Specialized vanadium(V)-iron(Fe)-based alloy additives utilized in the production of V-containing steels were investigated.Vanadium slag from the Panzhihua region of China was utilized as a raw material to optimize pr...Specialized vanadium(V)-iron(Fe)-based alloy additives utilized in the production of V-containing steels were investigated.Vanadium slag from the Panzhihua region of China was utilized as a raw material to optimize process parameters for the preparation of V-Fe-based alloy via silicon thermal reduction.Experiments were conducted to investigate the effects of reduction temperature,holding time,and slag composition on alloy-slag separation,alloy microstructure,and the oxide content of residual slag,with an emphasis on the recovery of valuable metal elements.The results indicated that the optimal process conditions for silicon thermal reduction were achieved at reduction temperature of 1823 K,holding time of 240 min,and slag composition of 45 wt.%SiO_(2),40 wt.%CaO,and 15 wt.%Al_(2)O_(3).The resulting V-Fe-based alloy predominantly consisted of Fe-based phases such as Fe,titanium(Ti),silicon(Si)and manganese(Mn),with Si,V,as well as chromium(Cr)concentrated in the intercrystalline phase of the Fe-based alloy.The recoveries of Fe,Mn,Cr,V,and Ti under the optimal conditions were 96.30%,91.96%,86.53%,80.29%,and 74.82%,respectively.The key components of the V-Fe-based alloy obtained were 41.96 wt.%Si,27.55 wt.%Fe,12.13 wt.%Mn,5.53 wt.%V,4.86 wt.%Cr,and 3.74 wt.%Ti,thereby enabling the comprehensive recovery of the valuable metal from vanadium slag.展开更多
The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is m...The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is mainly used for finite element analysis at present,and the effectiveness of the surrogate material model has been fully confirmed.However,there are some accuracy problems when dealing with boundary elements using the surrogate material model,which will affect the topology optimization results.In this study,a boundary element reconstruction(BER)model is proposed based on the surrogate material model under the MMC topology optimization framework to improve the accuracy of topology optimization.The proposed BER model can reconstruct the boundary elements by refining the local meshes and obtaining new nodes in boundary elements.Then the density of boundary elements is recalculated using the new node information,which is more accurate than the original model.Based on the new density of boundary elements,the material properties and volume information of the boundary elements are updated.Compared with other finite element analysis methods,the BER model is simple and feasible and can improve computational accuracy.Finally,the effectiveness and superiority of the proposed method are verified by comparing it with the optimization results of the original surrogate material model through several numerical examples.展开更多
Steel catenary riser represents the pioneering riser technology implemented in China’s deep-sea oil and gas opera-tions.Given the complex mechanical conditions of the riser,extensive research has been conducted on it...Steel catenary riser represents the pioneering riser technology implemented in China’s deep-sea oil and gas opera-tions.Given the complex mechanical conditions of the riser,extensive research has been conducted on its dynamic analysis and structural design.This study investigates a deep-sea oil and gas field by developing a coupled model of a semi-submersible platform and steel catenary riser to analyze it mechanical behavior under extreme marine condi-tions.Through multi-objective optimization methodology,the study compares and analyzes suspension point tension and touchdown point stress under various conditions by modifying the suspension position,suspension angle,and catenary length.The optimal configuration parameters were determined:a suspension angle of 12°,suspension position in the southwest direction of the column,and a catenary length of approximately 2000 m.These findings elucidate the impact of configuration parameters on riser dynamic response and establish reasonable parameter layout ranges for adverse sea conditions,offering valuable optimization strategies for steel catenary riser deployment in domestic deep-sea oil and gas fields.展开更多
The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can caus...The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods.展开更多
In this study,a novel synergistic swing energy-regenerative hybrid system(SSEHS)for excavators with a large inertia slewing platform is constructed.With the SSEHS,the pressure boosting and output energy synergy of mul...In this study,a novel synergistic swing energy-regenerative hybrid system(SSEHS)for excavators with a large inertia slewing platform is constructed.With the SSEHS,the pressure boosting and output energy synergy of multiple energy sources can be realized,while the swing braking energy can be recovered and used by means of hydraulic energy.Additionally,considering the system constraints and comprehensive optimization conditions of energy efficiency and dynamic characteristics,an improved multi-objective particle swarm optimization(IMOPSO)combined with an adaptive grid is proposed for parameter optimization of the SSEHS.Meanwhile,a parameter rule-based control strategy is designed,which can switch to a reasonable working mode according to the real-time state.Finally,a physical prototype of a 50-t excavator and its AMESim model is established.The semi-simulation and semi-experiment results demonstrate that compared with a conventional swing system,energy consumption under the 90°rotation condition could be reduced by about 51.4%in the SSEHS before parameter optimization,while the energy-saving efficiency is improved by another 13.2%after parameter optimization.This confirms the effectiveness of the SSEHS and the IMOPSO parameter optimization method proposed in this paper.The IMOPSO algorithm is universal and can be used for parameter matching and optimization of hybrid power systems.展开更多
Rate of penetration(ROP)is the key factor affecting the drilling cycle and cost,and it directly reflects the drilling efficiency.With the increasingly complex field data,the original drilling parameter optimization me...Rate of penetration(ROP)is the key factor affecting the drilling cycle and cost,and it directly reflects the drilling efficiency.With the increasingly complex field data,the original drilling parameter optimization method can't meet the needs of drilling parameter optimization in the era of big data and artificial intelligence.This paper presents a drilling parameter optimization method based on big data of drilling,which takes machine learning algorithms as a tool.First,field data is pre-processed according to the characteristics of big data of drilling.Then a formation clustering model based on unsupervised learning is established,which takes sonic logging,gamma logging,and density logging data as input.Formation clusters with similar stratum characteristics are decided.Aiming at improving ROP,the formation clusters are input into the ROP model,and the mechanical parameters(weight on bit,revolution per minute)and hydraulic parameters(standpipe pressure,flow rate)are optimized.Taking the Southern Margin block of Xinjiang as an example,the MAPE of prediction of ROP after clustering is decreased from 18.72%to 10.56%.The results of this paper provide a new method to improve drilling efficiency based on big data of drilling.展开更多
