the diversity scarcity of particles, a modified PSO algorithm is presented. For the basic PSO algorithm, the velocity of each particle is adjusted according to the inertia motion, the swarm previous best position and its own previous best position. However, in the improved PSO algorithm, each particle only learns from another randomly selected particle with higher performance, besides keeping the inertia motion. The inertia weight of the improved PSO algorithm is a random number. The modification decreases the uncertain parameters of the algorithm, simplifies the learning mechanism of the particle, and enhances the diversity of the swarm. Furthermore, a UAV attitude control system is built, and the improved PSO algorithm is applied in the optimized tuning of four controller parameters. Simulation results show that the improved PSO algorithm has stronger global searching ability than the common PSO algorithms, and obtains better UAV attitude control parameters.

GENETIC OPTIMIZATION OF HOT FILAMENT PARAMETERS IN HFCVD SYSTEM

In HFCVD system the substrate temperature is a key factor which deeply affects the quality of diamond films. Th e magnitude and the variation of the substrate temperature must be limited in a suitable range to deposit diamond films of uniform thickness over large areas. In this paper, the hot filament parameters are investigated on the basi s of GAs to realize a good substrate temperature profile. Computer simulations d emonstrate that on parameters optimized by GAs a uniform substrate temperatur e field can be formed over a relatively large circle area with R s=10 cm.

Parameters Optimization of a Novel 5-DOF Gasbag Polishing Machine Tool

The research on the parameters optimization for gasbag polishing machine tools, mainly aims at a better kinematics performance and a design scheme. Serial structural arm is mostly employed in gasbag polishing machine tools at present, but it is disadvantaged by its complexity, big inertia, and so on. In the multi-objective parameters optimization, it is very difficult to select good parameters to achieve excellent performance of the mechanism. In this paper, a statistics parameters optimization method based on index atlases is presented for a novel 5-DOF gasbag polishing machine tool. In the position analyses, the structure and workspace for a novel 5-DOF gasbag polishing machine tool is developed, where the gasbag polishing machine tool is advantaged by its simple structure, lower inertia and bigger workspace. In the kinematics analyses, several kinematics performance evaluation indices of the machine tool are proposed and discussed, and the global kinematics performance evaluation atlases are given. In the parameters optimization process, considering the assembly technique, a design scheme of the 5-DOF gasbag polishing machine tool is given to own better kinematics performance based on the proposed statistics parameters optimization method, and the global linear isotropic performance index is 0.5, the global rotational isotropic performance index is 0.5, the global linear velocity transmission performance index is 1.012 3 m/s in the case of unit input matrix, the global rotational velocity transmission performance index is 0.102 7 rad/s in the case of unit input matrix, and the workspace volume is 1. The proposed research provides the basis for applications of the novel 5-DOF gasbag polishing machine tool, which can be applied to the modern industrial fields requiring machines with lower inertia, better kinematics transmission performance and better technological efficiency.

Optimization of operating conditions and structure parameters of zinc electrolytic cell based on numerical simulation for electrolyte flow

The physical and mathematical model of an operating electrowinning cell was established, and the flow of electrolyte was numerically simulated by the commercial software Fluent. The results indicate that there are two circulations at the surface flow where part of electrolyte backflows to the inlet from the side of cell, and the rest flows directly to the outlet, and the separation of two circulations with opposite direction occurs at the 20th pair of anode-cathode. This phenomenon was observed in the real operation. The electrolyte flows into the space between anode and cathode from the side portion of the cell. Meanwhile, the interelectrode effective flow rate （IEFR） is put forward to describe quantitively the flow field characteristics and is defined as the ratio of electrolyte flow between the anode and cathode to the total flow area. The influences of structure parameters and operating conditions on IEFR, such as the inlet angle, the volumetric flow rate, the inlet position and the height of steel baffles were simulated. The inlet position has a significant influence on the IEFR and its optimal value is 0.9 m below free surface. The inlet angle should be in the range from -10° to 10°. IEFR is in linear proportion with the volumetric flow rate, and the height of the steel baffle has little influence on the flow field.

Optimization of Cutting Parameters for Trade-off Among Carbon Emissions, Surface Roughness, and Processing Time

As the manufacturing industry is facing increasingly serious environmental problems, because of which carbon tax policies are being implemented, choosing the optimum cutting parameters during the machining process is crucial for automobile panel dies in order to achieve synergistic minimization of the environment impact, product quality, and processing efficiency. This paper presents a processing task-based evaluation method to optimize the cutting parameters, considering the trade-off among carbon emissions, surface roughness, and processing time. Three objective models and their relationships with the cutting parameters were obtained through input–output, response surface, and theoretical analyses, respectively. Examples of cylindrical turning were applied to achieve a central composite design(CCD), and relative validation experiments were applied to evaluate the proposed method. The experiments were conducted on the CAK50135 di lathe cutting of AISI 1045 steel, and NSGA-Ⅱ was used to obtain the Pareto fronts of the three objectives. Based on the TOPSIS method, the Pareto solution set was ranked to find the optimal solution to evaluate and select the optimal cutting parameters. An S/N ratio analysis and contour plots were applied to analyze the influence of each decision variable on the optimization objective. Finally, the changing rules of a single factor for each objective were analyzed. The results demonstrate that the proposed method is effective in finding the trade-off among the three objectives and obtaining reasonable application ranges of the cutting parameters from Pareto fronts.