In order to effectively predict the surface roughness Ra of Ti-6Al-4 V material after magnetic abrasive finishing (MAF) process, and optimize the process parameters to improve the surface quality of the material. Firs...
详细信息
In order to effectively predict the surface roughness Ra of Ti-6Al-4 V material after magnetic abrasive finishing (MAF) process, and optimize the process parameters to improve the surface quality of the material. Firstly, diamond/Fe-based magnetic abrasive powders (MAPs) are prepared for the MAF process of Ti-6Al-4 V by using the gas-solid two-phase double-stage atomization and rapid solidification method. The effects of rotational speed of the magnetic pole, working gap, feed velocity of workpiece, and filling quantity of MAPs on the surface roughness efficiency are discussed. Secondly, the orthogonal experiment is designed. The prediction model of surface roughness based on graywolfoptimization (GWO) algorithm and support vector regression (SVR), which is constructed according to the experimental results. The simulation shows that the R-2 of the optimized prediction model is 0.987456, and the MAPE is less than 1.99%. Finally, GWO algorithm is employed again to perform a global optimization search on the constructed prediction model. The optimal combination of process parameters is searched and verified, the surface roughness Ra is 0.098 mu m, and the relative error is less than 2.82% compared with the model prediction. The comparison of surface morphology before and after MAF of Ti-6Al-4 V shows that the MAF technology combined with the prediction model based on GWO-SVR can effectively improve the surface quality of Ti-6Al-4 V.
The color of an object appears different from its true color when illuminated with light sources of different hues. To solve this problem, this article proposes a combination algorithm (SCA-GWO-LSSVR) based on the sin...
详细信息
The color of an object appears different from its true color when illuminated with light sources of different hues. To solve this problem, this article proposes a combination algorithm (SCA-GWO-LSSVR) based on the sine-cosine algorithm (SCA) and the graywolfoptimization (GWO) algorithm to optimize the regression prediction model of the least-squares support vector regression (LSSVR) algorithm. The performance of the traditional LSSVR is significantly affected by the penalty parameter (gamma) and the sig2 kernel function parameter. The proposed method uses the improved GWO algorithm to search the population to find the best LSSVR parameter solution. The proposed algorithm uses the SCA to create multiple random candidate solutions in population initialization to avoid blind initialization of the GWO algorithm. In the process of iterative optimization, the SCA is infiltrated, and its sine-cosine wave mathematical model is used to quickly identify the best outward or inward position of the graywolf. Finally, the LSSVR combines the optimal sig2 kernel function parameters and penalty parameters (gamma) to obtain a highly versatile illumination correction model. The experimental results show that the fitting accuracy of the proposed method reaches 86.8%, which is 5% higher than that of the LSSVR algorithm alone.
Following the technological and digital developments introduced by Industry 4.0, the vast amount of information generated by an industrial plant increasingly requires more efficient and accurate management mechanisms ...
详细信息
Lithium-ion batteries are widely used vehicle energy storage batteries globally, and their reaction mechanism directly influences the safety and performance of energy storage systems. Simulating the actual internal st...
详细信息
Lithium-ion batteries are widely used vehicle energy storage batteries globally, and their reaction mechanism directly influences the safety and performance of energy storage systems. Simulating the actual internal state of the battery through a simulation model has become a crucial approach. Despite the high level of physical interpretation provided by the physicochemical models of the battery, the parameterization process poses significant challenges. Impedance spectroscopy at various temperatures is analyzed in this study to assess the sensitivity of battery model parameters. The goal is to investigate the changes in model parameter sensitivity at different temperatures and identify model parameters under various states of charge. Conducting a sensitivity analysis of model parameters allows for the selection of highly sensitive and appropriate parameters, which enhances the accuracy of the model output. In addition, the gray wolf optimization algorithm is utilized to identify the parameters of the battery model. The identified parameter set is then applied to the battery P2D model to simulate the impedance data. The root mean square error between the simulation and experimental data of the model is less than 0.241 mS2, and the average absolute error is less than 0.185 mS2. This method can accurately identify model parameters, which results in high model simulation accuracy. Findings from the parameter sensitivity analysis provide valuable references for parameter identification and state estimation considering temperature effects.