The Rydberg atom-based receiver, as a novel type of antenna, demonstrates broad application prospects in the field of microwave communications. However, since Rydberg atomic receivers are nonlinear systems, mismatches...The Rydberg atom-based receiver, as a novel type of antenna, demonstrates broad application prospects in the field of microwave communications. However, since Rydberg atomic receivers are nonlinear systems, mismatches between the parameters of the received amplitude modulation(AM) signals and the system's linear workspace and demodulation operating points can cause severe distortion in the demodulated signals. To address this, the article proposes a method for determining the operational parameters based on the mean square error(MSE) and total harmonic distortion(THD) assessments and presents strategies for optimizing the system's operational parameters focusing on linear response characteristics(LRC) and linear dynamic range(LDR). Specifically, we employ a method that minimizes the MSE to define the system's linear workspace, thereby ensuring the system has a good LRC while maximizing the LDR. To ensure that the signal always operates within the linear workspace, an appropriate carrier amplitude is set as the demodulation operating point. By calculating the THD at different operating points, the LRC performance within different regions of the linear workspace is evaluated, and corresponding optimization strategies based on the range of signal strengths are proposed. Moreover, to more accurately restore the baseband signal, we establish a mapping relationship between the carrier Rabi frequency and the transmitted power of the probe light, and optimize the slope of the linear demodulation function to reduce the MSE to less than 0.8×10^(-4). Finally, based on these methods for determining the operational parameters, we explore the effects of different laser Rabi frequencies on the system performance, and provide optimization recommendations. This research provides robust support for the design of high-performance Rydberg atom-based AM receivers.展开更多
Purpose–To investigate the influence of vehicle operation speed,curve geometry parameters and rail profile parameters on wheel–rail creepage in high-speed railway curves and propose a multi-parameter coordinated opt...Purpose–To investigate the influence of vehicle operation speed,curve geometry parameters and rail profile parameters on wheel–rail creepage in high-speed railway curves and propose a multi-parameter coordinated optimization strategy to reduce wheel–rail contact fatigue damage.Design/methodology/approach–Taking a small-radius curve of a high-speed railway as the research object,field measurements were conducted to obtain track parameters and wheel–rail profiles.A coupled vehicle-track dynamics model was established.Multiple numerical experiments were designed using the Latin Hypercube Sampling method to extract wheel-rail creepage indicators and construct a parameter-creepage response surface model.Findings–Key service parameters affecting wheel–rail creepage were identified,including the matching relationship between curve geometry and vehicle speed and rail profile parameters.The influence patterns of various parameters on wheel–rail creepage were revealed through response surface analysis,leading to the establishment of parameter optimization criteria.Originality/value–This study presents the systematic investigation of wheel–rail creepage characteristics under multi-parameter coupling in high-speed railway curves.A response surface-based parameter-creepage relationship model was established,and a multi-parameter coordinated optimization strategy was proposed.The research findings provide theoretical guidance for controlling wheel–rail contact fatigue damage and optimizing wheel–rail profiles in high-speed railway curves.展开更多
Accurate identification of unknown internal parameters in photovoltaic(PV)cells is crucial and significantly affects the subsequent system-performance analysis and control.However,noise,insufficient data acquisition,a...Accurate identification of unknown internal parameters in photovoltaic(PV)cells is crucial and significantly affects the subsequent system-performance analysis and control.However,noise,insufficient data acquisition,and loss of recorded data can deteriorate the extraction accuracy of unknown parameters.Hence,this study proposes an intelligent parameter-identification strategy that integrates artificial ecosystem optimization(AEO)and a Bayesian neural network(BNN)for PV cell parameter extraction.A BNN is used for data preprocessing,including data denoising and prediction.Furthermore,the AEO algorithm is utilized to identify unknown parameters in the single-diode model(SDM),double-diode model(DDM),and three-diode model(TDM).Nine other metaheuristic algorithms(MhAs)are adopted for an unbiased and comprehensive validation.Simulation results show that BNN-based data preprocessing com-bined with effective MhAs significantly improve the parameter-extraction accuracy and stability compared with methods without data preprocessing.For instance,under denoised data,the accuracies of the SDM,DDM,and TDM increase by 99.69%,99.70%,and 99.69%,respectively,whereas their accuracy improvements increase by 66.71%,59.65%,and 70.36%,respectively.展开更多
This work proposes an optimization method for gas storage operation parameters under multi-factor coupled constraints to improve the peak-shaving capacity of gas storage reservoirs while ensuring operational safety.Pr...This work proposes an optimization method for gas storage operation parameters under multi-factor coupled constraints to improve the peak-shaving capacity of gas storage reservoirs while ensuring operational safety.Previous research primarily focused on integrating reservoir,wellbore,and surface facility constraints,often resulting in broad constraint ranges and slow model convergence.To solve this problem,the present study introduces additional constraints on maximum withdrawal rates by combining binomial deliverability equations with material balance equations for closed gas reservoirs,while considering extreme peak-shaving demands.This approach effectively narrows the constraint range.Subsequently,a collaborative optimization model with maximum gas production as the objective function is established,and the model employs a joint solution strategy combining genetic algorithms and numerical simulation techniques.Finally,this methodology was applied to optimize operational parameters for Gas Storage T.The results demonstrate:(1)The convergence of the model was achieved after 6 iterations,which significantly improved the convergence speed of the model;(2)The maximum working gas volume reached 11.605×10^(8) m^(3),which increased by 13.78%compared with the traditional optimization method;(3)This method greatly improves the operation safety and the ultimate peak load balancing capability.The research provides important technical support for the intelligent decision of injection and production parameters of gas storage and improving peak load balancing ability.展开更多
To investigate the influence of different longitudinal constraint systems on the longitudinal displacement at the girder ends of a three-tower suspension bridge,this study takes the Cangrong Xunjiang Bridge as an engi...To investigate the influence of different longitudinal constraint systems on the longitudinal displacement at the girder ends of a three-tower suspension bridge,this study takes the Cangrong Xunjiang Bridge as an engineering case for finite element analysis.This bridge employs an unprecedented tower-girder constraintmethod,with all vertical supports placed at the transition piers at both ends.This paper aims to study the characteristics of longitudinal displacement control at the girder ends under this novel structure,relying on finite element(FE)analysis.Initially,based on the Weigh In Motion(WIM)data,a random vehicle load model is generated and applied to the finite elementmodel.Several longitudinal constraint systems are proposed,and their effects on the structural response of the bridge are compared.The most reasonable system,balancing girder-end displacement and transitional pier stress,is selected.Subsequently,the study examines the impact of different viscous damper parameters on key structural response indicators,including cumulative longitudinal displacement at the girder ends,maximum longitudinal displacement at the girder ends,cumulative longitudinal displacement at the pier tops,maximum longitudinal displacement at the pier tops,longitudinal acceleration at the pier tops,and maximum bending moment at the pier bottoms.Finally,the coefficient of variation(CV)-TOPSIS method is used to optimize the viscous damper parameters for multiple objectives.The results show that adding viscous dampers at the side towers,in addition to the existing longitudinal limit bearings at the central tower,can most effectively reduce the response of structural indicators.The changes in these indicators are not entirely consistent with variations in damping coefficient and velocity exponent.The damper parameters significantly influence cumulative longitudinal displacement at the girder ends,cumulative longitudinal displacement at the pier tops,and maximum bending moments at the pier bottoms.The optimal damper parameters are found to be a damping coefficient of 5000 kN/(m/s)0.2 and a velocity exponent of 0.2.展开更多
Silicone material extrusion(MEX)is widely used for processing liquids and pastes.Owing to the uneven linewidth and elastic extrusion deformation caused by material accumulation,products may exhibit geometric errors an...Silicone material extrusion(MEX)is widely used for processing liquids and pastes.Owing to the uneven linewidth and elastic extrusion deformation caused by material accumulation,products may exhibit geometric errors and performance defects,leading to a decline in product quality and affecting its service life.This study proposes a process parameter optimization method that considers the mechanical properties of printed specimens and production costs.To improve the quality of silicone printing samples and reduce production costs,three machine learning models,kernel extreme learning machine(KELM),support vector regression(SVR),and random forest(RF),were developed to predict these three factors.Training data were obtained through a complete factorial experiment.A new dataset is obtained using the Euclidean distance method,which assigns the elimination factor.It is trained with Bayesian optimization algorithms for parameter optimization,the new dataset is input into the improved double Gaussian extreme learning machine,and finally obtains the improved KELM model.The results showed improved prediction accuracy over SVR and RF.Furthermore,a multi-objective optimization framework was proposed by combining genetic algorithm technology with the improved KELM model.The effectiveness and reasonableness of the model algorithm were verified by comparing the optimized results with the experimental results.展开更多
The method for optimizing the hydraulic fracturing parameters of the cube development infill well pad was proposed,aiming at the well pattern characteristic of“multi-layer and multi-period”of the infill wells in Sic...The method for optimizing the hydraulic fracturing parameters of the cube development infill well pad was proposed,aiming at the well pattern characteristic of“multi-layer and multi-period”of the infill wells in Sichuan Basin.The fracture propagation and inter-well interference model were established based on the evolution of 4D in-situ stress,and the evolution characteristics of stress and the mechanism of interference between wells were analyzed.The research shows that the increase in horizontal stress difference and the existence of natural fractures/faults are the main reasons for inter-well interference.Inter-well interference is likely to occur near the fracture zones and between the infill wells and parent wells that have been in production for a long time.When communication channels are formed between the infill wells and parent wells,it can increase the productivity of parent wells in the short term.However,it will have a delayed negative impact on the long-term sustained production of both infill wells and parent wells.The change trend of in-situ stress caused by parent well production is basically consistent with the decline trend of pore pressure.The lateral disturbance range of in-situ stress is initially the same as the fracture length and reaches 1.5 to 1.6 times that length after 2.5 years.The key to avoiding inter-well interference is to optimize the fracturing parameters.By adopting the M-shaped well pattern,the optimal well spacing for the infill wells is 300 m,the cluster spacing is 10 m,and the liquid volume per stage is 1800 m^(3).展开更多
For shale oil reservoirs in the Jimsar Sag of Junggar Basin,the fracturing treatments are challenged by poor prediction accuracy and difficulty in parameter optimization.This paper presents a fracturing parameter inte...For shale oil reservoirs in the Jimsar Sag of Junggar Basin,the fracturing treatments are challenged by poor prediction accuracy and difficulty in parameter optimization.This paper presents a fracturing parameter intelligent optimization technique for shale oil reservoirs and verifies it by field application.A self-governing database capable of automatic capture,storage,calls and analysis is established.With this database,22 geological and engineering variables are selected for correlation analysis.A separated fracturing effect prediction model is proposed,with the fracturing learning curve decomposed into two parts:(1)overall trend,which is predicted by the algorithm combining the convolutional neural network with the characteristics of local connection and parameter sharing and the gated recurrent unit that can solve the gradient disappearance;and(2)local fluctuation,which is predicted by integrating the adaptive boosting algorithm to dynamically adjust the random forest weight.A policy gradient-genetic-particle swarm algorithm is designed,which can adaptively adjust the inertia weights and learning factors in the iterative process,significantly improving the optimization ability of the optimization strategy.The fracturing effect prediction and optimization strategy are combined to realize the intelligent optimization of fracturing parameters.The field application verifies that the proposed technique significantly improves the fracturing effects of oil wells,and it has good practicability.展开更多
Fluxgate current sensors(FGCSs)are increasingly employed in power systems due to their high-precision characteristics,yet their measurement flexibility remains constrained by conventional closed-core designs.To addres...Fluxgate current sensors(FGCSs)are increasingly employed in power systems due to their high-precision characteristics,yet their measurement flexibility remains constrained by conventional closed-core designs.To address this limitation,we proposed a split-core sensor structure comprising four magnetic core strips,which achieved non-intrusive current measurement while maintaining detection accuracy.An analytical model of the induced electromotive force was established based on the probe’s geometric configuration,followed by finite element simulations to optimize key parameters including core radius,core width,excitation coil turns,and sensing coil configuration.A complete prototype integrating the measurement probe,excitation circuit,and signal processing circuitry was developed and experimentally validated.The experimental results show a sensitivity of 0.1099 V/A,a hysteresis error of 0.559%,and a repeatability error of 1.574%over a measurement range of±10 A.After polynomial fitting-based error compensation,the nonlinearity error was reduced to 0.208%,achieving performance comparable to closed-core sensors.This work provided a practical solution for applications demanding both high measurement accuracy and installation flexibility.展开更多