In view of the low precision of DV-Hop positioning algorithm in wireless sensor network, an improved DV-Hop localization algorithm that fuses chaotic sequences and Grey wolfoptimization (GWO) algorithms is proposed. ...
详细信息
ISBN:
(数字)9781665458641
ISBN:
(纸本)9781665458641
In view of the low precision of DV-Hop positioning algorithm in wireless sensor network, an improved DV-Hop localization algorithm that fuses chaotic sequences and Grey wolfoptimization (GWO) algorithms is proposed. Firstly, in order to reduce the average jump distance error, the algorithm introduces the correction factor, and secondly, for the cumulative calculation error brought about by the least squares method, the improved gray wolf optimization algorithm is used as an alternative, in order to further improve the positioning accuracy and convergence speed, the chaotic sequence is introduced to initialize the graywolf population, adaptive adjustment strategy is applied to control parameter a, and the algorithm global search ability is improved by using random walking strategy. The simulation results show that compared with the traditional DV-Hop algorithm, GA-DVhop algorithm and PSO-DVhop algorithm, the average positioning accuracy is improved by 90%, 86% and 78%.
In the past decade, open-source software (OSS) has become a very popular research topic in the field of software engineering. Because its code is open to the public, it has been pursued by programming teams all over t...
详细信息
ISBN:
(数字)9781665482233
ISBN:
(纸本)9781665482233
In the past decade, open-source software (OSS) has become a very popular research topic in the field of software engineering. Because its code is open to the public, it has been pursued by programming teams all over the world, including those in universities, government agencies and enterprises. In addition to analyzing some famous OSS projects, researchers also studied OSS projects and their functions in GitHub. However, the evolution process and the rules of OSS projects in GitHub have not been widely investigated so we conducted in-depth research on this issue. We use the cellular automata (CA) in the field of system dynamics to model the OSS project, construct the evolution rules of the graywolfoptimization (GWO) algorithm, define the objective vector, objective function and key optimization operators of GWO for OSS cellular evolution, and realize the intelligent acquisition of OSS project evolution rules in GitHub. In addition, we also selected the data of some OSS projects in GitHub from 2015 to 2020 for the simulation experiment. The experiment results show that the total accuracy of the simulation is 91.4%, which is consistent with real data.
Aiming at the problem of complex working mechanism of aeroengine gas path system and difficulty in effective fault diagnosis in actual work, a new fault diagnosis method of aeroengine gas path based on graywolf Optim...
详细信息
ISBN:
(纸本)9781665478960
Aiming at the problem of complex working mechanism of aeroengine gas path system and difficulty in effective fault diagnosis in actual work, a new fault diagnosis method of aeroengine gas path based on graywolfoptimization Deep Extreme Learning Machine (GWO-DELM) is proposed. Firstly, analyze a large amount of monitoring data of a certain type of aeroengine gas path components, and sort out the health data and fault sample data sets. Secondly, create the DELM fault diagnosis model by the health data and fault sample data set of the aeroengine gas circuit system. To reduce the influence of artificially setting network parameters on the diagnosis results, the gray wolf optimization algorithm (GWO) is used to optimize the DELM network parameters, and the optimal DELM fault diagnosis model GWO-DELM is created. Finally, the GWO-DELM fault diagnosis model is used to study the fault diagnosis verification technology of the aeroengine air circuit system, and the diagnosis results of the ELM, DELM and Multilayer Kernel Extreme Learning Machine (ML-KELM) fault diagnosis models are compared. The result shows that the fault diagnosis accuracy of the proposed GWO-DELM fault diagnosis model is 96.0%, which is significantly higher than that of the ELM model of 88.0%, the DELM model of 92.0% and the ML-KELM model of 94.0%, the effectiveness of the proposed method is verified, and it has a good application prospect.
Resource utilization of gangue solid waste has become an essential research direction for green development. This study prepared a novel gangue based geopolymer adsorbent (GPA) for the removal of Cd(II) from waste-wat...