Accurate determination of rock mass parameters is essential for ensuring the accuracy of numericalsimulations. Displacement back-analysis is the most widely used method;however, the reliability of thecurrent approache...Accurate determination of rock mass parameters is essential for ensuring the accuracy of numericalsimulations. Displacement back-analysis is the most widely used method;however, the reliability of thecurrent approaches remains unsatisfactory. Therefore, in this paper, a multistage rock mass parameterback-analysis method, that considers the construction process and displacement losses is proposed andimplemented through the coupling of numerical simulation, auto-machine learning (AutoML), andmulti-objective optimization algorithms (MOOAs). First, a parametric modeling platform for mechanizedtwin tunnels is developed, generating a dataset through extensive numerical simulations. Next, theAutoML method is utilized to establish a surrogate model linking rock parameters and displacements.The tunnel construction process is divided into multiple stages, transforming the rock mass parameterback-analysis into a multi-objective optimization problem, for which multi-objective optimization algorithmsare introduced to obtain the rock mass parameters. The newly proposed rock mass parameterback-analysis method is validated in a mechanized twin tunnel project, and its accuracy and effectivenessare demonstrated. Compared with traditional single-stage back-analysis methods, the proposedmodel decreases the average absolute percentage error from 12.73% to 4.34%, significantly improving theaccuracy of the back-analysis. Moreover, although the accuracy of back analysis significantly increaseswith the number of construction stages considered, the back analysis time is acceptable. This studyprovides a new method for displacement back analysis that is efficient and accurate, thereby paving theway for precise parameter determination in numerical simulations.展开更多
For density inversion of gravity anomaly data, once the inversion method is determined, the main factors affecting the inversion result are the inversion parameters and subdivision scheme. A set of reasonable inversio...For density inversion of gravity anomaly data, once the inversion method is determined, the main factors affecting the inversion result are the inversion parameters and subdivision scheme. A set of reasonable inversion parameters and subdivision scheme can, not only improve the inversion process efficiency, but also ensure inversion result accuracy. The gravity inversion method based on correlation searching and the golden section algorithm is an effective potential field inversion method. It can be used to invert 2D and 3D physical properties with potential data observed on flat or rough surfaces. In this paper, we introduce in detail the density inversion principles based on correlation searching and the golden section algorithm. Considering that the gold section algorithm is not globally optimized. we present a heuristic method to ensure the inversion result is globally optimized. With a series of model tests, we systematically compare and analyze the inversion result efficiency and accuracy with different parameters. Based on the model test results, we conclude the selection principles for each inversion parameter with which the inversion accuracy can be obviously improved.展开更多
Vertical picking method is a predominate method used to harvest cotton crop.However,a vertical picking method may cause spindle bending of the cotton picker if spindles collide with stones on the cotton field.Thus,how...Vertical picking method is a predominate method used to harvest cotton crop.However,a vertical picking method may cause spindle bending of the cotton picker if spindles collide with stones on the cotton field.Thus,how to realize a precise height control of the cotton picker is a crucial issue to be solved.The objective of this study is to design a height control system to avoid the collision.To design it,the mathematical models are established first.Then a multi-objective optimization model represented by structure parameters and control parameters is proposed to take the pressure of chamber without piston,response time and displacement error of the height control system as the opti-mization objectives.An integrated optimization approach that combines optimization via simulation,particle swarm optimization and simulated annealing is proposed to solve the model.Simulation and experimental test results show that the proposed integrated optimization approach can not only reduce the pressure of chamber without piston,but also decrease the response time and displacement error of the height control system.展开更多
The heating technological requirement of the conventional PID control is difficult to guarantee which based on the precise mathematical model,because the heating furnace for heating treatment with the big inertia,the ...The heating technological requirement of the conventional PID control is difficult to guarantee which based on the precise mathematical model,because the heating furnace for heating treatment with the big inertia,the pure time delay and nonlinear time-varying.Proposed one kind optimized variable method of PID controller based on the genetic algorithm with improved BP network that better realized the completely automatic intelligent control of the entire thermal process than the classics critical purporting(Z-N)method.A heating furnace for the object was simulated with MATLAB,simulation results show that the control system has the quicker response characteristic,the better dynamic characteristic and the quite stronger robustness,which has some promotional value for the control of industrial furnace.展开更多
Selecting the optimal machining parameters for impeller surface is a challenging task in the automatic manufacturing industry, due to its free-form surface and deep-crooked flow channel.Existing experimental methods r...Selecting the optimal machining parameters for impeller surface is a challenging task in the automatic manufacturing industry, due to its free-form surface and deep-crooked flow channel.Existing experimental methods require lots of machining experiments and off-line tests, which may lead to high machining cost and low efficiency. This paper proposes a novel method of machining parameters optimization for an impeller based on the on-machine measuring technique. The absolute average error and standard deviation of the measured points are used to define the grey relational grade for reconstructing the objective function, and the complex problem of multi-objective optimization is simplified into a problem of single-objective optimization. Then, by comparing the values of the defined grey relational grade in a designed orthogonal experiment, the optimal combination of the machining parameters is obtained. The experiment-solving process of the objective function corresponds to the minimization of the used errors, which is advantageous to reducing the machining error. The proposed method is efficient and low-cost, since it does not require re-clamping the workpiece for off-line tests. Its effectiveness is verified by an on-machine inspection experiment of the impeller blade.展开更多
基金the financial support provided by the National Key R&D Program of China(Grant No.2023YFC3903900)the Science and Technology Innovation Talent Program of Hubei Province(Grant No.2022EJD002)+1 种基金the Sichuan Science and Technology Program(Grant No.2025ZNSFSC0378)the Key Laboratory of Green Chemistry of Sichuan Institutes of Higher Education(Grant No.LZJ2303).
文摘Specialized vanadium(V)-iron(Fe)-based alloy additives utilized in the production of V-containing steels were investigated.Vanadium slag from the Panzhihua region of China was utilized as a raw material to optimize process parameters for the preparation of V-Fe-based alloy via silicon thermal reduction.Experiments were conducted to investigate the effects of reduction temperature,holding time,and slag composition on alloy-slag separation,alloy microstructure,and the oxide content of residual slag,with an emphasis on the recovery of valuable metal elements.The results indicated that the optimal process conditions for silicon thermal reduction were achieved at reduction temperature of 1823 K,holding time of 240 min,and slag composition of 45 wt.%SiO_(2),40 wt.%CaO,and 15 wt.%Al_(2)O_(3).The resulting V-Fe-based alloy predominantly consisted of Fe-based phases such as Fe,titanium(Ti),silicon(Si)and manganese(Mn),with Si,V,as well as chromium(Cr)concentrated in the intercrystalline phase of the Fe-based alloy.The recoveries of Fe,Mn,Cr,V,and Ti under the optimal conditions were 96.30%,91.96%,86.53%,80.29%,and 74.82%,respectively.The key components of the V-Fe-based alloy obtained were 41.96 wt.%Si,27.55 wt.%Fe,12.13 wt.%Mn,5.53 wt.%V,4.86 wt.%Cr,and 3.74 wt.%Ti,thereby enabling the comprehensive recovery of the valuable metal from vanadium slag.
基金supported by the Science and Technology Research Project of Henan Province(242102241055)the Industry-University-Research Collaborative Innovation Base on Automobile Lightweight of“Science and Technology Innovation in Central Plains”(2024KCZY315)the Opening Fund of State Key Laboratory of Structural Analysis,Optimization and CAE Software for Industrial Equipment(GZ2024A03-ZZU).
文摘The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is mainly used for finite element analysis at present,and the effectiveness of the surrogate material model has been fully confirmed.However,there are some accuracy problems when dealing with boundary elements using the surrogate material model,which will affect the topology optimization results.In this study,a boundary element reconstruction(BER)model is proposed based on the surrogate material model under the MMC topology optimization framework to improve the accuracy of topology optimization.The proposed BER model can reconstruct the boundary elements by refining the local meshes and obtaining new nodes in boundary elements.Then the density of boundary elements is recalculated using the new node information,which is more accurate than the original model.Based on the new density of boundary elements,the material properties and volume information of the boundary elements are updated.Compared with other finite element analysis methods,the BER model is simple and feasible and can improve computational accuracy.Finally,the effectiveness and superiority of the proposed method are verified by comparing it with the optimization results of the original surrogate material model through several numerical examples.
基金financially supported by the National Key Research and Development Program of China(Grant No.2022YFC2806100)the National Natural Science Foundation of China(Grant Nos.U22B20126 and 52374020)+1 种基金Science Foundation of China University of Petroleum,Beijing(Grant No.2462025QNXZ009)Beijing Nova Program(Grant No.20250484913).
文摘Steel catenary riser represents the pioneering riser technology implemented in China’s deep-sea oil and gas opera-tions.Given the complex mechanical conditions of the riser,extensive research has been conducted on its dynamic analysis and structural design.This study investigates a deep-sea oil and gas field by developing a coupled model of a semi-submersible platform and steel catenary riser to analyze it mechanical behavior under extreme marine condi-tions.Through multi-objective optimization methodology,the study compares and analyzes suspension point tension and touchdown point stress under various conditions by modifying the suspension position,suspension angle,and catenary length.The optimal configuration parameters were determined:a suspension angle of 12°,suspension position in the southwest direction of the column,and a catenary length of approximately 2000 m.These findings elucidate the impact of configuration parameters on riser dynamic response and establish reasonable parameter layout ranges for adverse sea conditions,offering valuable optimization strategies for steel catenary riser deployment in domestic deep-sea oil and gas fields.
基金Projects(U22B2084,52275483,52075142)supported by the National Natural Science Foundation of ChinaProject(2023ZY01050)supported by the Ministry of Industry and Information Technology High Quality Development,China。
文摘The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods.
基金supported by the Changsha Major Science and Technology Plan Project,China(No.kq2207002)the Natural Science Foundation of Hunan Province(No.2023JJ40720)the Postgraduate Innovative Project of Central South University,China(No.2022XQLH058)。
文摘In this study,a novel synergistic swing energy-regenerative hybrid system(SSEHS)for excavators with a large inertia slewing platform is constructed.With the SSEHS,the pressure boosting and output energy synergy of multiple energy sources can be realized,while the swing braking energy can be recovered and used by means of hydraulic energy.Additionally,considering the system constraints and comprehensive optimization conditions of energy efficiency and dynamic characteristics,an improved multi-objective particle swarm optimization(IMOPSO)combined with an adaptive grid is proposed for parameter optimization of the SSEHS.Meanwhile,a parameter rule-based control strategy is designed,which can switch to a reasonable working mode according to the real-time state.Finally,a physical prototype of a 50-t excavator and its AMESim model is established.The semi-simulation and semi-experiment results demonstrate that compared with a conventional swing system,energy consumption under the 90°rotation condition could be reduced by about 51.4%in the SSEHS before parameter optimization,while the energy-saving efficiency is improved by another 13.2%after parameter optimization.This confirms the effectiveness of the SSEHS and the IMOPSO parameter optimization method proposed in this paper.The IMOPSO algorithm is universal and can be used for parameter matching and optimization of hybrid power systems.
基金financially supported by Sichuan Science and Technology Program(No.2025ZNSFSC0373)National Natural Science foundation of China(Grant No.52104006)Science and Technology Cooperation Project of the CNPC-SWPU Innovation Alliance(Grant No.2020CX040202)。
文摘Rate of penetration(ROP)is the key factor affecting the drilling cycle and cost,and it directly reflects the drilling efficiency.With the increasingly complex field data,the original drilling parameter optimization method can't meet the needs of drilling parameter optimization in the era of big data and artificial intelligence.This paper presents a drilling parameter optimization method based on big data of drilling,which takes machine learning algorithms as a tool.First,field data is pre-processed according to the characteristics of big data of drilling.Then a formation clustering model based on unsupervised learning is established,which takes sonic logging,gamma logging,and density logging data as input.Formation clusters with similar stratum characteristics are decided.Aiming at improving ROP,the formation clusters are input into the ROP model,and the mechanical parameters(weight on bit,revolution per minute)and hydraulic parameters(standpipe pressure,flow rate)are optimized.Taking the Southern Margin block of Xinjiang as an example,the MAPE of prediction of ROP after clustering is decreased from 18.72%to 10.56%.The results of this paper provide a new method to improve drilling efficiency based on big data of drilling.