详细信息
Resource utilization of gangue solid waste has become an essential research direction for green development. This study prepared a novel gangue based geopolymer adsorbent (GPA) for the removal of Cd(II) from waste-water using pretreatment gangue (PG) as the main raw material. The ANOVA indicated that the obtained quadratic model of fitness function (R2 > 0.99, P-value <0.0001) was significant and adequate, and the contribution of the three preparation conditions to the removal of Cd(II) was: calcination temperature > Na2CO3: PG ratio > water-glass solid content. The hybrid response surface method and graywolfoptimization (RSM-GWO) algorithm were adopted to acquire the optimum conditions: Na2CO3:PG ratio = 1.05, calcination tem-perature of 701 degrees C, solid content of water glass of 22.42%, and the removal efficiency of Cd(II) by GPA obtained under the optimized conditions (GPAC) was 97.84%. Adsorption kinetics, adsorption isotherms and character-ization by XRD, FTIR, Zeta potential, FSEM-EDS and BET were utilized to investigate the adsorption mechanism of GPAC on Cd(II). The results showed that the adsorption of Cd(II) from GPAC was consistent with the pseudo -second-order model (R2 = 0.9936) and the Langmuir model (R2 = 0.9988), the adsorption was a monolayer adsorption process and the computed maximum Cd(II) adsorption (50.76 mg g-1) was approximate to experi-mental results (51.47 mg g-1). Moreover, the surface morphology of GPAC was rough and porous with a specific surface area (SSA) of 18.54 m2 g-1, which provided abundant active sites, and the internal kaolinite was destroyed to produce a zeolite-like structure where surface complexation and ion exchange with Cd(II) through hydroxyl (-OH) and oxygen-containing groups (-SiOH and-AlOH) were the main adsorption mechanisms. Thus, GPAC is a lucrative adsorbent material for effective Cd(II) wastewater treatment, complying with the "high value-added" usage of solid wastes and "waste to cure poison" green sustaina
The wind tunnel balance signal detection system is widely employed in aerospace applica-tions for the accurate and automated measurement of aerodynamic forces and moments. However, measurement errors arise under diffe...
详细信息
The wind tunnel balance signal detection system is widely employed in aerospace applica-tions for the accurate and automated measurement of aerodynamic forces and moments. However, measurement errors arise under different environmental temperature. This paper addresses the issue of measurement accuracy under different temperature conditions by proposing a temperature com-pensation method based on an improved graywolfoptimization (IGWO) algorithm and optimized extreme learning machine (ELM). The IGWO algorithm is enhanced by improving the initial popu-lation position, convergence factor, and iteration weights of the gray wolf optimization algorithm. Subsequently, the IGWO algorithm is employed to determine the optimal network parameters for the ELM. The calibration decoupling experiment and high-low temperature experiment are designed and carried out. On this basis, ELM, GWO-ELM, PSO-ELM, GWO-RBFNN and IGWO-ELM are used for temperature compensation experiments. The experimental results show that IGWO-ELM has a good temperature compensation effect, reducing the measurement error from 20%FS to within 0.04%FS. Consequently, the accuracy and stability of the wind tunnel balance signal detection system under different temperature environments are enhanced.
The optimized surface fitting method base on improved graywolfalgorithm (IGWO) is proposed to overcome the nonlinearity and temperature drift of piezoresistive pressure sensor. To improve the convergence rate and se...
详细信息
The optimized surface fitting method base on improved graywolfalgorithm (IGWO) is proposed to overcome the nonlinearity and temperature drift of piezoresistive pressure sensor. To improve the convergence rate and search ability of gray wolf optimization algorithm (GWO), the adjustable nonlinearity convergence factor is established. The weight vector and weight adjustment coefficient are integrated into the residual function of the least square method. Employing the maximum full range error as the fitness function, the least square method is continuously optimized by IGWO to establish the high-performance comprehensive compensation mathematical formula. The simulation and practical test results indicate that the maximum full-scale error in the full range is better than 0.03 %, and the maximum full-scale error in the local range is less than 0.02 % especially. The proposed method proposed method is very suitable for the application and production of industrial pressure sensor with many advantages.
暂无评论