基金Project supported by the National Natural Science Foundation of China (Grant No. U22B2095)the Civil Aerospace Technology Research Project (Grant No. D010103)。
文摘The Rydberg atom-based receiver, as a novel type of antenna, demonstrates broad application prospects in the field of microwave communications. However, since Rydberg atomic receivers are nonlinear systems, mismatches between the parameters of the received amplitude modulation(AM) signals and the system's linear workspace and demodulation operating points can cause severe distortion in the demodulated signals. To address this, the article proposes a method for determining the operational parameters based on the mean square error(MSE) and total harmonic distortion(THD) assessments and presents strategies for optimizing the system's operational parameters focusing on linear response characteristics(LRC) and linear dynamic range(LDR). Specifically, we employ a method that minimizes the MSE to define the system's linear workspace, thereby ensuring the system has a good LRC while maximizing the LDR. To ensure that the signal always operates within the linear workspace, an appropriate carrier amplitude is set as the demodulation operating point. By calculating the THD at different operating points, the LRC performance within different regions of the linear workspace is evaluated, and corresponding optimization strategies based on the range of signal strengths are proposed. Moreover, to more accurately restore the baseband signal, we establish a mapping relationship between the carrier Rabi frequency and the transmitted power of the probe light, and optimize the slope of the linear demodulation function to reduce the MSE to less than 0.8×10^(-4). Finally, based on these methods for determining the operational parameters, we explore the effects of different laser Rabi frequencies on the system performance, and provide optimization recommendations. This research provides robust support for the design of high-performance Rydberg atom-based AM receivers.
基金sponsored by the National Natural Science Foundation of China(Grant No.52405443)the Technology Research and Development Plan of China Railway(Grant No.N2023G063)the Fund of China Academy of Railway Sciences Corporation Limited(Grant No.2023YJ054).
文摘Purpose–To investigate the influence of vehicle operation speed,curve geometry parameters and rail profile parameters on wheel–rail creepage in high-speed railway curves and propose a multi-parameter coordinated optimization strategy to reduce wheel–rail contact fatigue damage.Design/methodology/approach–Taking a small-radius curve of a high-speed railway as the research object,field measurements were conducted to obtain track parameters and wheel–rail profiles.A coupled vehicle-track dynamics model was established.Multiple numerical experiments were designed using the Latin Hypercube Sampling method to extract wheel-rail creepage indicators and construct a parameter-creepage response surface model.Findings–Key service parameters affecting wheel–rail creepage were identified,including the matching relationship between curve geometry and vehicle speed and rail profile parameters.The influence patterns of various parameters on wheel–rail creepage were revealed through response surface analysis,leading to the establishment of parameter optimization criteria.Originality/value–This study presents the systematic investigation of wheel–rail creepage characteristics under multi-parameter coupling in high-speed railway curves.A response surface-based parameter-creepage relationship model was established,and a multi-parameter coordinated optimization strategy was proposed.The research findings provide theoretical guidance for controlling wheel–rail contact fatigue damage and optimizing wheel–rail profiles in high-speed railway curves.
基金supported by the National Natural Science Foundation of China(62263014)the Yunnan Provincial Basic Research Project(202301AT070443,202401AT070344).
文摘Accurate identification of unknown internal parameters in photovoltaic(PV)cells is crucial and significantly affects the subsequent system-performance analysis and control.However,noise,insufficient data acquisition,and loss of recorded data can deteriorate the extraction accuracy of unknown parameters.Hence,this study proposes an intelligent parameter-identification strategy that integrates artificial ecosystem optimization(AEO)and a Bayesian neural network(BNN)for PV cell parameter extraction.A BNN is used for data preprocessing,including data denoising and prediction.Furthermore,the AEO algorithm is utilized to identify unknown parameters in the single-diode model(SDM),double-diode model(DDM),and three-diode model(TDM).Nine other metaheuristic algorithms(MhAs)are adopted for an unbiased and comprehensive validation.Simulation results show that BNN-based data preprocessing com-bined with effective MhAs significantly improve the parameter-extraction accuracy and stability compared with methods without data preprocessing.For instance,under denoised data,the accuracies of the SDM,DDM,and TDM increase by 99.69%,99.70%,and 99.69%,respectively,whereas their accuracy improvements increase by 66.71%,59.65%,and 70.36%,respectively.
基金supported by the Science and Technology Research Program of Chongqing Municipal Education Commission(KJQN202401501,KJZD-M202401501).
文摘This work proposes an optimization method for gas storage operation parameters under multi-factor coupled constraints to improve the peak-shaving capacity of gas storage reservoirs while ensuring operational safety.Previous research primarily focused on integrating reservoir,wellbore,and surface facility constraints,often resulting in broad constraint ranges and slow model convergence.To solve this problem,the present study introduces additional constraints on maximum withdrawal rates by combining binomial deliverability equations with material balance equations for closed gas reservoirs,while considering extreme peak-shaving demands.This approach effectively narrows the constraint range.Subsequently,a collaborative optimization model with maximum gas production as the objective function is established,and the model employs a joint solution strategy combining genetic algorithms and numerical simulation techniques.Finally,this methodology was applied to optimize operational parameters for Gas Storage T.The results demonstrate:(1)The convergence of the model was achieved after 6 iterations,which significantly improved the convergence speed of the model;(2)The maximum working gas volume reached 11.605×10^(8) m^(3),which increased by 13.78%compared with the traditional optimization method;(3)This method greatly improves the operation safety and the ultimate peak load balancing capability.The research provides important technical support for the intelligent decision of injection and production parameters of gas storage and improving peak load balancing ability.
基金supported by the National Key Research and Development Program of China(No.2022YFB3706704)the Academician Special Science Research Project of CCCC(No.YSZX-03-2022-01-B).
文摘To investigate the influence of different longitudinal constraint systems on the longitudinal displacement at the girder ends of a three-tower suspension bridge,this study takes the Cangrong Xunjiang Bridge as an engineering case for finite element analysis.This bridge employs an unprecedented tower-girder constraintmethod,with all vertical supports placed at the transition piers at both ends.This paper aims to study the characteristics of longitudinal displacement control at the girder ends under this novel structure,relying on finite element(FE)analysis.Initially,based on the Weigh In Motion(WIM)data,a random vehicle load model is generated and applied to the finite elementmodel.Several longitudinal constraint systems are proposed,and their effects on the structural response of the bridge are compared.The most reasonable system,balancing girder-end displacement and transitional pier stress,is selected.Subsequently,the study examines the impact of different viscous damper parameters on key structural response indicators,including cumulative longitudinal displacement at the girder ends,maximum longitudinal displacement at the girder ends,cumulative longitudinal displacement at the pier tops,maximum longitudinal displacement at the pier tops,longitudinal acceleration at the pier tops,and maximum bending moment at the pier bottoms.Finally,the coefficient of variation(CV)-TOPSIS method is used to optimize the viscous damper parameters for multiple objectives.The results show that adding viscous dampers at the side towers,in addition to the existing longitudinal limit bearings at the central tower,can most effectively reduce the response of structural indicators.The changes in these indicators are not entirely consistent with variations in damping coefficient and velocity exponent.The damper parameters significantly influence cumulative longitudinal displacement at the girder ends,cumulative longitudinal displacement at the pier tops,and maximum bending moments at the pier bottoms.The optimal damper parameters are found to be a damping coefficient of 5000 kN/(m/s)0.2 and a velocity exponent of 0.2.
基金supported by the National Key R&D Program of China(No.2022YFA1005204l)。
文摘Silicone material extrusion(MEX)is widely used for processing liquids and pastes.Owing to the uneven linewidth and elastic extrusion deformation caused by material accumulation,products may exhibit geometric errors and performance defects,leading to a decline in product quality and affecting its service life.This study proposes a process parameter optimization method that considers the mechanical properties of printed specimens and production costs.To improve the quality of silicone printing samples and reduce production costs,three machine learning models,kernel extreme learning machine(KELM),support vector regression(SVR),and random forest(RF),were developed to predict these three factors.Training data were obtained through a complete factorial experiment.A new dataset is obtained using the Euclidean distance method,which assigns the elimination factor.It is trained with Bayesian optimization algorithms for parameter optimization,the new dataset is input into the improved double Gaussian extreme learning machine,and finally obtains the improved KELM model.The results showed improved prediction accuracy over SVR and RF.Furthermore,a multi-objective optimization framework was proposed by combining genetic algorithm technology with the improved KELM model.The effectiveness and reasonableness of the model algorithm were verified by comparing the optimized results with the experimental results.
基金Supported by the General Program of the NATIONAL NATURAL SCIENCE FOUNDATION OF CHINA(52374004)National Key Research and Development Program(2023YFF06141022023YFE0110900)。
文摘The method for optimizing the hydraulic fracturing parameters of the cube development infill well pad was proposed,aiming at the well pattern characteristic of“multi-layer and multi-period”of the infill wells in Sichuan Basin.The fracture propagation and inter-well interference model were established based on the evolution of 4D in-situ stress,and the evolution characteristics of stress and the mechanism of interference between wells were analyzed.The research shows that the increase in horizontal stress difference and the existence of natural fractures/faults are the main reasons for inter-well interference.Inter-well interference is likely to occur near the fracture zones and between the infill wells and parent wells that have been in production for a long time.When communication channels are formed between the infill wells and parent wells,it can increase the productivity of parent wells in the short term.However,it will have a delayed negative impact on the long-term sustained production of both infill wells and parent wells.The change trend of in-situ stress caused by parent well production is basically consistent with the decline trend of pore pressure.The lateral disturbance range of in-situ stress is initially the same as the fracture length and reaches 1.5 to 1.6 times that length after 2.5 years.The key to avoiding inter-well interference is to optimize the fracturing parameters.By adopting the M-shaped well pattern,the optimal well spacing for the infill wells is 300 m,the cluster spacing is 10 m,and the liquid volume per stage is 1800 m^(3).
基金Supported by the National Science and Technology Major Project(2017ZX05009-005-003)National Natural Science Grant Fund for Surface Project(52174045)+1 种基金Chinese Academy of Engineering Strategic Consulting Project(2018-XZ-09)China National Petroleum Corporation(CNPC)-China University of Petroleum(Beijing)Special Project for Strategic Cooperation in Science and Technology(ZLZX2020-01)。
文摘For shale oil reservoirs in the Jimsar Sag of Junggar Basin,the fracturing treatments are challenged by poor prediction accuracy and difficulty in parameter optimization.This paper presents a fracturing parameter intelligent optimization technique for shale oil reservoirs and verifies it by field application.A self-governing database capable of automatic capture,storage,calls and analysis is established.With this database,22 geological and engineering variables are selected for correlation analysis.A separated fracturing effect prediction model is proposed,with the fracturing learning curve decomposed into two parts:(1)overall trend,which is predicted by the algorithm combining the convolutional neural network with the characteristics of local connection and parameter sharing and the gated recurrent unit that can solve the gradient disappearance;and(2)local fluctuation,which is predicted by integrating the adaptive boosting algorithm to dynamically adjust the random forest weight.A policy gradient-genetic-particle swarm algorithm is designed,which can adaptively adjust the inertia weights and learning factors in the iterative process,significantly improving the optimization ability of the optimization strategy.The fracturing effect prediction and optimization strategy are combined to realize the intelligent optimization of fracturing parameters.The field application verifies that the proposed technique significantly improves the fracturing effects of oil wells,and it has good practicability.
基金supported by Yunnan Fundamental Research Projects(No.202301AT070181)Yunnan Fundamental Research Projects(No.202401CF070126)+1 种基金Xingdian Talent Support Program of Yunnan Province(No.KKRD202203070)Yunnan High level Science and Technology Talents and Innovation Team Selection Special Project(No.202405AS350001).
文摘Fluxgate current sensors(FGCSs)are increasingly employed in power systems due to their high-precision characteristics,yet their measurement flexibility remains constrained by conventional closed-core designs.To address this limitation,we proposed a split-core sensor structure comprising four magnetic core strips,which achieved non-intrusive current measurement while maintaining detection accuracy.An analytical model of the induced electromotive force was established based on the probe’s geometric configuration,followed by finite element simulations to optimize key parameters including core radius,core width,excitation coil turns,and sensing coil configuration.A complete prototype integrating the measurement probe,excitation circuit,and signal processing circuitry was developed and experimentally validated.The experimental results show a sensitivity of 0.1099 V/A,a hysteresis error of 0.559%,and a repeatability error of 1.574%over a measurement range of±10 A.After polynomial fitting-based error compensation,the nonlinearity error was reduced to 0.208%,achieving performance comparable to closed-core sensors.This work provided a practical solution for applications demanding both high measurement accuracy and installation flexibility.
基金supported by the National Natural Science Foundation of China(Grant Nos.52090081,52079068)the State Key Laboratory of Hydroscience and Hydraulic Engineering(Grant No.2021-KY-04).
文摘Accurate determination of rock mass parameters is essential for ensuring the accuracy of numericalsimulations. Displacement back-analysis is the most widely used method;however, the reliability of thecurrent approaches remains unsatisfactory. Therefore, in this paper, a multistage rock mass parameterback-analysis method, that considers the construction process and displacement losses is proposed andimplemented through the coupling of numerical simulation, auto-machine learning (AutoML), andmulti-objective optimization algorithms (MOOAs). First, a parametric modeling platform for mechanizedtwin tunnels is developed, generating a dataset through extensive numerical simulations. Next, theAutoML method is utilized to establish a surrogate model linking rock parameters and displacements.The tunnel construction process is divided into multiple stages, transforming the rock mass parameterback-analysis into a multi-objective optimization problem, for which multi-objective optimization algorithmsare introduced to obtain the rock mass parameters. The newly proposed rock mass parameterback-analysis method is validated in a mechanized twin tunnel project, and its accuracy and effectivenessare demonstrated. Compared with traditional single-stage back-analysis methods, the proposedmodel decreases the average absolute percentage error from 12.73% to 4.34%, significantly improving theaccuracy of the back-analysis. Moreover, although the accuracy of back analysis significantly increaseswith the number of construction stages considered, the back analysis time is acceptable. This studyprovides a new method for displacement back analysis that is efficient and accurate, thereby paving theway for precise parameter determination in numerical simulations.
基金supported by Specialized Research Fund for the Doctoral Program of Higher Education of China(20110022120004)the Fundamental Research Funds for the Central Universities
文摘For density inversion of gravity anomaly data, once the inversion method is determined, the main factors affecting the inversion result are the inversion parameters and subdivision scheme. A set of reasonable inversion parameters and subdivision scheme can, not only improve the inversion process efficiency, but also ensure inversion result accuracy. The gravity inversion method based on correlation searching and the golden section algorithm is an effective potential field inversion method. It can be used to invert 2D and 3D physical properties with potential data observed on flat or rough surfaces. In this paper, we introduce in detail the density inversion principles based on correlation searching and the golden section algorithm. Considering that the gold section algorithm is not globally optimized. we present a heuristic method to ensure the inversion result is globally optimized. With a series of model tests, we systematically compare and analyze the inversion result efficiency and accuracy with different parameters. Based on the model test results, we conclude the selection principles for each inversion parameter with which the inversion accuracy can be obviously improved.
基金Supported by National Natural Science Foundation of China(Grant No.51905448)Chongqing Technology Innovation and Application Program of China(Grant No.cstc2018jszx-cyzdX0183)Fundamental Research Funds for the Central Universities of China(Grant No.SWU119060).
文摘Vertical picking method is a predominate method used to harvest cotton crop.However,a vertical picking method may cause spindle bending of the cotton picker if spindles collide with stones on the cotton field.Thus,how to realize a precise height control of the cotton picker is a crucial issue to be solved.The objective of this study is to design a height control system to avoid the collision.To design it,the mathematical models are established first.Then a multi-objective optimization model represented by structure parameters and control parameters is proposed to take the pressure of chamber without piston,response time and displacement error of the height control system as the opti-mization objectives.An integrated optimization approach that combines optimization via simulation,particle swarm optimization and simulated annealing is proposed to solve the model.Simulation and experimental test results show that the proposed integrated optimization approach can not only reduce the pressure of chamber without piston,but also decrease the response time and displacement error of the height control system.
基金This work was supported by the youth backbone teachers training program of Henan colleges and universities under Grant No.2016ggjs-287the project of science and technology of Henan province under Grant No.172102210124the Key Scientific Research projects in Colleges and Universities in Henan(Grant No.18B460003).
文摘The heating technological requirement of the conventional PID control is difficult to guarantee which based on the precise mathematical model,because the heating furnace for heating treatment with the big inertia,the pure time delay and nonlinear time-varying.Proposed one kind optimized variable method of PID controller based on the genetic algorithm with improved BP network that better realized the completely automatic intelligent control of the entire thermal process than the classics critical purporting(Z-N)method.A heating furnace for the object was simulated with MATLAB,simulation results show that the control system has the quicker response characteristic,the better dynamic characteristic and the quite stronger robustness,which has some promotional value for the control of industrial furnace.
基金co-supported by the National Basic Research Program of China(No.2015CB057304)the National Natural Science Foundation of China(Nos.51535004 and91648111)
文摘Selecting the optimal machining parameters for impeller surface is a challenging task in the automatic manufacturing industry, due to its free-form surface and deep-crooked flow channel.Existing experimental methods require lots of machining experiments and off-line tests, which may lead to high machining cost and low efficiency. This paper proposes a novel method of machining parameters optimization for an impeller based on the on-machine measuring technique. The absolute average error and standard deviation of the measured points are used to define the grey relational grade for reconstructing the objective function, and the complex problem of multi-objective optimization is simplified into a problem of single-objective optimization. Then, by comparing the values of the defined grey relational grade in a designed orthogonal experiment, the optimal combination of the machining parameters is obtained. The experiment-solving process of the objective function corresponds to the minimization of the used errors, which is advantageous to reducing the machining error. The proposed method is efficient and low-cost, since it does not require re-clamping the workpiece for off-line tests. Its effectiveness is verified by an on-machine inspection experiment of the impeller blade.
